Eliminate unused safetensor loading analysis method and update example configurations, adding one with LoRAs as one of the tested configurations to avoid the issue seen during initial release.
This commit is contained in:
John Pollock
-147
@@ -377,152 +377,6 @@ def analyze_safetensor_loading(model_patcher, allocations_str):
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"device_assignments": device_assignments,
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"block_assignments": block_assignments
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}
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def analyze_safetensor_loading_comfy(model_patcher, allocations_str):
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"""
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Analyze and distribute safetensor model blocks across devices utilizing model_patcher._load_list().sort(reverse=True) method like Comfy
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"""
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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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# Parse allocation string EXACTLY like GGML
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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_safetensor_vvram_allocation(model_patcher, virtual_vram_str)
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# EXACT SAME FORMATTING AS GGML
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eq_line = "=" * 50
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dash_line = "-" * 50
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fmt_assign = "{:<18}{:>7}{:>14}{:>10}"
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# Parse device allocations
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for allocation in distorch_alloc.split(';'):
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if ',' not in allocation:
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continue
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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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# IDENTICAL LOGGING TO DISTORCH
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logger.info(eq_line)
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logger.info(" DisTorch2 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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# Get the model blocks using ComfyUI's method
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block_list = model_patcher._load_list()
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block_list.sort(reverse=True)
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# Log layer distribution
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total_memory = sum(b[0] for b in block_list)
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memory_by_type = defaultdict(int)
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block_summary = defaultdict(int)
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for module_size, module_name, module_object, params in block_list:
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block_type = module_object.__class__.__name__
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block_summary[block_type] += 1
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memory_by_type[block_type] += module_size
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# Log layer distribution - IDENTICAL FORMAT TO GGML
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logger.info(" DisTorch2 Model Layer Distribution")
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logger.info(dash_line)
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fmt_layer = "{:<18}{:>7}{:>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 block_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[:18], str(count), f"{mem_mb:.2f}", f"{mem_percent:.1f}%"))
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logger.info(dash_line)
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# Distribute blocks sequentially
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device_assignments = {device: [] for device in DEVICE_RATIOS_DISTORCH.keys()}
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block_assignments = {}
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compute_device = str(current_device)
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# Calculate total memory to be offloaded to donor devices
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total_offload_gb = sum(DEVICE_RATIOS_DISTORCH.get(d, 0) for d in sorted_devices if d != compute_device)
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total_offload_bytes = total_offload_gb * (1024**3)
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offloaded_bytes = 0
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# Iterate through the sorted list (largest blocks first)
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for module_size, module_name, module_object, params in block_list:
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# Assign to donor device until target is met
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if offloaded_bytes < total_offload_bytes:
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# For now, simple offload to CPU, will expand for multi-donor
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donor_device = "cpu"
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for dev in sorted_devices:
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if dev != compute_device:
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donor_device = dev
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break # Use first available donor
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block_assignments[module_name] = donor_device
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setattr(module_object, 'distorch2_cpu_offload', True) # Attach the attribute here
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offloaded_bytes += module_size
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else:
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# Assign remaining blocks to the primary compute device
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block_assignments[module_name] = compute_device
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# Populate device_assignments from the final block_assignments
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for module_size, module_name, module_object, params in block_list:
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device = block_assignments[module_name]
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if device not in device_assignments:
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device_assignments[device] = []
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device_assignments[device].append((module_name, module_object, module_object.__class__.__name__, module_size))
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# Log final assignments - IDENTICAL FORMAT TO GGML
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logger.info("DisTorch2 Model Final Device/Layer Assignments")
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logger.info(dash_line)
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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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# Log distributed blocks
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total_assigned_memory = 0
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device_memories = {}
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for device, blocks in device_assignments.items():
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device_memory = sum(b[3] for b in blocks)
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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_memories.keys(), key=lambda d: (d == "cpu", d))
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for dev in sorted_assignments:
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if dev not in device_memories:
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continue
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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(device_assignments[dev])), f"{mem_mb:.2f}", f"{mem_percent:.1f}%"))
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logger.info(dash_line)
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return {
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"device_assignments": device_assignments,
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"block_assignments": block_assignments,
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"lowvram_model_memory": total_assigned_memory,
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}
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def calculate_safetensor_vvram_allocation(model_patcher, virtual_vram_str):
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"""Calculate virtual VRAM allocation string for distributed safetensor loading"""
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@@ -609,7 +463,6 @@ def calculate_safetensor_vvram_allocation(model_patcher, virtual_vram_str):
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return allocation_string
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def override_class_with_distorch_safetensor_v2(cls):
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"""DisTorch 2.0 wrapper for safetensor models"""
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from .nodes import get_device_list
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+855
@@ -0,0 +1,855 @@
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{
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"last_link_id": 97,
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"nodes": [
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"id": 10,
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"type": "SaveImage",
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"name": "images",
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"outputs": [],
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"properties": {
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"color": "#233",
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""
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Reference in New Issue
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