284 lines
14 KiB
Python
284 lines
14 KiB
Python
import sys
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import logging
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import torch
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from collections import defaultdict
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import comfy.model_management
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import copy
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model_allocation_store = {}
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def create_model_hash(model, caller):
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import hashlib
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logging.info(f"\nMultiGPU: Hash creation from {caller}")
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# Model type, size, and first 3 layers as identifier
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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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logging.info(f"MultiGPU: Type: {model_type}")
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logging.info(f"MultiGPU: Size: {model_size}")
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logging.info(f"MultiGPU: Layer sample: {first_layers}")
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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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logging.info(f"MultiGPU: Hash: {final_hash}")
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return final_hash
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# Add to both places as specified
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def register_patched_ggufmodelpatcher(): # Removed node_instance parameter
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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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logging.info("MultiGPU: GGUFDisTorch - GGUF ModelPatcher not yet patched, applying patch")
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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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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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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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#logging.info(f"MultiGPU: GGUFDisTorch - Weight Module {n} on device {device}, offload_device is {self.offload_device}")
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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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#logging.info(f"MultiGPU: GGUFDisTorch - Bias Module {n} on device {device}, offload_device is {self.offload_device}")
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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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#logging.info(f"MultiGPU: GGUFDisTorch - Found {len(linked)} linked modules out of {module_count} total modules")
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if linked:
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#logging.info(f"MultiGPU: GGUFDisTorch - Found {len(linked)} linked modules, computing reallocation")
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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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logging.info(f"MultiGPU: Hash lookup - Found allocations: {debug_allocations}")
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logging.info(f"MultiGPU: LOOKUP - Hash {debug_hash}")
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if debug_allocations: # No need for else case as per instruction
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logging.info(f"MultiGPU: FOUND - Hash matches, using allocations: {debug_allocations}")
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device_assignments = analyze_ggml_loading(self.model, debug_allocations)['device_assignments'] # Use debug_allocations
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for device, layers in device_assignments.items():
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#logging.info(f"MultiGPU: GGUFDisTorch - Moving {len(layers)} layers to {device}")
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target_device = torch.device(device)
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#logging.info(f"MultiGPU: GGUFDisTorch - Moving {len(layers)} layers to {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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logging.info("MultiGPU: GGUFDisTorch - 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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logging.info("MultiGPU: GGUFDisTorch - Successfully patched GGUF ModelPatcher")
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else:
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logging.info("MultiGPU: GGUFDisTorch - GGUF ModelPatcher already patched")
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def analyze_ggml_loading(model, distorch_allocations): # Removed node_instance parameter
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DEVICE_RATIOS_DISTORCH = {}
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device_table = {}
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primary_dev_name = distorch_allocations.get("compute_device")
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primary_total_mem_bytes = comfy.model_management.get_total_memory(torch.device(primary_dev_name))
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primary_fraction = distorch_allocations.get("compute_device_alloc", 0.0)
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primary_alloc_gb = (primary_total_mem_bytes * primary_fraction) / (1024**3)
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DEVICE_RATIOS_DISTORCH[primary_dev_name] = primary_alloc_gb
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device_table[primary_dev_name] = {"fraction": primary_fraction,"total_gb": primary_total_mem_bytes / (1024**3),"alloc_gb": primary_alloc_gb}
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i = 1
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while f"distorch{i}_device" in distorch_allocations:
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dev_key = f"distorch{i}_device"
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alloc_key = f"distorch{i}_alloc"
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dev_name = distorch_allocations[dev_key]
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dev_total_mem_bytes = comfy.model_management.get_total_memory(torch.device(dev_name))
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dev_fraction = distorch_allocations.get(alloc_key, 0.0)
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dev_alloc_gb = (dev_total_mem_bytes * dev_fraction) / (1024**3)
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DEVICE_RATIOS_DISTORCH[dev_name] = dev_alloc_gb
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device_table[dev_name] = {"fraction": dev_fraction,"total_gb": dev_total_mem_bytes / (1024**3),"alloc_gb": dev_alloc_gb}
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i += 1
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cpu_dev_name = distorch_allocations.get("distorch_cpu", "cpu")
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cpu_total_mem_bytes = comfy.model_management.get_total_memory(torch.device(cpu_dev_name))
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cpu_fraction = distorch_allocations.get("distorch_cpu_alloc", 0.0)
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cpu_alloc_gb = (cpu_total_mem_bytes * cpu_fraction) / (1024**3)
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DEVICE_RATIOS_DISTORCH[cpu_dev_name] = cpu_alloc_gb
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device_table[cpu_dev_name] = {"fraction": cpu_fraction,"total_gb": cpu_total_mem_bytes / (1024**3),"alloc_gb": cpu_alloc_gb}
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eq_line = "=" * 47
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dash_line = "-" * 47
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fmt_alloc = "{:<12}{:>10}{:>14}{:>10}"
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.info(eq_line)
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logging.info(" DisTorch Analysis")
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logging.info(eq_line)
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logging.info(dash_line)
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logging.info(" DisTorch Device Allocations")
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logging.info(dash_line)
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logging.info(fmt_alloc.format("Device", "Alloc %", "Total (GB)", " Alloc (GB)"))
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logging.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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logging.info(fmt_alloc.format(dev,f"{int(frac * 100)}%",f"{tot_gb:.2f}",f"{alloc_gb:.2f}"))
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logging.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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logging.info(" DisTorch GGML Layer Distribution")
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logging.info(dash_line)
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fmt_layer = "{:<12}{:>10}{:>14}{:>10}"
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logging.info(fmt_layer.format("Layer Type", "Layers", "Memory (MB)", "% Total"))
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logging.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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logging.info(fmt_layer.format(layer_type,str(count),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
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logging.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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logging.info(" DisTorch Final Device/Layer Assignments")
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logging.info(dash_line)
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fmt_assign = "{:<12}{:>10}{:>14}{:>10}"
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logging.info(fmt_assign.format("Device", "Layers", "Memory (MB)", "% Total"))
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logging.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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logging.info(fmt_assign.format(dev,str(len(layers)),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
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logging.info(dash_line)
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return {"device_assignments": device_assignments}
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def override_class_with_distorch(cls):
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from . import register_patched_ggufmodelpatcher # Removed node_instance import
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from . import get_device_list
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import copy
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import logging
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class NodeOverrideDisTorch(cls):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# self.distorch_allocations = {} # No longer needed here
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self.distorch_compute_device = None
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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 = [d for d in get_device_list() if d != "cpu"]
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inputs["optional"] = inputs.get("optional", {})
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inputs["required"]["compute_device"] = (devices, {"default": devices[0], "tooltip": "Device model will use for computation"})
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inputs["required"]["compute_device_alloc"] = ("FLOAT", {"default": 0.15, "step": 0.01, "tooltip": "Fraction of memory NOT allocated to active latent space computation, recommended <= 15%"})
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for i in range(len(devices) - 1):
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inputs["optional"][f"distorch{i+1}_device"] = (devices, {"default": devices[i+1], "tooltip": f"Device for distorch{i+1} model layer VRAM allocation"})
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inputs["optional"][f"distorch{i+1}_alloc"] = ("FLOAT", {"default": 0.9, "step": 0.01, "tooltip": f"Fraction of memory allocated to distorch{i+1} model layer, recommended >= 90%"})
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inputs["optional"]["distorch_cpu"] = (["cpu"], {"default": "cpu", "tooltip": "Device for distorch CPU memory allocation"})
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inputs["optional"]["distorch_cpu_alloc"] = ("FLOAT", {"default": 0.0, "step": 0.01, "tooltip": "Fraction of memory allocated to distorch CPU memory (potentially slower than cuda)"})
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return inputs
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CATEGORY = "multigpu"
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FUNCTION = "override"
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def override(self, *args, **kwargs):
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global current_device, model_allocation_store
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# self.distorch_allocations = {} # No longer needed here
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distorch_compute_device = kwargs.get("compute_device", None)
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if distorch_compute_device is not None:
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current_device = distorch_compute_device
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register_patched_ggufmodelpatcher() # Removed node_instance argument
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allocation_params = {}
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keys_to_remove = list(kwargs.keys())
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for key in keys_to_remove:
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if key not in {"unet_name", "clip_name1", "clip_name2", "clip_name2", "type"}:
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value = kwargs.pop(key)
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logging.info(f"MultiGPU: Removing {key} from kwargs")
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logging.info(f"MultiGPU: Value: {value}")
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allocation_params[key] = value
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fn = getattr(super(), cls.FUNCTION)
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model = fn(*args, **kwargs)
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if hasattr(model[0], 'model'):
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model_hash = create_model_hash(model[0], "override")
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model_allocation_store[model_hash] = allocation_params.copy()
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logging.info(f"MultiGPU: STORE - Hash {model_hash}, Allocations: {model_allocation_store[model_hash]}")
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elif hasattr(model[0], 'patcher') and hasattr(model[0].patcher, 'model'):
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model_hash = create_model_hash(model[0].patcher, "override")
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model_allocation_store[model_hash] = allocation_params.copy()
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logging.info(f"MultiGPU: STORE - Hash {model_hash}, Allocations: {model_allocation_store[model_hash]}")
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return model
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return NodeOverrideDisTorch |