feat: Add advanced checkpoint loaders for MultiGPU and DisTorch2
This commit is contained in:
John Pollock
@@ -191,10 +191,18 @@ from .distorch_2 import (
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override_class_with_distorch_safetensor_v2
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)
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# Import advanced checkpoint loaders
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from .checkpoint_multigpu import (
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CheckpointLoaderAdvancedMultiGPU,
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CheckpointLoaderAdvancedDisTorch2MultiGPU
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)
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# Initialize NODE_CLASS_MAPPINGS
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NODE_CLASS_MAPPINGS = {
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"DeviceSelectorMultiGPU": DeviceSelectorMultiGPU,
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"HunyuanVideoEmbeddingsAdapter": HunyuanVideoEmbeddingsAdapter,
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"CheckpointLoaderAdvancedMultiGPU": CheckpointLoaderAdvancedMultiGPU,
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"CheckpointLoaderAdvancedDisTorch2MultiGPU": CheckpointLoaderAdvancedDisTorch2MultiGPU,
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}
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# Standard MultiGPU nodes
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@@ -0,0 +1,310 @@
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"""
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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 copy
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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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from .device_utils import get_device_list
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from .distorch_2 import safetensor_allocation_store, create_safetensor_model_hash
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logger = logging.getLogger("MultiGPU")
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# Store checkpoint loading configurations
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checkpoint_device_config = {}
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checkpoint_distorch_config = {}
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# Store the original function
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original_load_state_dict_guess_config = None
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def create_checkpoint_config_hash(checkpoint_name, config_str):
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"""Create a unique hash for checkpoint configuration"""
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identifier = f"{checkpoint_name}_{config_str}"
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return hashlib.sha256(identifier.encode()).hexdigest()
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def patch_load_state_dict_guess_config():
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"""Monkey patch the load_state_dict_guess_config function to support per-component device selection"""
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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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return # Already patched
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original_load_state_dict_guess_config = comfy.sd.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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# Import here to avoid circular imports
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from . import set_current_device, set_current_text_encoder_device, current_device, current_text_encoder_device
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# Check if we have a device configuration for this checkpoint
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# We use the state dict size as a simple identifier
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sd_size = sum(t.numel() for t in sd.values() if hasattr(t, '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 device_config or distorch_config:
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logger.info(f"[MultiGPU] Using custom device configuration for checkpoint")
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# Save original devices
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original_unet_device = current_device
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original_clip_device = current_text_encoder_device
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# Handle UNet device/DisTorch config
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if device_config and 'unet_device' in device_config:
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set_current_device(device_config['unet_device'])
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logger.info(f"[MultiGPU] Setting UNet device to: {device_config['unet_device']}")
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# Apply DisTorch2 config for UNet if present
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if distorch_config and 'unet_allocation' in distorch_config:
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# We'll store this for when the model patcher is created
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logger.info(f"[MultiGPU] DisTorch2 UNet allocation will be applied: {distorch_config['unet_allocation']}")
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# Call original function to load the checkpoint
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result = original_load_state_dict_guess_config(
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sd, output_vae=output_vae, output_clip=output_clip, output_clipvision=output_clipvision,
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embedding_directory=embedding_directory, output_model=output_model,
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model_options=model_options, te_model_options=te_model_options, metadata=metadata
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)
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model_patcher, clip, vae, clipvision = result
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# Apply DisTorch2 configurations after loading
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if distorch_config:
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if model_patcher and 'unet_allocation' in distorch_config:
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model_hash = create_safetensor_model_hash(model_patcher, "checkpoint_loader")
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safetensor_allocation_store[model_hash] = distorch_config['unet_allocation']
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if 'unet_settings' in distorch_config:
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from .distorch_2 import safetensor_settings_store
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safetensor_settings_store[model_hash] = distorch_config['unet_settings']
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logger.info(f"[MultiGPU] Applied DisTorch2 config to UNet: {model_hash[:8]}")
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if clip and 'clip_allocation' in distorch_config:
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# For CLIP, we need to get the model from the CLIP object
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if hasattr(clip, 'patcher'):
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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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if 'clip_settings' in distorch_config:
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from .distorch_2 import safetensor_settings_store
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safetensor_settings_store[clip_hash] = distorch_config['clip_settings']
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logger.info(f"[MultiGPU] Applied DisTorch2 config to CLIP: {clip_hash[:8]}")
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# Handle CLIP device
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if device_config and 'clip_device' in device_config and clip:
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set_current_text_encoder_device(device_config['clip_device'])
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logger.info(f"[MultiGPU] Setting CLIP device to: {device_config['clip_device']}")
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# Force CLIP to load on the specified device
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if hasattr(clip, 'patcher'):
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clip.patcher.load(force_patch_weights=True)
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# Handle VAE device
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if device_config and 'vae_device' in device_config and vae:
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vae_device = torch.device(device_config['vae_device'])
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logger.info(f"[MultiGPU] Setting VAE device to: {device_config['vae_device']}")
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# Move VAE to specified device
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if hasattr(vae, 'first_stage_model'):
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vae.first_stage_model = vae.first_stage_model.to(vae_device)
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# Clean up stored configs
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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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return result
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else:
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# No custom config, use original behavior
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return original_load_state_dict_guess_config(
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sd, output_vae=output_vae, output_clip=output_clip, output_clipvision=output_clipvision,
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embedding_directory=embedding_directory, output_model=output_model,
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model_options=model_options, te_model_options=te_model_options, metadata=metadata
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)
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# Apply the patch
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comfy.sd.load_state_dict_guess_config = patched_load_state_dict_guess_config
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logger.info("[MultiGPU] Successfully patched load_state_dict_guess_config")
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class CheckpointLoaderAdvancedMultiGPU:
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"""
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Checkpoint loader that allows loading UNet, CLIP, and VAE to different devices
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"""
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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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# Apply the patch if not already applied
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patch_load_state_dict_guess_config()
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# Store device configuration
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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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# Use state dict size as identifier
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sd_size = sum(t.numel() for t in sd.values() if hasattr(t, 'numel'))
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config_hash = str(sd_size)
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# Store the device configuration
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checkpoint_device_config[config_hash] = {
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'unet_device': unet_device,
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'clip_device': clip_device,
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'vae_device': vae_device
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}
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logger.info(f"[MultiGPU] CheckpointLoaderAdvanced configured - UNet: {unet_device}, CLIP: {clip_device}, VAE: {vae_device}")
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# Load the checkpoint - our patched function will handle device placement
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from nodes import CheckpointLoaderSimple
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loader = CheckpointLoaderSimple()
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return loader.load_checkpoint(ckpt_name)
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class CheckpointLoaderAdvancedDisTorch2MultiGPU:
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"""
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Checkpoint loader with full DisTorch2 sharding for UNet and CLIP, device selection for VAE
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"""
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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 DisTorch2 settings
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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": (devices, {"default": "cpu"}),
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# CLIP DisTorch2 settings
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"clip_compute_device": (devices, {"default": compute_device}),
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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": (devices, {"default": "cpu"}),
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# VAE simple device
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"vae_device": (devices, {"default": compute_device}),
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},
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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,
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unet_compute_device, unet_virtual_vram_gb, unet_donor_device,
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clip_compute_device, clip_virtual_vram_gb, clip_donor_device,
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vae_device,
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unet_expert_mode_allocations="", clip_expert_mode_allocations="",
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high_precision_loras=True):
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# Apply the patch if not already applied
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patch_load_state_dict_guess_config()
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# Register DisTorch2 model patcher
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from .distorch_2 import register_patched_safetensor_modelpatcher
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register_patched_safetensor_modelpatcher()
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# Store device configuration
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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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# Use state dict size as identifier
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sd_size = sum(t.numel() for t in sd.values() if hasattr(t, 'numel'))
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config_hash = str(sd_size)
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# Store device configuration
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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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# Build DisTorch2 allocation strings
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unet_vram_string = ""
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if unet_virtual_vram_gb > 0:
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unet_vram_string = f"{unet_compute_device};{unet_virtual_vram_gb};{unet_donor_device}"
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elif unet_expert_mode_allocations:
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unet_vram_string = unet_compute_device
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unet_allocation = f"{unet_expert_mode_allocations}#{unet_vram_string}" if unet_expert_mode_allocations or unet_vram_string else ""
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clip_vram_string = ""
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if clip_virtual_vram_gb > 0:
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clip_vram_string = f"{clip_compute_device};{clip_virtual_vram_gb};{clip_donor_device}"
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elif clip_expert_mode_allocations:
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clip_vram_string = clip_compute_device
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clip_allocation = f"{clip_expert_mode_allocations}#{clip_vram_string}" if clip_expert_mode_allocations or clip_vram_string else ""
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# Create settings hashes for DisTorch2
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unet_settings_str = f"{unet_compute_device}{unet_virtual_vram_gb}{unet_donor_device}{unet_expert_mode_allocations}{high_precision_loras}"
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unet_settings_hash = hashlib.sha256(unet_settings_str.encode()).hexdigest()
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clip_settings_str = f"{clip_compute_device}{clip_virtual_vram_gb}{clip_donor_device}{clip_expert_mode_allocations}{high_precision_loras}"
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clip_settings_hash = hashlib.sha256(clip_settings_str.encode()).hexdigest()
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# Store DisTorch2 configuration
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checkpoint_distorch_config[config_hash] = {
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'unet_allocation': unet_allocation,
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'unet_settings': unet_settings_hash,
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'clip_allocation': clip_allocation,
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'clip_settings': clip_settings_hash,
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'high_precision_loras': high_precision_loras
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}
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logger.info(f"[MultiGPU] CheckpointLoaderDisTorch2 configured:")
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logger.info(f" UNet: compute={unet_compute_device}, vram={unet_virtual_vram_gb}GB, donor={unet_donor_device}")
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logger.info(f" CLIP: compute={clip_compute_device}, vram={clip_virtual_vram_gb}GB, donor={clip_donor_device}")
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logger.info(f" VAE: device={vae_device}")
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# Load the checkpoint - our patched function will handle device placement and DisTorch2
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from nodes import CheckpointLoaderSimple
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loader = CheckpointLoaderSimple()
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# Set high precision loras flag
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result = loader.load_checkpoint(ckpt_name)
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# Store high_precision_loras in the models
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model_patcher, clip, vae = result
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if model_patcher and hasattr(model_patcher, 'model'):
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model_patcher.model._distorch_high_precision_loras = high_precision_loras
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if clip and hasattr(clip, 'patcher') and hasattr(clip.patcher, 'model'):
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clip.patcher.model._distorch_high_precision_loras = high_precision_loras
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return result
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