mirror of
https://github.com/vladmandic/sdnext.git
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393 lines
18 KiB
Python
393 lines
18 KiB
Python
import sys
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import copy
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import time
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import diffusers
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from installer import install, log, setup_logging
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ao = None
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bnb = None
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intel_nncf = None
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optimum_quanto = None
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quant_last_model_name = None
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quant_last_model_device = None
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def get_quant(name):
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if "qint8" in name.lower():
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return 'qint8'
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if "qint4" in name.lower():
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return 'qint4'
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if "fp8" in name.lower():
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return 'fp8'
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if "fp4" in name.lower():
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return 'fp4'
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if "nf4" in name.lower():
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return 'nf4'
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if name.endswith('.gguf'):
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return 'gguf'
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return 'none'
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def create_bnb_config(kwargs = None, allow_bnb: bool = True):
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from modules import shared, devices
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if len(shared.opts.bnb_quantization) > 0 and allow_bnb:
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if 'Model' in shared.opts.bnb_quantization:
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load_bnb()
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if bnb is None:
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return kwargs
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bnb_config = diffusers.BitsAndBytesConfig(
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load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'],
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load_in_4bit=shared.opts.bnb_quantization_type in ['nf4', 'fp4'],
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bnb_4bit_quant_storage=shared.opts.bnb_quantization_storage,
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bnb_4bit_quant_type=shared.opts.bnb_quantization_type,
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bnb_4bit_compute_dtype=devices.dtype
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)
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shared.log.debug(f'Quantization: module=all type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
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if kwargs is None:
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return bnb_config
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else:
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kwargs['quantization_config'] = bnb_config
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return kwargs
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return kwargs
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def create_ao_config(kwargs = None, allow_ao: bool = True):
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from modules import shared
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if len(shared.opts.torchao_quantization) > 0 and shared.opts.torchao_quantization_mode == 'pre' and allow_ao:
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if 'Model' in shared.opts.torchao_quantization:
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load_torchao()
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if ao is None:
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return kwargs
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diffusers.utils.import_utils.is_torchao_available = lambda: True
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ao_config = diffusers.TorchAoConfig(shared.opts.torchao_quantization_type)
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shared.log.debug(f'Quantization: module=all type=torchao dtype={shared.opts.torchao_quantization_type}')
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if kwargs is None:
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return ao_config
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else:
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kwargs['quantization_config'] = ao_config
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return kwargs
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return kwargs
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def load_torchao(msg='', silent=False):
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global ao # pylint: disable=global-statement
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if ao is not None:
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return ao
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install('torchao==0.7.0', quiet=True)
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try:
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import torchao
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ao = torchao
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fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
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log.debug(f'Quantization: type=torchao version={ao.__version__} fn={fn}') # pylint: disable=protected-access
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return ao
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except Exception as e:
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if len(msg) > 0:
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log.error(f"{msg} failed to import torchao: {e}")
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ao = None
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if not silent:
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raise
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return None
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def load_bnb(msg='', silent=False):
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from modules import devices
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global bnb # pylint: disable=global-statement
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if bnb is not None:
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return bnb
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if devices.backend == 'cuda':
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# forcing a version will uninstall the multi-backend-refactor branch of bnb
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install('bitsandbytes==0.45.1', quiet=True)
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try:
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import bitsandbytes
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bnb = bitsandbytes
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diffusers.utils.import_utils._bitsandbytes_available = True # pylint: disable=protected-access
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diffusers.utils.import_utils._bitsandbytes_version = '0.43.3' # pylint: disable=protected-access
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fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
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log.debug(f'Quantization: type=bitsandbytes version={bnb.__version__} fn={fn}') # pylint: disable=protected-access
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return bnb
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except Exception as e:
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if len(msg) > 0:
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log.error(f"{msg} failed to import bitsandbytes: {e}")
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bnb = None
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if not silent:
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raise
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return None
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def load_quanto(msg='', silent=False):
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from modules import shared
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global optimum_quanto # pylint: disable=global-statement
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if optimum_quanto is not None:
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return optimum_quanto
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install('optimum-quanto==0.2.6', quiet=True)
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try:
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from optimum import quanto # pylint: disable=no-name-in-module
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optimum_quanto = quanto
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fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
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log.debug(f'Quantization: type=quanto version={quanto.__version__} fn={fn}') # pylint: disable=protected-access
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if shared.opts.diffusers_offload_mode in {'balanced', 'sequential'}:
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shared.log.error(f'Quantization: type=quanto offload={shared.opts.diffusers_offload_mode} not supported')
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return optimum_quanto
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except Exception as e:
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if len(msg) > 0:
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log.error(f"{msg} failed to import optimum.quanto: {e}")
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optimum_quanto = None
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if not silent:
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raise
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return None
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def load_nncf(msg='', silent=False):
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global intel_nncf # pylint: disable=global-statement
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if intel_nncf is not None:
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return intel_nncf
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install('nncf==2.7.0', quiet=True)
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try:
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import nncf
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intel_nncf = nncf
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fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
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log.debug(f'Quantization: type=nncf version={nncf.__version__} fn={fn}') # pylint: disable=protected-access
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return intel_nncf
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except Exception as e:
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if len(msg) > 0:
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log.error(f"{msg} failed to import nncf: {e}")
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intel_nncf = None
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if not silent:
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raise
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return None
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def apply_layerwise(sd_model, quiet:bool=False):
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import torch
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from diffusers.quantizers import quantization_config
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from modules import shared, devices, sd_models
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if shared.opts.layerwise_quantization_storage == 'float8_e4m3fn' and hasattr(torch, 'float8_e4m3fn'):
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storage_dtype = torch.float8_e4m3fn
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elif shared.opts.layerwise_quantization_storage == 'float8_e5m2' and hasattr(torch, 'float8_e5m2'):
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storage_dtype = torch.float8_e5m2
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else:
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storage_dtype = None
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shared.log.warning(f'Quantization: type=layerwise storage={shared.opts.layerwise_quantization_storage} not supported')
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return
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non_blocking = False
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if not hasattr(quantization_config.QuantizationMethod, 'LAYERWISE'):
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setattr(quantization_config.QuantizationMethod, 'LAYERWISE', 'layerwise') # noqa: B010
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for module in sd_models.get_signature(sd_model).keys():
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if not hasattr(sd_model, module):
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continue
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try:
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cls = getattr(sd_model, module).__class__.__name__
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if module.startswith('unet') and ('Model' in shared.opts.layerwise_quantization):
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m = getattr(sd_model, module)
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if hasattr(m, 'enable_layerwise_casting'):
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m.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=non_blocking)
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m.quantization_method = 'LayerWise'
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log.quiet(quiet, f'Quantization: type=layerwise module={module} cls={cls} storage={storage_dtype} compute={devices.dtype} blocking={not non_blocking}')
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if module.startswith('transformer') and ('Model' in shared.opts.layerwise_quantization or 'Transformer' in shared.opts.layerwise_quantization):
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m = getattr(sd_model, module)
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if hasattr(m, 'enable_layerwise_casting'):
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m.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=non_blocking)
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m.quantization_method = 'LayerWise'
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log.quiet(quiet, f'Quantization: type=layerwise module={module} cls={cls} storage={storage_dtype} compute={devices.dtype} blocking={not non_blocking}')
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if module.startswith('text_encoder') and ('Model' in shared.opts.layerwise_quantization or 'Text Encoder' in shared.opts.layerwise_quantization) and ('clip' not in cls.lower()):
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m = getattr(sd_model, module)
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if hasattr(m, 'enable_layerwise_casting'):
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m.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=non_blocking)
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m.quantization_method = quantization_config.QuantizationMethod.LAYERWISE # pylint: disable=no-member
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log.quiet(quiet, f'Quantization: type=layerwise module={module} cls={cls} storage={storage_dtype} compute={devices.dtype} blocking={not non_blocking}')
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except Exception as e:
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shared.log.error(f'Quantization: type=layerwise {e}')
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def nncf_send_to_device(model, device):
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for child in model.children():
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if child.__class__.__name__ == "WeightsDecompressor":
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child.scale = child.scale.to(device)
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child.zero_point = child.zero_point.to(device)
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nncf_send_to_device(child, device)
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def nncf_compress_model(model, op=None, sd_model=None):
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from modules import devices, shared
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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nncf = load_nncf('Quantize model: type=NNCF')
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model.eval()
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backup_embeddings = None
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if hasattr(model, "get_input_embeddings"):
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backup_embeddings = copy.deepcopy(model.get_input_embeddings())
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model = nncf.compress_weights(model)
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nncf_send_to_device(model, devices.device)
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if hasattr(model, "set_input_embeddings") and backup_embeddings is not None:
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model.set_input_embeddings(backup_embeddings)
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if op is not None and shared.opts.nncf_quantize_shuffle_weights:
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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if shared.cmd_opts.medvram or shared.cmd_opts.lowvram or shared.opts.diffusers_offload_mode != "none":
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quant_last_model_name = op
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quant_last_model_device = model.device
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else:
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quant_last_model_name = None
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quant_last_model_device = None
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model.to(devices.device)
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devices.torch_gc(force=True)
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return model
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def nncf_compress_weights(sd_model):
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try:
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t0 = time.time()
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from modules import shared, devices, sd_models
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shared.log.info(f"Quantization: type=NNCF modules={shared.opts.nncf_compress_weights}")
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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sd_model = sd_models.apply_function_to_model(sd_model, nncf_compress_model, shared.opts.nncf_compress_weights, op="nncf")
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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quant_last_model_name = None
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quant_last_model_device = None
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t1 = time.time()
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shared.log.info(f"Quantization: type=NNCF time={t1-t0:.2f}")
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except Exception as e:
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shared.log.warning(f"Quantization: type=NNCF {e}")
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return sd_model
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def optimum_quanto_model(model, op=None, sd_model=None, weights=None, activations=None):
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from modules import devices, shared
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quanto = load_quanto('Quantize model: type=Optimum Quanto')
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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if sd_model is not None and "Flux" in sd_model.__class__.__name__: # LayerNorm is not supported
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exclude_list = ["transformer_blocks.*.norm1.norm", "transformer_blocks.*.norm2", "transformer_blocks.*.norm1_context.norm", "transformer_blocks.*.norm2_context", "single_transformer_blocks.*.norm.norm", "norm_out.norm"]
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else:
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exclude_list = None
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weights = getattr(quanto, weights) if weights is not None else getattr(quanto, shared.opts.optimum_quanto_weights_type)
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if activations is not None:
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activations = getattr(quanto, activations) if activations != 'none' else None
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elif shared.opts.optimum_quanto_activations_type != 'none':
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activations = getattr(quanto, shared.opts.optimum_quanto_activations_type)
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else:
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activations = None
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model.eval()
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backup_embeddings = None
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if hasattr(model, "get_input_embeddings"):
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backup_embeddings = copy.deepcopy(model.get_input_embeddings())
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quanto.quantize(model, weights=weights, activations=activations, exclude=exclude_list)
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quanto.freeze(model)
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if hasattr(model, "set_input_embeddings") and backup_embeddings is not None:
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model.set_input_embeddings(backup_embeddings)
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if op is not None and shared.opts.optimum_quanto_shuffle_weights:
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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if shared.cmd_opts.medvram or shared.cmd_opts.lowvram or shared.opts.diffusers_offload_mode != "none":
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quant_last_model_name = op
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quant_last_model_device = model.device
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else:
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quant_last_model_name = None
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quant_last_model_device = None
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model.to(devices.device)
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devices.torch_gc(force=True)
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return model
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def optimum_quanto_weights(sd_model):
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try:
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t0 = time.time()
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from modules import shared, devices, sd_models
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if shared.opts.diffusers_offload_mode in {"balanced", "sequential"}:
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shared.log.warning(f"Quantization: type=Optimum.quanto offload={shared.opts.diffusers_offload_mode} not compatible")
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return sd_model
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shared.log.info(f"Quantization: type=Optimum.quanto: modules={shared.opts.optimum_quanto_weights}")
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global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
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quanto = load_quanto()
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quanto.tensor.qbits.QBitsTensor.create = lambda *args, **kwargs: quanto.tensor.qbits.QBitsTensor(*args, **kwargs)
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sd_model = sd_models.apply_function_to_model(sd_model, optimum_quanto_model, shared.opts.optimum_quanto_weights, op="optimum-quanto")
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if quant_last_model_name is not None:
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if "." in quant_last_model_name:
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last_model_names = quant_last_model_name.split(".")
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getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
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else:
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getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
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devices.torch_gc(force=True)
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quant_last_model_name = None
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quant_last_model_device = None
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if shared.opts.optimum_quanto_activations_type != 'none':
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activations = getattr(quanto, shared.opts.optimum_quanto_activations_type)
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else:
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activations = None
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if activations is not None:
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def optimum_quanto_freeze(model, op=None, sd_model=None): # pylint: disable=unused-argument
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quanto.freeze(model)
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return model
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if shared.opts.diffusers_offload_mode == "model":
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sd_model.enable_model_cpu_offload(device=devices.device)
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if hasattr(sd_model, "encode_prompt"):
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original_encode_prompt = sd_model.encode_prompt
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def encode_prompt(*args, **kwargs):
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embeds = original_encode_prompt(*args, **kwargs)
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sd_model.maybe_free_model_hooks() # Diffusers keeps the TE on VRAM
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return embeds
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sd_model.encode_prompt = encode_prompt
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else:
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sd_models.move_model(sd_model, devices.device)
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with quanto.Calibration(momentum=0.9):
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sd_model(prompt="dummy prompt", num_inference_steps=10)
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sd_model = sd_models.apply_function_to_model(sd_model, optimum_quanto_freeze, shared.opts.optimum_quanto_weights, op="optimum-quanto-freeze")
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if shared.opts.diffusers_offload_mode == "model":
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sd_models.disable_offload(sd_model)
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sd_models.move_model(sd_model, devices.cpu)
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if hasattr(sd_model, "encode_prompt"):
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sd_model.encode_prompt = original_encode_prompt
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devices.torch_gc(force=True)
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t1 = time.time()
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shared.log.info(f"Quantization: type=Optimum.quanto time={t1-t0:.2f}")
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except Exception as e:
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shared.log.warning(f"Quantization: type=Optimum.quanto {e}")
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return sd_model
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def torchao_quantization(sd_model):
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from modules import shared, devices, sd_models
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torchao = load_torchao()
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q = torchao.quantization
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fn = getattr(q, shared.opts.torchao_quantization_type, None)
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if fn is None:
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shared.log.error(f"Quantization: type=TorchAO type={shared.opts.torchao_quantization_type} not supported")
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return sd_model
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def torchao_model(model, op=None, sd_model=None): # pylint: disable=unused-argument
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q.quantize_(model, fn(), device=devices.device)
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return model
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shared.log.info(f"Quantization: type=TorchAO pipe={sd_model.__class__.__name__} quant={shared.opts.torchao_quantization_type} fn={fn} targets={shared.opts.torchao_quantization}")
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try:
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t0 = time.time()
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sd_models.apply_function_to_model(sd_model, torchao_model, shared.opts.torchao_quantization, op="torchao")
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t1 = time.time()
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shared.log.info(f"Quantization: type=TorchAO time={t1-t0:.2f}")
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except Exception as e:
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shared.log.error(f"Quantization: type=TorchAO {e}")
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setup_logging() # torchao uses dynamo which messes with logging so reset is needed
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return sd_model
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