John Pollock
c63b539f1e
Add comprehensive memory cache clearing aligned with ComfyUI patterns to improve stability and reduce OOM incidents in multi-device scenarios. **Addresses Memory/Garbage Collection Issues:** - Created `soft_empty_cache_multigpu()` function in device_utils.py - Replicates ComfyUI's cache clearing for all devices (CUDA, MPS, XPU, NPU, MLU) - Includes CUDA IPC collect optimization like ComfyUI - Strategically placed calls before major memory allocations **Addresses CLIP loading issues:** - Fixed DisTorch2 device device varibale management before text encoder operations **`soft_empty_cache_multigpu()` implementation Aligned with ComfyUI's Patterns:** - Called after GC operations - Placed before major memory allocations - Matches ComfyUI's proven memory management strategy - Same device clearing logic for multi-device scenarios
281 lines
14 KiB
Python
281 lines
14 KiB
Python
"""
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Advanced Checkpoint Loaders for MultiGPU
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Provides device-specific and DisTorch2 sharding for checkpoint components
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"""
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import torch
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import logging
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import hashlib
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import comfy.sd
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import comfy.utils
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import comfy.model_management as mm
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import comfy.model_detection
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import comfy.clip_vision
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from comfy.sd import VAE, CLIP
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from .device_utils import get_device_list, soft_empty_cache_multigpu
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from .distorch_2 import safetensor_allocation_store, safetensor_settings_store, create_safetensor_model_hash, register_patched_safetensor_modelpatcher
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logger = logging.getLogger("MultiGPU")
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checkpoint_device_config = {}
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checkpoint_distorch_config = {}
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original_load_state_dict_guess_config = None
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def patch_load_state_dict_guess_config():
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"""
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Monkey patch the load_state_dict_guess_config function to replace its logic
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with a MultiGPU-aware implementation.
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"""
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global original_load_state_dict_guess_config
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if original_load_state_dict_guess_config is not None:
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logger.info("[MultiGPU] load_state_dict_guess_config is already patched.")
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return
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logger.info("[MultiGPU] Patching comfy.sd.load_state_dict_guess_config for advanced MultiGPU loading.")
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original_load_state_dict_guess_config = comfy.sd.load_state_dict_guess_config
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comfy.sd.load_state_dict_guess_config = patched_load_state_dict_guess_config
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def patched_load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False,
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embedding_directory=None, output_model=True, model_options={},
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te_model_options={}, metadata=None):
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from . import set_current_device, set_current_text_encoder_device, current_device, current_text_encoder_device
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sd_size = sum(p.numel() for p in sd.values() if hasattr(p, 'numel'))
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config_hash = str(sd_size)
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device_config = checkpoint_device_config.get(config_hash)
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distorch_config = checkpoint_distorch_config.get(config_hash)
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if not device_config and not distorch_config:
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return original_load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options, metadata)
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logger.info("--- [MultiGPU] ENTERING Patched Checkpoint Loader ---")
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logger.info(f"Received Device Config: {device_config}")
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logger.info(f"Received DisTorch2 Config: {distorch_config}")
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clip = None
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clipvision = None
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vae = None
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model = None
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model_patcher = None
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original_main_device = current_device
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original_clip_device = current_text_encoder_device
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logger.info(f"Saved original device contexts: UNet/VAE='{original_main_device}', CLIP='{original_clip_device}'")
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try:
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diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(sd)
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parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
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weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
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model_config = comfy.model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)
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if model_config is None:
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logger.warning("[MultiGPU] Warning: Not a standard checkpoint file. Trying to load as diffusion model only.")
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# Simplified fallback for non-checkpoints
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set_current_device(device_config.get('unet_device', original_main_device))
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diffusion_model = comfy.sd.load_diffusion_model_state_dict(sd, model_options={})
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if diffusion_model is None:
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return None
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return (diffusion_model, None, VAE(sd={}), None)
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logger.info(f"[MultiGPU] Detected Model Config: {type(model_config).__name__}, Parameters: {parameters/10**9:.2f}B")
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unet_weight_dtype = list(model_config.supported_inference_dtypes)
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if model_config.scaled_fp8 is not None:
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weight_dtype = None
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model_config.custom_operations = model_options.get("custom_operations", None)
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unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
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if unet_dtype is None:
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unet_dtype = mm.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
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unet_compute_device = device_config.get('unet_device', original_main_device)
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manual_cast_dtype = mm.unet_manual_cast(unet_dtype, torch.device(unet_compute_device), model_config.supported_inference_dtypes)
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model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
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logger.info(f"UNet DType: {unet_dtype}, Manual Cast: {manual_cast_dtype}")
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if model_config.clip_vision_prefix is not None and output_clipvision:
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clipvision = comfy.clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
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if output_model:
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unet_compute_device = device_config.get('unet_device', original_main_device)
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set_current_device(unet_compute_device)
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inital_load_device = mm.unet_inital_load_device(parameters, unet_dtype)
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model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
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soft_empty_cache_multigpu(logger)
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model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=unet_compute_device, offload_device=mm.unet_offload_device())
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if distorch_config and 'unet_allocation' in distorch_config:
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register_patched_safetensor_modelpatcher()
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model_hash = create_safetensor_model_hash(model_patcher, "checkpoint_loader_unet")
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safetensor_allocation_store[model_hash] = distorch_config['unet_allocation']
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safetensor_settings_store[model_hash] = distorch_config.get('unet_settings','')
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model.is_distorch = True
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model._distorch_high_precision_loras = distorch_config.get('high_precision_loras', True)
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logger.info(f"Stored DisTorch2 config for UNet (hash {model_hash[:8]}): {distorch_config['unet_allocation']}")
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model.load_model_weights(sd, diffusion_model_prefix)
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if output_vae:
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vae_target_device = torch.device(device_config.get('vae_device', original_main_device))
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set_current_device(vae_target_device) # Use main device context for VAE
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vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
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vae_sd = model_config.process_vae_state_dict(vae_sd)
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vae = VAE(sd=vae_sd, metadata=metadata)
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if output_clip:
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clip_target_device = device_config.get('clip_device', original_clip_device)
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set_current_text_encoder_device(clip_target_device)
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clip_target = model_config.clip_target(state_dict=sd)
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if clip_target is not None:
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clip_sd = model_config.process_clip_state_dict(sd)
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if len(clip_sd) > 0:
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soft_empty_cache_multigpu(logger)
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clip_params = comfy.utils.calculate_parameters(clip_sd)
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clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=clip_params, model_options=te_model_options)
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if distorch_config and 'clip_allocation' in distorch_config:
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if hasattr(clip, 'patcher'):
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register_patched_safetensor_modelpatcher()
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clip_hash = create_safetensor_model_hash(clip.patcher, "checkpoint_loader_clip")
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safetensor_allocation_store[clip_hash] = distorch_config['clip_allocation']
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safetensor_settings_store[clip_hash] = distorch_config.get('clip_settings','')
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clip.patcher.model.is_distorch = True
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clip.patcher.model._distorch_high_precision_loras = distorch_config.get('high_precision_loras', True)
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logger.info(f"Stored DisTorch2 config for CLIP (hash {clip_hash[:8]}): {distorch_config['clip_allocation']}")
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m, u = clip.load_sd(clip_sd, full_model=True) # This respects the patched text_encoder_device
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if len(m) > 0: logger.warning(f"CLIP missing keys: {m}")
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if len(u) > 0: logger.debug(f"CLIP unexpected keys: {u}")
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logger.info("CLIP Loaded.")
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else:
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logger.warning("No CLIP/text encoder weights in checkpoint.")
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else:
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logger.warning("CLIP target not found in model config.")
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finally:
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# --- Restore original device contexts and clean up ---
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set_current_device(original_main_device)
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set_current_text_encoder_device(original_clip_device)
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if config_hash in checkpoint_device_config:
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del checkpoint_device_config[config_hash]
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if config_hash in checkpoint_distorch_config:
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del checkpoint_distorch_config[config_hash]
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logger.info(f"Restored original device contexts. UNet/VAE='{original_main_device}', CLIP='{original_clip_device}'")
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logger.info("--- [MultiGPU] EXITING Patched Checkpoint Loader ---")
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return (model_patcher, clip, vae, clipvision)
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class CheckpointLoaderAdvancedMultiGPU:
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@classmethod
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def INPUT_TYPES(s):
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import folder_paths
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devices = get_device_list()
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default_device = devices[1] if len(devices) > 1 else devices[0]
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return {
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"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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"unet_device": (devices, {"default": default_device}),
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"clip_device": (devices, {"default": default_device}),
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"vae_device": (devices, {"default": default_device}),
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}
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}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "multigpu"
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TITLE = "Checkpoint Loader Advanced (MultiGPU)"
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def load_checkpoint(self, ckpt_name, unet_device, clip_device, vae_device):
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patch_load_state_dict_guess_config()
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import folder_paths
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import comfy.utils
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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sd = comfy.utils.load_torch_file(ckpt_path)
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sd_size = sum(p.numel() for p in sd.values() if hasattr(p, 'numel'))
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config_hash = str(sd_size)
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checkpoint_device_config[config_hash] = {
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'unet_device': unet_device, 'clip_device': clip_device, 'vae_device': vae_device
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}
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# Load using standard loader, our patch will intercept
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from nodes import CheckpointLoaderSimple
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return CheckpointLoaderSimple().load_checkpoint(ckpt_name)
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class CheckpointLoaderAdvancedDisTorch2MultiGPU:
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@classmethod
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def INPUT_TYPES(s):
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import folder_paths
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devices = get_device_list()
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compute_device = devices[1] if len(devices) > 1 else devices[0]
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return {
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"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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"unet_compute_device": (devices, {"default": compute_device}),
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"unet_virtual_vram_gb": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 128.0, "step": 0.1}),
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"unet_donor_device": ("STRING", {"default": "cpu"}),
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"clip_compute_device": (devices, {"default": "cpu"}),
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"clip_virtual_vram_gb": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 128.0, "step": 0.1}),
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"clip_donor_device": ("STRING", {"default": "cpu"}),
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"vae_device": (devices, {"default": compute_device}),
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}, "optional": {
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"unet_expert_mode_allocations": ("STRING", {"multiline": False, "default": ""}),
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"clip_expert_mode_allocations": ("STRING", {"multiline": False, "default": ""}),
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"high_precision_loras": ("BOOLEAN", {"default": True}),
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}
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}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "multigpu/distorch_2"
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TITLE = "Checkpoint Loader Advanced (DisTorch2)"
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def load_checkpoint(self, ckpt_name, unet_compute_device, unet_virtual_vram_gb, unet_donor_device,
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clip_compute_device, clip_virtual_vram_gb, clip_donor_device, vae_device,
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unet_expert_mode_allocations="", clip_expert_mode_allocations="", high_precision_loras=True):
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patch_load_state_dict_guess_config()
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import folder_paths
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import comfy.utils
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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sd = comfy.utils.load_torch_file(ckpt_path)
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sd_size = sum(p.numel() for p in sd.values() if hasattr(p, 'numel'))
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config_hash = str(sd_size)
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checkpoint_device_config[config_hash] = {
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'unet_device': unet_compute_device,
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'clip_device': clip_compute_device,
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'vae_device': vae_device
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}
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unet_vram_str = f"{unet_compute_device};{unet_virtual_vram_gb};{unet_donor_device}"
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unet_alloc = f"{unet_expert_mode_allocations}#{unet_vram_str}"
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clip_vram_str = f"{clip_compute_device};{clip_virtual_vram_gb};{clip_donor_device}"
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clip_alloc = f"{clip_expert_mode_allocations}#{clip_vram_str}"
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checkpoint_distorch_config[config_hash] = {
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'unet_allocation': unet_alloc,
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'clip_allocation': clip_alloc,
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'high_precision_loras': high_precision_loras,
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'unet_settings': hashlib.sha256(f"{unet_alloc}{high_precision_loras}".encode()).hexdigest(),
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'clip_settings': hashlib.sha256(f"{clip_alloc}{high_precision_loras}".encode()).hexdigest(),
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}
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from nodes import CheckpointLoaderSimple
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return CheckpointLoaderSimple().load_checkpoint(ckpt_name)
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