""" DisTorch Safetensor Memory Management Module Contains all safetensor related code for distributed memory management """ import torch import logging import re from collections import defaultdict logger = logging.getLogger("MultiGPU") import comfy.model_management as mm import comfy.model_patcher from .device_utils import get_device_list from .model_management_mgpu import multigpu_memory_log def unpack_load_item(item): """Handle ComfyUI 0.6.0+ 5-tuple vs legacy 4-tuple""" if len(item) == 5: # (module_offload_mem, module_mem, module_name, module_object, params) return item[1], item[2], item[3], item[4] # (module_mem, module_name, module_object, params) return item[0], item[1], item[2], item[3] def register_patched_safetensor_modelpatcher(): """Register and patch the ModelPatcher for distributed safetensor loading""" # Patch ComfyUI's ModelPatcher if not hasattr(comfy.model_patcher.ModelPatcher, '_distorch_patched'): # PATCH load_models_gpu with correct memory calculations per model flags def patched_load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False): from comfy.model_management import cleanup_models_gc, get_free_memory, free_memory, current_loaded_models from comfy.model_management import VRAMState, vram_state, lowvram_available, MIN_WEIGHT_MEMORY_RATIO from comfy.model_management import minimum_inference_memory, extra_reserved_memory, is_device_cpu multigpu_memory_log("load_models_gpu_top_level", "start") cleanup_models_gc() inference_memory = minimum_inference_memory() extra_reserved_mem = extra_reserved_memory() memory_required_total = memory_required + extra_reserved_mem extra_mem = max(inference_memory, memory_required_total) if minimum_memory_required is None: minimum_memory_required = extra_mem else: minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_mem) models_temp = set() for m in models: models_temp.add(m) model_type = type(m).__name__ if ("GGUF" in model_type or "ModelPatcher" in model_type) and hasattr(m, "model_patches_to") and not hasattr(m, "model_patches_models"): logger.info(f"[MultiGPU DisTorch V2] {type(m).__name__} missing 'model_patches_models' attribute, using 'model_patches_to' fallback.") target_device = m.load_device logger.debug(f"[MultiGPU DisTorch V2] Target device: {target_device}") patches = m.model_patches_to(target_device) if patches: logger.debug(f"[MultiGPU DisTorch V2] Found {len(patches)} mm_patch(es) for {type(m).__name__} on device {target_device}") for mm_patch in patches: logger.debug(f"[MultiGPU DisTorch V2] Registering mm_patch: {type(mm_patch).__name__}") models_temp.add(mm_patch) continue for mm_patch in m.model_patches_models(): models_temp.add(mm_patch) patches = m.model_patches_to(m.load_device) if patches: for mm_patch in patches: models_temp.add(mm_patch) models = models_temp models_to_load = [] for x in models: loaded_model = mm.LoadedModel(x) try: loaded_model_index = current_loaded_models.index(loaded_model) except ValueError: loaded_model_index = None if loaded_model_index is not None: loaded = current_loaded_models[loaded_model_index] loaded.currently_used = True models_to_load.append(loaded) else: if hasattr(x, "model"): logging.info(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) for loaded_model in models_to_load: to_unload = [] for i, current_loaded_model in enumerate(current_loaded_models): if loaded_model.model.is_clone(current_loaded_model.model): to_unload = [i] + to_unload for i in to_unload: model_to_unload = current_loaded_models.pop(i) model_to_unload.model.detach(unpatch_all=False) model_to_unload.model_finalizer.detach() # DisTorch Processing total_memory_required = {} eject_device = None for loaded_model in models_to_load: device = loaded_model.device base_memory = loaded_model.model_memory_required(device) inner_model = loaded_model.model.model if hasattr(inner_model, '_distorch_v2_meta'): meta = inner_model._distorch_v2_meta allocation_str = meta['full_allocation'] # Parse allocation string: "expert#compute_device;virtual_vram_gb;donors" parts = allocation_str.split('#') virtual_vram_gb = 0.0 has_eject = False if len(parts) > 1: virtual_vram_str = parts[1] virtual_info = virtual_vram_str.split(';') if len(virtual_info) > 1: virtual_vram_gb = float(virtual_info[1]) if len(virtual_info) > 2 and virtual_info[2]: has_eject = True if has_eject: eject_device = device logger.mgpu_mm_log("DisTorch eject_models detected - MAX memory eviction") virtual_vram_bytes = virtual_vram_gb * (1024**3) adjusted_memory = max(0, base_memory - virtual_vram_bytes) total_memory_required[device] = total_memory_required.get(device, 0) + adjusted_memory logger.mgpu_mm_log(f"DisTorch model adjusted {(base_memory - virtual_vram_bytes)/(1024**3):.2f}GB for device {device}") else: # Standard model: use full model size total_memory_required[device] = total_memory_required.get(device, 0) + base_memory logger.mgpu_mm_log(f"[LOAD_MODELS_GPU] Standard model {(base_memory)/(1024**3):.2f}GB for device {device}") for device, device_memory in total_memory_required.items(): if device != torch.device("cpu"): requested_mem = device_memory * 1.1 + extra_mem logger.mgpu_mm_log(f"[FREE_MEMORY_CALL] Device {device}: requesting {requested_mem/(1024**3):.2f}GB = {device_memory/(1024**3):.2f}GB * 1.1 + {extra_mem/(1024**3):.2f}GB inference") multigpu_memory_log("free_memory", "pre") for device, device_memory in total_memory_required.items(): if device != torch.device("cpu"): if device == eject_device: total_device_memory = mm.get_total_memory(device) logger.mgpu_mm_log(f"[LOAD_MODELS_GPU] eject_models=1, is_distorch=1 → using MAX memory ({total_device_memory/(1024**3):.2f}GB) for eviction") free_memory(total_device_memory,device) else: logger.mgpu_mm_log(f"[LOAD_MODELS_GPU] eject_models=0, using Comfy Core Computed memory ({(device_memory * 1.1 + extra_mem)/(1024**3):.2f}GB) for eviction") free_memory(device_memory * 1.1 + extra_mem, device) multigpu_memory_log("free_memory/minimum_memory_required", "post/pre") for device in total_memory_required: if device != torch.device("cpu"): free_mem = get_free_memory(device) free_mem_gb = free_mem / (1024**3) min_required_gb = minimum_memory_required / (1024**3) logger.mgpu_mm_log(f"[MIN_MEMORY_CHECK] Device {device}: free={free_mem_gb:.2f}GB, required={min_required_gb:.2f}GB, will_evict={free_mem < minimum_memory_required}") if free_mem < minimum_memory_required: models_l = free_memory(minimum_memory_required, device) logger.mgpu_mm_log(f"[EVICTION] Device {device}: unloaded {len(models_l)} models due to insufficient memory") logging.info(f"{len(models_l)} models unloaded.") multigpu_memory_log("minimum_memory_required", "post") for loaded_model in models_to_load: model = loaded_model.model torch_dev = model.load_device if is_device_cpu(torch_dev): vram_set_state = VRAMState.DISABLED else: vram_set_state = vram_state lowvram_model_memory = 0 if lowvram_available and vram_set_state in (VRAMState.LOW_VRAM, VRAMState.NORMAL_VRAM) and not force_full_load: loaded_memory = loaded_model.model_loaded_memory() current_free_mem = get_free_memory(torch_dev) + loaded_memory lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory) if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 0.1 loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) current_loaded_models.insert(0, loaded_model) # Replace the module function mm.load_models_gpu = patched_load_models_gpu original_partially_load = comfy.model_patcher.ModelPatcher.partially_load def new_partially_load(self, device_to, extra_memory=0, full_load=False, force_patch_weights=False, **kwargs): """Override to use direct model annotation for allocation""" mp_id = id(self) inner_model = self.model inner_model_id = id(inner_model) if not hasattr(inner_model, "_distorch_v2_meta"): logger.debug(f"[DISTORCH_SKIP] ModelPatcher=0x{mp_id:x} inner_model=0x{inner_model_id:x} type={type(inner_model).__name__} - no metadata, using standard loading") result = original_partially_load(self, device_to, extra_memory, force_patch_weights) if hasattr(self, '_distorch_block_assignments'): del self._distorch_block_assignments return result allocations = inner_model._distorch_v2_meta['full_allocation'] if not hasattr(self.model, '_distorch_high_precision_loras'): self.model._distorch_high_precision_loras = True if not hasattr(self.model, 'current_weight_patches_uuid'): self.model.current_weight_patches_uuid = None unpatch_weights = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid or force_patch_weights) if unpatch_weights: logger.debug("[MultiGPU DisTorch V2] Patches changed or forced. Unpatching model.") self.unpatch_model(self.offload_device, unpatch_weights=True) self.patch_model(load_weights=False) mem_counter = 0 is_clip_model = getattr(self, 'is_clip', False) ## TODO - I do not believe this code is needed and needs to be flagged for proof it is needed # Check for valid cache allocations_match = hasattr(self, '_distorch_last_allocations') and self._distorch_last_allocations == allocations cache_exists = hasattr(self, '_distorch_cached_assignments') if cache_exists and allocations_match and not unpatch_weights and not force_patch_weights: device_assignments = self._distorch_cached_assignments logger.debug(f"[MultiGPU DisTorch V2] Reusing cached analysis for {type(inner_model).__name__}") else: device_assignments = analyze_safetensor_loading(self, allocations, is_clip=is_clip_model) ## This should be the only required line - that is how it worked previous release so if it doesn't it is Comfy changes self._distorch_cached_assignments = device_assignments self._distorch_last_allocations = allocations model_original_dtype = comfy.utils.weight_dtype(self.model.state_dict()) high_precision_loras = getattr(self.model, "_distorch_high_precision_loras", True) # Use standard ComfyUI load list - the device comparison fix ensures we don't crash loading = self._load_list() loading.sort(reverse=True) for item in loading: module_size, module_name, module_object, params = unpack_load_item(item) if not unpatch_weights and hasattr(module_object, "comfy_patched_weights") and module_object.comfy_patched_weights is True: block_target_device = device_assignments['block_assignments'].get(module_name, device_to) current_module_device = None try: if any(p.numel() > 0 for p in module_object.parameters(recurse=False)): current_module_device = next(module_object.parameters(recurse=False)).device except StopIteration: pass if current_module_device is not None and str(current_module_device) != str(block_target_device): logger.debug(f"[MultiGPU DisTorch V2] Moving already patched {module_name} to {block_target_device}") module_object.to(block_target_device) mem_counter += module_size continue # Step 1: Write block/tensor to compute device first module_object.to(device_to) # Step 2: Apply LoRa patches while on compute device weight_key = f"{module_name}.weight" bias_key = f"{module_name}.bias" if weight_key in self.patches: self.patch_weight_to_device(weight_key, device_to=device_to) if weight_key in self.weight_wrapper_patches: module_object.weight_function.extend(self.weight_wrapper_patches[weight_key]) if bias_key in self.patches: self.patch_weight_to_device(bias_key, device_to=device_to) if bias_key in self.weight_wrapper_patches: module_object.bias_function.extend(self.weight_wrapper_patches[bias_key]) # Step 3: FP8 casting for CPU storage (if enabled) block_target_device = device_assignments['block_assignments'].get(module_name, device_to) has_patches = weight_key in self.patches or bias_key in self.patches if not high_precision_loras and block_target_device == "cpu" and has_patches and model_original_dtype in [torch.float8_e4m3fn, torch.float8_e5m2]: for param_name, param in module_object.named_parameters(): if param.dtype.is_floating_point: cast_data = comfy.float.stochastic_rounding(param.data, torch.float8_e4m3fn) new_param = torch.nn.Parameter(cast_data.to(torch.float8_e4m3fn)) new_param.requires_grad = param.requires_grad setattr(module_object, param_name, new_param) logger.debug(f"[MultiGPU DisTorch V2] Cast {module_name}.{param_name} to FP8 for CPU storage") # Step 4: Move to ultimate destination based on DisTorch assignment if str(block_target_device) != str(device_to): logger.debug(f"[MultiGPU DisTorch V2] Moving {module_name} from {device_to} to {block_target_device}") module_object.to(block_target_device) module_object.comfy_cast_weights = True # Mark as patched and update memory counter module_object.comfy_patched_weights = True mem_counter += module_size self.model.current_weight_patches_uuid = self.patches_uuid self.model.device = device_to logger.info("[MultiGPU DisTorch V2] DisTorch loading completed.") logger.info(f"[MultiGPU DisTorch V2] Total memory: {mem_counter / (1024 * 1024):.2f}MB") return 0 comfy.model_patcher.ModelPatcher.partially_load = new_partially_load comfy.model_patcher.ModelPatcher._distorch_patched = True logger.info("[MultiGPU Core Patching] Successfully patched ModelPatcher.partially_load") def _extract_clip_head_blocks(raw_block_list, compute_device): """Identify and pre-assign CLIP head blocks to compute device returning head_blocks, distributable_blocks, block_assignments, and head_memory.""" head_keywords = ['embed', 'wte', 'wpe', 'token_embedding', 'position_embedding'] head_blocks = [] distributable_blocks = [] head_memory = 0 block_assignments = {} block_assignments = {} for item in raw_block_list: module_size, module_name, module_object, params = unpack_load_item(item) if any(kw in module_name.lower() for kw in head_keywords): head_blocks.append((module_size, module_name, module_object, params)) block_assignments[module_name] = compute_device head_memory += module_size else: distributable_blocks.append((module_size, module_name, module_object, params)) return head_blocks, distributable_blocks, block_assignments, head_memory def analyze_safetensor_loading(model_patcher, allocations_string, is_clip=False): """ Analyze and distribute safetensor model blocks across devices. Supports CLIP head preservation when is_clip=True. """ DEVICE_RATIOS_DISTORCH = {} device_table = {} distorch_alloc = "" virtual_vram_str = "" if '#' in allocations_string: distorch_alloc, virtual_vram_str = allocations_string.split('#', 1) else: distorch_alloc = allocations_string compute_device = virtual_vram_str.split(';')[0] if virtual_vram_str else "cuda:0" logger.debug(f"[MultiGPU DisTorch V2] Compute Device: {compute_device}") if not distorch_alloc: distorch_alloc = calculate_safetensor_vvram_allocation(model_patcher, virtual_vram_str) elif any(c in distorch_alloc.lower() for c in ['g', 'm', 'k', 'b']): distorch_alloc = calculate_fraction_from_byte_expert_string(model_patcher, distorch_alloc) elif "%" in distorch_alloc: distorch_alloc = calculate_fraction_from_ratio_expert_string(model_patcher, distorch_alloc) all_devices = get_device_list() present_devices = {item.split(',')[0] for item in distorch_alloc.split(';') if ',' in item} for device in all_devices: if device not in present_devices: distorch_alloc += f";{device},0.0" eq_line = "=" * 50 dash_line = "-" * 50 fmt_assign = "{:<18}{:>7}{:>14}{:>10}" logger.info(eq_line) logger.info(f"[MultiGPU DisTorch V2] Final Allocation String:\n{distorch_alloc}") for allocation in distorch_alloc.split(';'): if ',' not in allocation: continue dev_name, fraction = allocation.split(',') fraction = float(fraction) total_mem_bytes = mm.get_total_memory(torch.device(dev_name)) alloc_gb = (total_mem_bytes * fraction) / (1024**3) DEVICE_RATIOS_DISTORCH[dev_name] = alloc_gb device_table[dev_name] = { "fraction": fraction, "total_gb": total_mem_bytes / (1024**3), "alloc_gb": alloc_gb } logger.info(eq_line) logger.info(" DisTorch2 Model Device Allocations") logger.info(eq_line) fmt_rosetta = "{:<8}{:>9}{:>9}{:>11}{:>10}" logger.info(fmt_rosetta.format("Device", "VRAM GB", "Dev %", "Model GB", "Dist %")) logger.info(dash_line) sorted_devices = sorted(device_table.keys(), key=lambda d: (d == "cpu", d)) total_allocated_model_bytes = sum(d["alloc_gb"] * (1024**3) for d in device_table.values()) for dev in sorted_devices: total_dev_gb = device_table[dev]["total_gb"] alloc_fraction = device_table[dev]["fraction"] alloc_gb = device_table[dev]["alloc_gb"] dist_ratio_percent = (alloc_gb * (1024**3) / total_allocated_model_bytes) * 100 if total_allocated_model_bytes > 0 else 0 logger.info(fmt_rosetta.format( dev, f"{total_dev_gb:.2f}", f"{alloc_fraction*100:.1f}%", f"{alloc_gb:.2f}", f"{dist_ratio_percent:.1f}%" )) logger.info(dash_line) block_summary = {} block_list = [] memory_by_type = defaultdict(int) total_memory = 0 raw_block_list = model_patcher._load_list() total_memory = sum(unpack_load_item(x)[0] for x in raw_block_list) MIN_BLOCK_THRESHOLD = total_memory * 0.0001 logger.debug(f"[MultiGPU DisTorch V2] Total model memory: {total_memory} bytes") logger.debug(f"[MultiGPU DisTorch V2] Tiny block threshold (0.01%): {MIN_BLOCK_THRESHOLD} bytes") # CLIP-specific: Extract head blocks and get pre-assignments head_memory = 0 block_assignments = {} if is_clip: head_blocks, distributable_raw, block_assignments, head_memory = \ _extract_clip_head_blocks(raw_block_list, compute_device) logger.info(f"[MultiGPU DisTorch V2 CLIP] Preserving {len(head_blocks)} head layer(s) ({head_memory/(1024**2):.2f} MB) on compute device: {compute_device}") else: distributable_raw = raw_block_list # Build all_blocks list for summary (using full raw_block_list) all_blocks = [] for item in raw_block_list: module_size, module_name, module_object, params = unpack_load_item(item) block_type = type(module_object).__name__ # Populate summary dictionaries block_summary[block_type] = block_summary.get(block_type, 0) + 1 memory_by_type[block_type] += module_size all_blocks.append((module_name, module_object, block_type, module_size)) # Use distributable blocks for actual allocation (for CLIP, this excludes heads) distributable_all_blocks = [] for item in distributable_raw: module_size, module_name, module_object, params = unpack_load_item(item) distributable_all_blocks.append((module_name, module_object, type(module_object).__name__, module_size)) block_list = [b for b in distributable_all_blocks if (b[3] >= MIN_BLOCK_THRESHOLD and hasattr(b[1], "bias"))] tiny_block_list = [b for b in distributable_all_blocks if b not in block_list] logger.debug(f"[MultiGPU DisTorch V2] Total blocks: {len(all_blocks)}") logger.debug(f"[MultiGPU DisTorch V2] Distributable blocks: {len(block_list)}") logger.debug(f"[MultiGPU DisTorch V2] Tiny blocks (<0.01%): {len(tiny_block_list)}") logger.info(" DisTorch2 Model Layer Distribution") logger.info(dash_line) fmt_layer = "{:<18}{:>7}{:>14}{:>10}" logger.info(fmt_layer.format("Layer Type", "Layers", "Memory (MB)", "% Total")) logger.info(dash_line) for layer_type, count in block_summary.items(): mem_mb = memory_by_type[layer_type] / (1024 * 1024) mem_percent = (memory_by_type[layer_type] / total_memory) * 100 if total_memory > 0 else 0 logger.info(fmt_layer.format(layer_type[:18], str(count), f"{mem_mb:.2f}", f"{mem_percent:.1f}%")) logger.info(dash_line) # Distribute blocks sequentially from the tail of the model device_assignments = {device: [] for device in DEVICE_RATIOS_DISTORCH} # Create a memory quota for each donor device based on its calculated allocation. donor_devices = list(sorted_devices) donor_quotas = { dev: device_table[dev]["alloc_gb"] * (1024**3) for dev in donor_devices } # CLIP-specific: Adjust compute_device quota to account for locked head blocks if is_clip and compute_device in donor_quotas and head_memory > 0: donor_quotas[compute_device] = max(0, donor_quotas[compute_device] - head_memory) logger.debug(f"[MultiGPU DisTorch V2 CLIP] Adjusted {compute_device} quota by -{head_memory/(1024**2):.2f} MB for head preservation") # Iterate from the TAIL of the model, assigning blocks to donors until their quotas are filled. for block_name, module, block_type, block_memory in reversed(block_list): assigned_to_donor = False for donor in donor_devices: if donor_quotas[donor] >= block_memory: block_assignments[block_name] = donor donor_quotas[donor] -= block_memory assigned_to_donor = True break # Move to the next block if not assigned_to_donor: #Note - small rounding errors and tensor-fitting on devices make a block occasionally an orphan. We treat orphans the same as tiny_block_list as they are generally small rounding errors block_assignments[block_name] = compute_device if tiny_block_list: for block_name, module, block_type, block_memory in tiny_block_list: block_assignments[block_name] = compute_device # Populate device_assignments from the final block_assignments for block_name, device in block_assignments.items(): # Find the block in the original list to get all its info for b_name, b_module, b_type, b_mem in all_blocks: if b_name == block_name: device_assignments[device].append((b_name, b_module, b_type, b_mem)) break logger.info("DisTorch2 Model Final Device/Layer Assignments") logger.info(dash_line) logger.info(fmt_assign.format("Device", "Layers", "Memory (MB)", "% Total")) logger.info(dash_line) if tiny_block_list: tiny_block_memory = sum(b[3] for b in tiny_block_list) tiny_mem_mb = tiny_block_memory / (1024 * 1024) tiny_mem_percent = (tiny_block_memory / total_memory) * 100 if total_memory > 0 else 0 device_label = f"{compute_device} (<0.01%)" logger.info(fmt_assign.format(device_label, str(len(tiny_block_list)), f"{tiny_mem_mb:.2f}", f"{tiny_mem_percent:.1f}%")) logger.debug(f"[MultiGPU DisTorch V2] Tiny block memory breakdown: {tiny_block_memory} bytes ({tiny_mem_mb:.2f} MB), which is {tiny_mem_percent:.4f}% of total model memory.") total_assigned_memory = 0 device_memories = {} for device, blocks in device_assignments.items(): dist_blocks = [b for b in blocks if b[3] >= MIN_BLOCK_THRESHOLD] if not dist_blocks: continue device_memory = sum(b[3] for b in dist_blocks) device_memories[device] = device_memory total_assigned_memory += device_memory sorted_assignments = sorted(device_memories.keys(), key=lambda d: (d == "cpu", d)) for dev in sorted_assignments: # Get only the distributed blocks for the count dist_blocks = [b for b in device_assignments[dev] if b[3] >= MIN_BLOCK_THRESHOLD] if not dist_blocks: continue mem_mb = device_memories[dev] / (1024 * 1024) mem_percent = (device_memories[dev] / total_memory) * 100 if total_memory > 0 else 0 logger.info(fmt_assign.format(dev, str(len(dist_blocks)), f"{mem_mb:.2f}", f"{mem_percent:.1f}%")) logger.info(dash_line) return { "device_assignments": device_assignments, "block_assignments": block_assignments } def parse_memory_string(mem_str): """Parses a memory string (e.g., '4.0g', '512M') and returns bytes.""" mem_str = mem_str.strip().lower() match = re.match(r'(\d+\.?\d*)\s*([gmkb]?)', mem_str) if not match: raise ValueError(f"Invalid memory string format: {mem_str}") val, unit = match.groups() val = float(val) if unit == 'g': return val * (1024**3) elif unit == 'm': return val * (1024**2) elif unit == 'k': return val * 1024 else: # b or no unit return val def calculate_fraction_from_byte_expert_string(model_patcher, byte_str): """Convert byte allocation string (e.g. 'cuda:1,4gb;cpu,*') to fractional VRAM allocation string respecting device order and byte quotas.""" raw_block_list = model_patcher._load_list() total_model_memory = sum(unpack_load_item(x)[0] for x in raw_block_list) remaining_model_bytes = total_model_memory # Use a list of tuples to preserve the user-defined order parsed_allocations = [] wildcard_device = "cpu" # Default wildcard device for allocation in byte_str.split(';'): if ',' not in allocation: continue dev_name, val_str = allocation.split(',', 1) is_wildcard = '*' in val_str if is_wildcard: wildcard_device = dev_name # Don't add wildcard to the priority list yet else: byte_val = parse_memory_string(val_str) parsed_allocations.append({'device': dev_name, 'bytes': byte_val}) final_byte_allocations = defaultdict(int) # Process devices with specific byte allocations first, in order for alloc in parsed_allocations: dev = alloc['device'] requested_bytes = alloc['bytes'] # Determine the actual bytes to allocate to this device bytes_to_assign = min(requested_bytes, remaining_model_bytes) if bytes_to_assign > 0: final_byte_allocations[dev] = bytes_to_assign remaining_model_bytes -= bytes_to_assign logger.info(f"[MultiGPU DisTorch V2] Assigning {bytes_to_assign / (1024**2):.2f}MB of model to {dev} (requested {requested_bytes / (1024**2):.2f}MB).") if remaining_model_bytes <= 0: logger.info("[MultiGPU DisTorch V2] All model blocks have been allocated. Subsequent devices in the string will receive no assignment.") break # Assign any leftover model bytes to the wildcard device if remaining_model_bytes > 0: final_byte_allocations[wildcard_device] += remaining_model_bytes logger.info(f"[MultiGPU DisTorch V2] Assigning remaining {remaining_model_bytes / (1024**2):.2f}MB of model to wildcard device '{wildcard_device}'.") # Convert the final byte allocations to VRAM fractions allocation_parts = [] for dev, bytes_alloc in final_byte_allocations.items(): total_device_vram = mm.get_total_memory(torch.device(dev)) if total_device_vram > 0: fraction = bytes_alloc / total_device_vram allocation_parts.append(f"{dev},{fraction:.4f}") allocations_string = ";".join(allocation_parts) return allocations_string def calculate_fraction_from_ratio_expert_string(model_patcher, ratio_str): """Convert ratio allocation string (e.g. 'cuda:0,25%;cpu,75%') describing model split to fractional VRAM allocation string.""" raw_block_list = model_patcher._load_list() total_model_memory = sum(unpack_load_item(x)[0] for x in raw_block_list) raw_ratios = {} for allocation in ratio_str.split(';'): if ',' not in allocation: continue dev_name, val_str = allocation.split(',', 1) # Assumes the value is a unitless ratio number, ignores '%' for simplicity. value = float(val_str.replace('%','').strip()) raw_ratios[dev_name] = value total_ratio_parts = sum(raw_ratios.values()) allocation_parts = [] for dev, ratio_val in raw_ratios.items(): bytes_of_model_for_device = (ratio_val / total_ratio_parts) * total_model_memory total_vram_of_device = mm.get_total_memory(torch.device(dev)) if total_vram_of_device > 0: required_fraction = bytes_of_model_for_device / total_vram_of_device allocation_parts.append(f"{dev},{required_fraction:.4f}") ratio_values = [str(v) for v in raw_ratios.values()] ratio_string = ":".join(ratio_values) normalized_pcts = [(v / total_ratio_parts) * 100 for v in raw_ratios.values()] put_parts = [] for i, dev_name in enumerate(raw_ratios.keys()): put_parts.append(f"{int(normalized_pcts[i])}% on {dev_name}") if len(put_parts) == 1: put_part = put_parts[0] elif len(put_parts) == 2: put_part = f"{put_parts[0]} and {put_parts[1]}" else: put_part = ", ".join(put_parts[:-1]) + f", and {put_parts[-1]}" logger.info(f"[MultiGPU DisTorch V2] Ratio(%) Mode - {ratio_str} -> {ratio_string} ratio, put {put_part}") allocations_string = ";".join(allocation_parts) return allocations_string def calculate_safetensor_vvram_allocation(model_patcher, virtual_vram_str): """Calculate virtual VRAM allocation string for distributed safetensor loading""" recipient_device, vram_amount, donors = virtual_vram_str.split(';') virtual_vram_gb = float(vram_amount) eq_line = "=" * 47 dash_line = "-" * 47 fmt_assign = "{:<8} {:<6} {:>11} {:>9} {:>9}" logger.info(eq_line) logger.info(" DisTorch2 Model Virtual VRAM Analysis") logger.info(eq_line) logger.info(fmt_assign.format("Object", "Role", "Original(GB)", "Total(GB)", "Virt(GB)")) logger.info(dash_line) # Calculate recipient VRAM recipient_vram = mm.get_total_memory(torch.device(recipient_device)) / (1024**3) recipient_virtual = recipient_vram + virtual_vram_gb logger.info(fmt_assign.format(recipient_device, 'recip', f"{recipient_vram:.2f}GB",f"{recipient_virtual:.2f}GB", f"+{virtual_vram_gb:.2f}GB")) # Handle donor devices ram_donors = list(donors.split(',')) remaining_vram_needed = virtual_vram_gb donor_device_info = {} donor_allocations = {} for donor in ram_donors: donor_vram = mm.get_total_memory(torch.device(donor)) / (1024**3) max_donor_capacity = donor_vram donation = min(remaining_vram_needed, max_donor_capacity) donor_virtual = donor_vram - donation remaining_vram_needed -= donation donor_allocations[donor] = donation donor_device_info[donor] = (donor_vram, donor_virtual) logger.info(fmt_assign.format(donor, 'donor', f"{donor_vram:.2f}GB", f"{donor_virtual:.2f}GB", f"-{donation:.2f}GB")) logger.info(dash_line) # Calculate model size model = model_patcher.model if hasattr(model_patcher, 'model') else model_patcher total_memory = 0 for name, module in model.named_modules(): if hasattr(module, "weight"): if module.weight is not None: total_memory += module.weight.numel() * module.weight.element_size() if hasattr(module, "bias") and module.bias is not None: total_memory += module.bias.numel() * module.bias.element_size() model_size_gb = total_memory / (1024**3) new_model_size_gb = max(0, model_size_gb - virtual_vram_gb) 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")) # Warning if model too large if model_size_gb > (recipient_vram * 0.9): required_offload_gb = model_size_gb - (recipient_vram * 0.9) logger.warning(f"\n\n[MultiGPU DisTorch V2] Model size ({model_size_gb:.2f}GB) is larger than 90% of available VRAM on: {recipient_device} ({recipient_vram * 0.9:.2f}GB).") logger.warning(f"[MultiGPU DisTorch V2] To prevent an OOM error, set 'virtual_vram_gb' to at least {required_offload_gb:.2f}.\n\n") new_on_recipient = max(0, model_size_gb - virtual_vram_gb) # Build allocation string allocation_parts = [] recipient_percent = new_on_recipient / recipient_vram allocation_parts.append(f"{recipient_device},{recipient_percent:.4f}") for donor in ram_donors: donor_vram = donor_device_info[donor][0] donor_percent = donor_allocations[donor] / donor_vram allocation_parts.append(f"{donor},{donor_percent:.4f}") allocations_string = ";".join(allocation_parts) return allocations_string