John Pollock
63ff1a4064
- Add comfyui_memory_load and create_model_identifier utilities (device_utils) - Log GPU memory before/after UNet, VAE, and CLIP construction and after UNet weight load - Include model identifiers in logs to correlate memory to specific patchers - Guard logging calls with try/except to avoid impacting load flow - Improves observability of memory usage for multi-GPU checkpoints and aids OOM/debugging
535 lines
23 KiB
Python
535 lines
23 KiB
Python
"""
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DisTorch GGUF/GGML Memory Management Module
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Contains all GGUF/GGML related code for distributed memory management
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"""
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import sys
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import torch
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import logging
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import hashlib
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logger = logging.getLogger("MultiGPU")
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import copy
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from collections import defaultdict
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import comfy.model_management as mm
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from .device_utils import get_device_list, soft_empty_cache_multigpu, comfyui_memory_load
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# Global store for model allocations
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model_allocation_store = {}
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def create_model_hash(model, caller):
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"""Create a unique hash for a model to track allocations"""
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model_type = type(model.model).__name__
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model_size = model.model_size()
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first_layers = str(list(model.model_state_dict().keys())[:3])
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identifier = f"{model_type}_{model_size}_{first_layers}"
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final_hash = hashlib.sha256(identifier.encode()).hexdigest()
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logger.debug(f"[MultiGPU_DisTorch_HASH] Created hash for {caller}: {final_hash[:8]}...")
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return final_hash
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def register_patched_ggufmodelpatcher():
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"""Register and patch the GGUFModelPatcher for distributed loading"""
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["UnetLoaderGGUF"]
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module = sys.modules[original_loader.__module__]
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if not hasattr(module.GGUFModelPatcher, '_patched'):
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original_load = module.GGUFModelPatcher.load
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def new_load(self, *args, force_patch_weights=False, **kwargs):
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global model_allocation_store
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try:
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logger.info(comfyui_memory_load("pre-model-load:gguf"))
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except Exception:
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pass
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super(module.GGUFModelPatcher, self).load(*args, force_patch_weights=True, **kwargs)
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debug_hash = create_model_hash(self, "patcher")
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try:
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logger.info(comfyui_memory_load(f"post-model-load:gguf:{debug_hash[:8]}"))
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except Exception:
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pass
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linked = []
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module_count = 0
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for n, m in self.model.named_modules():
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module_count += 1
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if hasattr(m, "weight"):
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device = getattr(m.weight, "device", None)
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if device is not None:
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linked.append((n, m))
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continue
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if hasattr(m, "bias"):
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device = getattr(m.bias, "device", None)
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if device is not None:
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linked.append((n, m))
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continue
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if linked:
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if hasattr(self, 'model'):
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debug_hash = create_model_hash(self, "patcher")
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debug_allocations = model_allocation_store.get(debug_hash)
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if debug_allocations:
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logger.info("[MultiGPU DisTorch GGUF] Invoking soft_empty_cache_multigpu before GGUF device assignment")
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soft_empty_cache_multigpu()
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device_assignments = analyze_ggml_loading(self.model, debug_allocations)['device_assignments']
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for device, layers in device_assignments.items():
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target_device = torch.device(device)
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for n, m, _ in layers:
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m.to(self.load_device).to(target_device)
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self.mmap_released = True
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module.GGUFModelPatcher.load = new_load
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module.GGUFModelPatcher._patched = True
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def analyze_ggml_loading(model, allocations_str):
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"""Analyze and distribute GGML model layers across devices"""
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DEVICE_RATIOS_DISTORCH = {}
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device_table = {}
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distorch_alloc = allocations_str
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virtual_vram_gb = 0.0
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if '#' in allocations_str:
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distorch_alloc, virtual_vram_str = allocations_str.split('#')
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if not distorch_alloc:
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distorch_alloc = calculate_vvram_allocation_string(model, virtual_vram_str)
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eq_line = "=" * 47
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dash_line = "-" * 47
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fmt_assign = "{:<12}{:>10}{:>14}{:>10}"
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for allocation in distorch_alloc.split(';'):
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dev_name, fraction = allocation.split(',')
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fraction = float(fraction)
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total_mem_bytes = mm.get_total_memory(torch.device(dev_name))
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alloc_gb = (total_mem_bytes * fraction) / (1024**3)
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DEVICE_RATIOS_DISTORCH[dev_name] = alloc_gb
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device_table[dev_name] = {
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"fraction": fraction,
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"total_gb": total_mem_bytes / (1024**3),
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"alloc_gb": alloc_gb
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}
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logger.info(eq_line)
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logger.info(" DisTorch Model Device Allocations")
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logger.info(eq_line)
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logger.info(fmt_assign.format("Device", "Alloc %", "Total (GB)", " Alloc (GB)"))
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logger.info(dash_line)
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sorted_devices = sorted(device_table.keys(), key=lambda d: (d == "cpu", d))
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for dev in sorted_devices:
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frac = device_table[dev]["fraction"]
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tot_gb = device_table[dev]["total_gb"]
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alloc_gb = device_table[dev]["alloc_gb"]
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logger.info(fmt_assign.format(dev,f"{int(frac * 100)}%",f"{tot_gb:.2f}",f"{alloc_gb:.2f}"))
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logger.info(dash_line)
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layer_summary = {}
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layer_list = []
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memory_by_type = defaultdict(int)
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total_memory = 0
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for name, module in model.named_modules():
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if hasattr(module, "weight"):
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layer_type = type(module).__name__
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layer_summary[layer_type] = layer_summary.get(layer_type, 0) + 1
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layer_list.append((name, module, layer_type))
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layer_memory = 0
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if module.weight is not None:
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layer_memory += module.weight.numel() * module.weight.element_size()
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if hasattr(module, "bias") and module.bias is not None:
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layer_memory += module.bias.numel() * module.bias.element_size()
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memory_by_type[layer_type] += layer_memory
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total_memory += layer_memory
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logger.info(" DisTorch Model Layer Distribution")
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logger.info(dash_line)
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fmt_layer = "{:<12}{:>10}{:>14}{:>10}"
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logger.info(fmt_layer.format("Layer Type", "Layers", "Memory (MB)", "% Total"))
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logger.info(dash_line)
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for layer_type, count in layer_summary.items():
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mem_mb = memory_by_type[layer_type] / (1024 * 1024)
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mem_percent = (memory_by_type[layer_type] / total_memory) * 100 if total_memory > 0 else 0
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logger.info(fmt_layer.format(layer_type,str(count),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
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logger.info(dash_line)
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nonzero_devices = [d for d, r in DEVICE_RATIOS_DISTORCH.items() if r > 0]
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nonzero_total_ratio = sum(DEVICE_RATIOS_DISTORCH[d] for d in nonzero_devices)
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device_assignments = {device: [] for device in DEVICE_RATIOS_DISTORCH.keys()}
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total_layers = len(layer_list)
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current_layer = 0
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for idx, device in enumerate(nonzero_devices):
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ratio = DEVICE_RATIOS_DISTORCH[device]
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if idx == len(nonzero_devices) - 1:
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device_layer_count = total_layers - current_layer
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else:
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device_layer_count = int((ratio / nonzero_total_ratio) * total_layers)
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start_idx = current_layer
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end_idx = current_layer + device_layer_count
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device_assignments[device] = layer_list[start_idx:end_idx]
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current_layer += device_layer_count
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logger.info("DisTorch Model Final Device/Layer Assignments")
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logger.info(dash_line)
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fmt_assign = "{:<12}{:>10}{:>14}{:>10}"
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logger.info(fmt_assign.format("Device", "Layers", "Memory (MB)", "% Total"))
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logger.info(dash_line)
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total_assigned_memory = 0
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device_memories = {}
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for device, layers in device_assignments.items():
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device_memory = 0
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for layer_type in layer_summary:
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type_layers = sum(1 for _, _, lt in layers if lt == layer_type)
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if layer_summary[layer_type] > 0:
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mem_per_layer = memory_by_type[layer_type] / layer_summary[layer_type]
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device_memory += mem_per_layer * type_layers
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device_memories[device] = device_memory
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total_assigned_memory += device_memory
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sorted_assignments = sorted(device_assignments.keys(), key=lambda d: (d == "cpu", d))
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for dev in sorted_assignments:
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layers = device_assignments[dev]
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mem_mb = device_memories[dev] / (1024 * 1024)
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mem_percent = (device_memories[dev] / total_memory) * 100 if total_memory > 0 else 0
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logger.info(fmt_assign.format(dev,str(len(layers)),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
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logger.info(dash_line)
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return {"device_assignments": device_assignments}
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def calculate_vvram_allocation_string(model, virtual_vram_str):
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"""Calculate virtual VRAM allocation string for distributed loading"""
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recipient_device, vram_amount, donors = virtual_vram_str.split(';')
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virtual_vram_gb = float(vram_amount)
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eq_line = "=" * 47
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dash_line = "-" * 47
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fmt_assign = "{:<8} {:<6} {:>11} {:>9} {:>9}"
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logger.info(eq_line)
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logger.info(" DisTorch Model Virtual VRAM Analysis")
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logger.info(eq_line)
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logger.info(fmt_assign.format("Object", "Role", "Original(GB)", "Total(GB)", "Virt(GB)"))
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logger.info(dash_line)
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recipient_vram = mm.get_total_memory(torch.device(recipient_device)) / (1024**3)
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recipient_virtual = recipient_vram + virtual_vram_gb
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logger.info(fmt_assign.format(recipient_device, 'recip', f"{recipient_vram:.2f}GB",f"{recipient_virtual:.2f}GB", f"+{virtual_vram_gb:.2f}GB"))
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ram_donors = [d for d in donors.split(',') if d != 'cpu']
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remaining_vram_needed = virtual_vram_gb
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donor_device_info = {}
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donor_allocations = {}
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for donor in ram_donors:
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donor_vram = mm.get_total_memory(torch.device(donor)) / (1024**3)
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max_donor_capacity = donor_vram * 0.9
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donation = min(remaining_vram_needed, max_donor_capacity)
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donor_virtual = donor_vram - donation
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remaining_vram_needed -= donation
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donor_allocations[donor] = donation
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donor_device_info[donor] = (donor_vram, donor_virtual)
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logger.info(fmt_assign.format(donor, 'donor', f"{donor_vram:.2f}GB", f"{donor_virtual:.2f}GB", f"-{donation:.2f}GB"))
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system_dram_gb = mm.get_total_memory(torch.device('cpu')) / (1024**3)
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cpu_donation = remaining_vram_needed
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cpu_virtual = system_dram_gb - cpu_donation
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donor_allocations['cpu'] = cpu_donation
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logger.info(fmt_assign.format('cpu', 'donor', f"{system_dram_gb:.2f}GB", f"{cpu_virtual:.2f}GB", f"-{cpu_donation:.2f}GB"))
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logger.info(dash_line)
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layer_summary = {}
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layer_list = []
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memory_by_type = defaultdict(int)
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total_memory = 0
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for name, module in model.named_modules():
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if hasattr(module, "weight"):
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layer_type = type(module).__name__
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layer_summary[layer_type] = layer_summary.get(layer_type, 0) + 1
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layer_list.append((name, module, layer_type))
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layer_memory = 0
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if module.weight is not None:
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layer_memory += module.weight.numel() * module.weight.element_size()
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if hasattr(module, "bias") and module.bias is not None:
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layer_memory += module.bias.numel() * module.bias.element_size()
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memory_by_type[layer_type] += layer_memory
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total_memory += layer_memory
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model_size_gb = total_memory / (1024**3)
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new_model_size_gb = max(0, model_size_gb - virtual_vram_gb)
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logger.info(fmt_assign.format('model', 'model', f"{model_size_gb:.2f}GB",f"{new_model_size_gb:.2f}GB", f"-{virtual_vram_gb:.2f}GB"))
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if model_size_gb > (recipient_vram * 0.9):
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on_recipient = recipient_vram * 0.9
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on_virtuals = model_size_gb - on_recipient
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logger.info(f"\nWarning: Model size is greater than 90% of recipient VRAM. {on_virtuals:.2f} GB of GGML Layers Offloaded Automatically to Virtual VRAM.\n")
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else:
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on_recipient = model_size_gb
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on_virtuals = 0
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new_on_recipient = max(0, on_recipient - virtual_vram_gb)
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allocation_parts = []
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recipient_percent = new_on_recipient / recipient_vram
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allocation_parts.append(f"{recipient_device},{recipient_percent:.4f}")
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for donor in ram_donors:
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donor_vram = donor_device_info[donor][0]
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donor_percent = donor_allocations[donor] / donor_vram
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allocation_parts.append(f"{donor},{donor_percent:.4f}")
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cpu_percent = donor_allocations['cpu'] / system_dram_gb
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allocation_parts.append(f"cpu,{cpu_percent:.4f}")
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allocation_string = ";".join(allocation_parts)
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fmt_mem = "{:<20}{:>20}"
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logger.info(fmt_mem.format("\n v1 Expert String", allocation_string))
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return allocation_string
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def override_class_with_distorch_gguf(cls):
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"""Legacy DisTorch wrapper for GGUF models for backward compatibility."""
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from . import current_device
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class NodeOverrideDisTorchGGUFLegacy(cls):
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@classmethod
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def INPUT_TYPES(s):
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inputs = copy.deepcopy(cls.INPUT_TYPES())
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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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inputs["optional"] = inputs.get("optional", {})
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inputs["optional"]["device"] = (devices, {"default": default_device})
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inputs["optional"]["virtual_vram_gb"] = ("FLOAT", {"default": 4.0, "min": 0.0, "max": 24.0, "step": 0.1})
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inputs["optional"]["use_other_vram"] = ("BOOLEAN", {"default": False})
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inputs["optional"]["expert_mode_allocations"] = ("STRING", {
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"multiline": False,
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"default": "",
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})
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return inputs
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CATEGORY = "multigpu/legacy"
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FUNCTION = "override"
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if hasattr(cls, 'TITLE'):
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TITLE = f"{cls.TITLE} (Legacy)"
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else:
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TITLE = "Legacy DisTorch Node"
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def override(self, *args, device=None, expert_mode_allocations=None, use_other_vram=None, virtual_vram_gb=0.0, **kwargs):
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from . import set_current_device
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if device is not None:
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set_current_device(device)
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register_patched_ggufmodelpatcher()
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fn = getattr(super(), cls.FUNCTION)
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out = fn(*args, **kwargs)
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vram_string = ""
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if virtual_vram_gb > 0:
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if use_other_vram:
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available_devices = [d for d in get_device_list() if d != "cpu"]
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other_devices = [d for d in available_devices if d != device]
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other_devices.sort(key=lambda x: int(x.split(':')[1] if ':' in x else x[-1]), reverse=False)
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device_string = ','.join(other_devices + ['cpu'])
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vram_string = f"{device};{virtual_vram_gb};{device_string}"
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else:
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vram_string = f"{device};{virtual_vram_gb};cpu"
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full_allocation = f"{expert_mode_allocations}#{vram_string}" if expert_mode_allocations or vram_string else ""
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if hasattr(out[0], 'model'):
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model_hash = create_model_hash(out[0], "override")
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model_allocation_store[model_hash] = full_allocation
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elif hasattr(out[0], 'patcher') and hasattr(out[0].patcher, 'model'):
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model_hash = create_model_hash(out[0].patcher, "override")
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model_allocation_store[model_hash] = full_allocation
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return out
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return NodeOverrideDisTorchGGUFLegacy
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def override_class_with_distorch_gguf_v2(cls):
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"""DisTorch 2.0 wrapper for GGUF models."""
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from . import current_device
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class NodeOverrideDisTorchGGUFv2(cls):
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@classmethod
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def INPUT_TYPES(s):
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inputs = copy.deepcopy(cls.INPUT_TYPES())
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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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inputs["optional"] = inputs.get("optional", {})
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inputs["optional"]["compute_device"] = (devices, {"default": compute_device})
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inputs["optional"]["virtual_vram_gb"] = ("FLOAT", {"default": 4.0, "min": 0.0, "max": 128.0, "step": 0.1})
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inputs["optional"]["donor_device"] = (devices, {"default": "cpu"})
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inputs["optional"]["expert_mode_allocations"] = ("STRING", {"multiline": False, "default": ""})
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return inputs
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CATEGORY = "multigpu/distorch_2"
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FUNCTION = "override"
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def override(self, *args, compute_device=None, virtual_vram_gb=4.0,
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donor_device="cpu", expert_mode_allocations="", **kwargs):
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from . import set_current_device
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if compute_device is not None:
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set_current_device(compute_device)
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register_patched_ggufmodelpatcher()
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fn = getattr(super(), cls.FUNCTION)
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out = fn(*args, **kwargs)
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vram_string = ""
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if virtual_vram_gb > 0:
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vram_string = f"{compute_device};{virtual_vram_gb};{donor_device}"
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full_allocation = f"{expert_mode_allocations}#{vram_string}" if expert_mode_allocations or vram_string else ""
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logger.info(f"[MultiGPU_DisTorch] Full allocation string: {full_allocation}")
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if hasattr(out[0], 'model'):
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model_hash = create_model_hash(out[0], "override")
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model_allocation_store[model_hash] = full_allocation
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elif hasattr(out[0], 'patcher') and hasattr(out[0].patcher, 'model'):
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model_hash = create_model_hash(out[0].patcher, "override")
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model_allocation_store[model_hash] = full_allocation
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return out
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return NodeOverrideDisTorchGGUFv2
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def override_class_with_distorch_clip(cls):
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"""DisTorch wrapper for CLIP models with GGUF support"""
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from . import current_text_encoder_device
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class NodeOverrideDisTorch(cls):
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@classmethod
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def INPUT_TYPES(s):
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inputs = copy.deepcopy(cls.INPUT_TYPES())
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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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inputs["optional"] = inputs.get("optional", {})
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inputs["optional"]["device"] = (devices, {"default": default_device})
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inputs["optional"]["virtual_vram_gb"] = ("FLOAT", {"default": 4.0, "min": 0.0, "max": 24.0, "step": 0.1})
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inputs["optional"]["use_other_vram"] = ("BOOLEAN", {"default": False})
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inputs["optional"]["expert_mode_allocations"] = ("STRING", {
|
|
"multiline": False,
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|
"default": "",
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|
"tooltip": "Expert use only: Manual VRAM allocation string. Incorrect values can cause crashes. Do not modify unless you fully understand DisTorch memory management."
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|
})
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|
return inputs
|
|
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CATEGORY = "multigpu"
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FUNCTION = "override"
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|
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def override(self, *args, device=None, expert_mode_allocations=None, use_other_vram=None, virtual_vram_gb=0.0, **kwargs):
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from . import set_current_text_encoder_device
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if device is not None:
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set_current_text_encoder_device(device)
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|
|
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register_patched_ggufmodelpatcher()
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|
fn = getattr(super(), cls.FUNCTION)
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|
out = fn(*args, **kwargs)
|
|
|
|
vram_string = ""
|
|
if virtual_vram_gb > 0:
|
|
if use_other_vram:
|
|
available_devices = [d for d in get_device_list() if d != "cpu"]
|
|
other_devices = [d for d in available_devices if d != device]
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|
other_devices.sort(key=lambda x: int(x.split(':')[1] if ':' in x else x[-1]), reverse=False)
|
|
device_string = ','.join(other_devices + ['cpu'])
|
|
vram_string = f"{device};{virtual_vram_gb};{device_string}"
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|
else:
|
|
vram_string = f"{device};{virtual_vram_gb};cpu"
|
|
|
|
full_allocation = f"{expert_mode_allocations}#{vram_string}" if expert_mode_allocations or vram_string else ""
|
|
|
|
logging.info(f"[MultiGPU_DisTorch] Full allocation string: {full_allocation}")
|
|
|
|
if hasattr(out[0], 'model'):
|
|
model_hash = create_model_hash(out[0], "override")
|
|
model_allocation_store[model_hash] = full_allocation
|
|
elif hasattr(out[0], 'patcher') and hasattr(out[0].patcher, 'model'):
|
|
model_hash = create_model_hash(out[0].patcher, "override")
|
|
model_allocation_store[model_hash] = full_allocation
|
|
|
|
return out
|
|
|
|
return NodeOverrideDisTorch
|
|
def override_class_with_distorch_clip_no_device(cls):
|
|
"""DisTorch wrapper for CLIP models with GGUF support"""
|
|
from . import current_text_encoder_device
|
|
|
|
class NodeOverrideDisTorchClipNoDevice(cls):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
inputs = copy.deepcopy(cls.INPUT_TYPES())
|
|
devices = get_device_list()
|
|
default_device = devices[1] if len(devices) > 1 else devices[0]
|
|
inputs["optional"] = inputs.get("optional", {})
|
|
inputs["optional"]["device"] = (devices, {"default": default_device})
|
|
inputs["optional"]["virtual_vram_gb"] = ("FLOAT", {"default": 4.0, "min": 0.0, "max": 24.0, "step": 0.1})
|
|
inputs["optional"]["use_other_vram"] = ("BOOLEAN", {"default": False})
|
|
inputs["optional"]["expert_mode_allocations"] = ("STRING", {
|
|
"multiline": False,
|
|
"default": "",
|
|
"tooltip": "Expert use only: Manual VRAM allocation string. Incorrect values can cause crashes. Do not modify unless you fully understand DisTorch memory management."
|
|
})
|
|
return inputs
|
|
|
|
CATEGORY = "multigpu"
|
|
FUNCTION = "override"
|
|
|
|
def override(self, *args, device=None, expert_mode_allocations=None, use_other_vram=None, virtual_vram_gb=0.0, **kwargs):
|
|
from . import set_current_text_encoder_device
|
|
if device is not None:
|
|
set_current_text_encoder_device(device)
|
|
|
|
register_patched_ggufmodelpatcher()
|
|
fn = getattr(super(), cls.FUNCTION)
|
|
out = fn(*args, **kwargs)
|
|
|
|
vram_string = ""
|
|
if virtual_vram_gb > 0:
|
|
if use_other_vram:
|
|
available_devices = [d for d in get_device_list() if d != "cpu"]
|
|
other_devices = [d for d in available_devices if d != device]
|
|
other_devices.sort(key=lambda x: int(x.split(':')[1] if ':' in x else x[-1]), reverse=False)
|
|
device_string = ','.join(other_devices + ['cpu'])
|
|
vram_string = f"{device};{virtual_vram_gb};{device_string}"
|
|
else:
|
|
vram_string = f"{device};{virtual_vram_gb};cpu"
|
|
|
|
full_allocation = f"{expert_mode_allocations}#{vram_string}" if expert_mode_allocations or vram_string else ""
|
|
|
|
logging.info(f"[MultiGPU_DisTorch] Full allocation string: {full_allocation}")
|
|
|
|
if hasattr(out[0], 'model'):
|
|
model_hash = create_model_hash(out[0], "override")
|
|
model_allocation_store[model_hash] = full_allocation
|
|
elif hasattr(out[0], 'patcher') and hasattr(out[0].patcher, 'model'):
|
|
model_hash = create_model_hash(out[0].patcher, "override")
|
|
model_allocation_store[model_hash] = full_allocation
|
|
|
|
return out
|
|
|
|
return NodeOverrideDisTorchClipNoDevice
|
|
|
|
# Alias for backward compatibility
|
|
override_class_with_distorch = override_class_with_distorch_gguf
|