mirror of https://github.com/vladmandic/automatic
298 lines
17 KiB
Python
298 lines
17 KiB
Python
import os
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import sys
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import time
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import inspect
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import torch
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import diffusers
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import accelerate.hooks
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from modules import shared, devices, errors, model_quant
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from modules.timer import process as process_timer
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debug_move = shared.log.trace if os.environ.get('SD_MOVE_DEBUG', None) is not None else lambda *args, **kwargs: None
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should_offload = ['sc', 'sd3', 'f1', 'hunyuandit', 'auraflow', 'omnigen', 'hunyuanvideo', 'cogvideox', 'mochi']
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offload_hook_instance = None
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def get_signature(cls):
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signature = inspect.signature(cls.__init__, follow_wrapped=True)
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return signature.parameters
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def disable_offload(sd_model):
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if not getattr(sd_model, 'has_accelerate', False):
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return
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if hasattr(sd_model, "_internal_dict"):
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keys = sd_model._internal_dict.keys() # pylint: disable=protected-access
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else:
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keys = get_signature(sd_model).keys()
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for module_name in keys: # pylint: disable=protected-access
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module = getattr(sd_model, module_name, None)
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if isinstance(module, torch.nn.Module):
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network_layer_name = getattr(module, "network_layer_name", None)
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module = accelerate.hooks.remove_hook_from_module(module, recurse=True)
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if network_layer_name:
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module.network_layer_name = network_layer_name
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sd_model.has_accelerate = False
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def set_accelerate(sd_model):
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def set_accelerate_to_module(model):
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if hasattr(model, "pipe"):
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set_accelerate_to_module(model.pipe)
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if hasattr(model, "_internal_dict"):
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for k in model._internal_dict.keys(): # pylint: disable=protected-access
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component = getattr(model, k, None)
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if isinstance(component, torch.nn.Module):
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component.has_accelerate = True
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sd_model.has_accelerate = True
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set_accelerate_to_module(sd_model)
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if hasattr(sd_model, "prior_pipe"):
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set_accelerate_to_module(sd_model.prior_pipe)
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if hasattr(sd_model, "decoder_pipe"):
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set_accelerate_to_module(sd_model.decoder_pipe)
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def set_diffuser_offload(sd_model, op:str='model', quiet:bool=False):
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t0 = time.time()
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if not shared.native:
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shared.log.warning('Attempting to use offload with backend=original')
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return
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if sd_model is None:
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shared.log.warning(f'{op} is not loaded')
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return
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if not (hasattr(sd_model, "has_accelerate") and sd_model.has_accelerate):
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sd_model.has_accelerate = False
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if shared.opts.diffusers_offload_mode == "none":
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if shared.sd_model_type in should_offload:
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shared.log.warning(f'Setting {op}: offload={shared.opts.diffusers_offload_mode} type={shared.sd_model.__class__.__name__} large model')
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else:
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shared.log.quiet(quiet, f'Setting {op}: offload={shared.opts.diffusers_offload_mode} limit={shared.opts.cuda_mem_fraction}')
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if hasattr(sd_model, 'maybe_free_model_hooks'):
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sd_model.maybe_free_model_hooks()
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sd_model.has_accelerate = False
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if shared.opts.diffusers_offload_mode == "model" and hasattr(sd_model, "enable_model_cpu_offload"):
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try:
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shared.log.quiet(quiet, f'Setting {op}: offload={shared.opts.diffusers_offload_mode} limit={shared.opts.cuda_mem_fraction}')
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if shared.opts.diffusers_move_base or shared.opts.diffusers_move_unet or shared.opts.diffusers_move_refiner:
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shared.opts.diffusers_move_base = False
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shared.opts.diffusers_move_unet = False
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shared.opts.diffusers_move_refiner = False
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shared.log.warning(f'Disabling {op} "Move model to CPU" since "Model CPU offload" is enabled')
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if not hasattr(sd_model, "_all_hooks") or len(sd_model._all_hooks) == 0: # pylint: disable=protected-access
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sd_model.enable_model_cpu_offload(device=devices.device)
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else:
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sd_model.maybe_free_model_hooks()
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set_accelerate(sd_model)
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except Exception as e:
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shared.log.error(f'Setting {op}: offload={shared.opts.diffusers_offload_mode} {e}')
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if shared.opts.diffusers_offload_mode == "sequential" and hasattr(sd_model, "enable_sequential_cpu_offload"):
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try:
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shared.log.debug(f'Setting {op}: offload={shared.opts.diffusers_offload_mode} limit={shared.opts.cuda_mem_fraction}')
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if shared.opts.diffusers_move_base or shared.opts.diffusers_move_unet or shared.opts.diffusers_move_refiner:
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shared.opts.diffusers_move_base = False
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shared.opts.diffusers_move_unet = False
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shared.opts.diffusers_move_refiner = False
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shared.log.warning(f'Disabling {op} "Move model to CPU" since "Sequential CPU offload" is enabled')
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if sd_model.has_accelerate:
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if op == "vae": # reapply sequential offload to vae
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from accelerate import cpu_offload
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sd_model.vae.to("cpu")
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cpu_offload(sd_model.vae, devices.device, offload_buffers=len(sd_model.vae._parameters) > 0) # pylint: disable=protected-access
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else:
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pass # do nothing if offload is already applied
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else:
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sd_model.enable_sequential_cpu_offload(device=devices.device)
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set_accelerate(sd_model)
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except Exception as e:
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shared.log.error(f'Setting {op}: offload={shared.opts.diffusers_offload_mode} {e}')
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if shared.opts.diffusers_offload_mode == "balanced":
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sd_model = apply_balanced_offload(sd_model)
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process_timer.add('offload', time.time() - t0)
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class OffloadHook(accelerate.hooks.ModelHook):
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def __init__(self, checkpoint_name):
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if shared.opts.diffusers_offload_max_gpu_memory > 1:
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shared.opts.diffusers_offload_max_gpu_memory = 0.75
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if shared.opts.diffusers_offload_max_cpu_memory > 1:
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shared.opts.diffusers_offload_max_cpu_memory = 0.75
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self.checkpoint_name = checkpoint_name
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self.min_watermark = shared.opts.diffusers_offload_min_gpu_memory
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self.max_watermark = shared.opts.diffusers_offload_max_gpu_memory
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self.cpu_watermark = shared.opts.diffusers_offload_max_cpu_memory
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self.gpu = int(shared.gpu_memory * shared.opts.diffusers_offload_max_gpu_memory * 1024*1024*1024)
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self.cpu = int(shared.cpu_memory * shared.opts.diffusers_offload_max_cpu_memory * 1024*1024*1024)
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self.offload_map = {}
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self.param_map = {}
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gpu = f'{shared.gpu_memory * shared.opts.diffusers_offload_min_gpu_memory:.3f}-{shared.gpu_memory * shared.opts.diffusers_offload_max_gpu_memory}:{shared.gpu_memory}'
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shared.log.info(f'Offload: type=balanced op=init watermark={self.min_watermark}-{self.max_watermark} gpu={gpu} cpu={shared.cpu_memory:.3f} limit={shared.opts.cuda_mem_fraction:.2f}')
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self.validate()
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super().__init__()
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def validate(self):
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if shared.opts.diffusers_offload_mode != 'balanced':
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return
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if shared.opts.diffusers_offload_min_gpu_memory < 0 or shared.opts.diffusers_offload_min_gpu_memory > 1:
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shared.opts.diffusers_offload_min_gpu_memory = 0.25
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shared.log.warning(f'Offload: type=balanced op=validate: watermark low={shared.opts.diffusers_offload_min_gpu_memory} invalid value')
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if shared.opts.diffusers_offload_max_gpu_memory < 0.1 or shared.opts.diffusers_offload_max_gpu_memory > 1:
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shared.opts.diffusers_offload_max_gpu_memory = 0.75
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shared.log.warning(f'Offload: type=balanced op=validate: watermark high={shared.opts.diffusers_offload_max_gpu_memory} invalid value')
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if shared.opts.diffusers_offload_min_gpu_memory > shared.opts.diffusers_offload_max_gpu_memory:
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shared.opts.diffusers_offload_min_gpu_memory = shared.opts.diffusers_offload_max_gpu_memory
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shared.log.warning(f'Offload: type=balanced op=validate: watermark low={shared.opts.diffusers_offload_min_gpu_memory} reset')
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if shared.opts.diffusers_offload_max_gpu_memory * shared.gpu_memory < 4:
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shared.log.warning(f'Offload: type=balanced op=validate: watermark high={shared.opts.diffusers_offload_max_gpu_memory} low memory')
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def model_size(self):
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return sum(self.offload_map.values())
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def init_hook(self, module):
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return module
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def pre_forward(self, module, *args, **kwargs):
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if devices.normalize_device(module.device) != devices.normalize_device(devices.device):
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device_index = torch.device(devices.device).index
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if device_index is None:
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device_index = 0
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max_memory = { device_index: self.gpu, "cpu": self.cpu }
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device_map = getattr(module, "balanced_offload_device_map", None)
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if device_map is None or max_memory != getattr(module, "balanced_offload_max_memory", None):
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device_map = accelerate.infer_auto_device_map(module, max_memory=max_memory)
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offload_dir = getattr(module, "offload_dir", os.path.join(shared.opts.accelerate_offload_path, module.__class__.__name__))
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module = accelerate.dispatch_model(module, device_map=device_map, offload_dir=offload_dir)
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module._hf_hook.execution_device = torch.device(devices.device) # pylint: disable=protected-access
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module.balanced_offload_device_map = device_map
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module.balanced_offload_max_memory = max_memory
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return args, kwargs
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def post_forward(self, module, output):
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return output
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def detach_hook(self, module):
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return module
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def apply_balanced_offload(sd_model, exclude=[]):
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global offload_hook_instance # pylint: disable=global-statement
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if shared.opts.diffusers_offload_mode != "balanced":
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return sd_model
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t0 = time.time()
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excluded = ['OmniGenPipeline']
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if sd_model.__class__.__name__ in excluded:
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return sd_model
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cached = True
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checkpoint_name = sd_model.sd_checkpoint_info.name if getattr(sd_model, "sd_checkpoint_info", None) is not None else None
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if checkpoint_name is None:
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checkpoint_name = sd_model.__class__.__name__
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if (offload_hook_instance is None) or (offload_hook_instance.min_watermark != shared.opts.diffusers_offload_min_gpu_memory) or (offload_hook_instance.max_watermark != shared.opts.diffusers_offload_max_gpu_memory) or (checkpoint_name != offload_hook_instance.checkpoint_name):
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cached = False
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offload_hook_instance = OffloadHook(checkpoint_name)
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def get_pipe_modules(pipe):
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if hasattr(pipe, "_internal_dict"):
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modules_names = pipe._internal_dict.keys() # pylint: disable=protected-access
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else:
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modules_names = get_signature(pipe).keys()
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modules_names = [m for m in modules_names if m not in exclude and not m.startswith('_')]
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modules = {}
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for module_name in modules_names:
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module_size = offload_hook_instance.offload_map.get(module_name, None)
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if module_size is None:
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module = getattr(pipe, module_name, None)
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if not isinstance(module, torch.nn.Module):
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continue
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try:
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module_size = sum(p.numel() * p.element_size() for p in module.parameters(recurse=True)) / 1024 / 1024 / 1024
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param_num = sum(p.numel() for p in module.parameters(recurse=True)) / 1024 / 1024 / 1024
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except Exception as e:
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shared.log.error(f'Offload: type=balanced op=calc module={module_name} {e}')
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module_size = 0
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offload_hook_instance.offload_map[module_name] = module_size
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offload_hook_instance.param_map[module_name] = param_num
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modules[module_name] = module_size
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modules = sorted(modules.items(), key=lambda x: x[1], reverse=True)
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return modules
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def apply_balanced_offload_to_module(pipe):
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used_gpu, used_ram = devices.torch_gc(fast=True)
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if hasattr(pipe, "pipe"):
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apply_balanced_offload_to_module(pipe.pipe)
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if hasattr(pipe, "_internal_dict"):
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keys = pipe._internal_dict.keys() # pylint: disable=protected-access
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else:
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keys = get_signature(pipe).keys()
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keys = [k for k in keys if k not in exclude and not k.startswith('_')]
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for module_name, module_size in get_pipe_modules(pipe): # pylint: disable=protected-access
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module = getattr(pipe, module_name, None)
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if module is None:
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continue
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network_layer_name = getattr(module, "network_layer_name", None)
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device_map = getattr(module, "balanced_offload_device_map", None)
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max_memory = getattr(module, "balanced_offload_max_memory", None)
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module = accelerate.hooks.remove_hook_from_module(module, recurse=True)
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perc_gpu = used_gpu / shared.gpu_memory
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try:
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prev_gpu = used_gpu
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do_offload = (perc_gpu > shared.opts.diffusers_offload_min_gpu_memory) and (module.device != devices.cpu)
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if do_offload:
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module = module.to(devices.cpu, non_blocking=True)
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used_gpu -= module_size
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cls = module.__class__.__name__
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quant = getattr(module, "quantization_method", None)
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if not cached:
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shared.log.debug(f'Model module={module_name} type={cls} dtype={module.dtype} quant={quant} params={offload_hook_instance.param_map[module_name]:.3f} size={offload_hook_instance.offload_map[module_name]:.3f}')
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debug_move(f'Offload: type=balanced op={"move" if do_offload else "skip"} gpu={prev_gpu:.3f}:{used_gpu:.3f} perc={perc_gpu:.2f} ram={used_ram:.3f} current={module.device} dtype={module.dtype} quant={quant} module={cls} size={module_size:.3f}')
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except Exception as e:
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if 'out of memory' in str(e):
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devices.torch_gc(fast=True, force=True, reason='oom')
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elif 'bitsandbytes' in str(e):
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pass
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else:
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shared.log.error(f'Offload: type=balanced op=apply module={module_name} {e}')
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if os.environ.get('SD_MOVE_DEBUG', None):
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errors.display(e, f'Offload: type=balanced op=apply module={module_name}')
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module.offload_dir = os.path.join(shared.opts.accelerate_offload_path, checkpoint_name, module_name)
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module = accelerate.hooks.add_hook_to_module(module, offload_hook_instance, append=True)
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module._hf_hook.execution_device = torch.device(devices.device) # pylint: disable=protected-access
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if network_layer_name:
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module.network_layer_name = network_layer_name
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if device_map and max_memory:
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module.balanced_offload_device_map = device_map
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module.balanced_offload_max_memory = max_memory
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devices.torch_gc(fast=True, force=True, reason='offload')
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apply_balanced_offload_to_module(sd_model)
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if hasattr(sd_model, "pipe"):
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apply_balanced_offload_to_module(sd_model.pipe)
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if hasattr(sd_model, "prior_pipe"):
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apply_balanced_offload_to_module(sd_model.prior_pipe)
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if hasattr(sd_model, "decoder_pipe"):
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apply_balanced_offload_to_module(sd_model.decoder_pipe)
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if shared.opts.layerwise_quantization:
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model_quant.apply_layerwise(sd_model, quiet=True) # need to reapply since hooks were removed/readded
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if shared.opts.pab_enabled and hasattr(sd_model, 'transformer'):
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pab_config = diffusers.PyramidAttentionBroadcastConfig(
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spatial_attention_block_skip_range=shared.opts.pab_block_skip_range,
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spatial_attention_timestep_skip_range=(int(100 * shared.opts.pab_timestep_skip_start), int(100 * shared.opts.pab_timestep_skip_end)),
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current_timestep_callback=lambda: sd_model.current_timestep, # pylint: disable=protected-access
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)
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try:
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diffusers.apply_pyramid_attention_broadcast(sd_model.transformer, pab_config)
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except Exception: # hook may already exist
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pass
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if not cached:
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shared.log.info(f'Applying PAB: cls={sd_model.transformer.__class__.__name__} block={shared.opts.pab_block_skip_range} start={shared.opts.pab_timestep_skip_start} end={shared.opts.pab_timestep_skip_end}')
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set_accelerate(sd_model)
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t = time.time() - t0
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process_timer.add('offload', t)
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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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debug_move(f'Apply offload: time={t:.2f} type=balanced fn={fn}')
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if not cached:
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shared.log.info(f'Model class={sd_model.__class__.__name__} modules={len(offload_hook_instance.offload_map)} size={offload_hook_instance.model_size():.3f}')
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return sd_model
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