mirror of
https://github.com/comfyanonymous/ComfyUI.git
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e154da83b1
* memory_management: Add direct to read GPU mode Make destination optional (or make it optionally GPU) and use aimdo to file_read direct to GPU. * ops: Remove stream pin buffers and use aimdo reads This consumed too much RAM and its better to just take the hit on the CPU syncing back the stream on a short ring buffer. Aimdo implements this so just rip the stream pin buffer from comfy. * model_management: all active pin registration movement Its better to just let the active model load past the pin limit as pins and let the pins move around. The saves the HDD and SATA people disk traffic while only costing a few GPU syncs. * utils: use aimdo file handle This opens on windows with more favourable flags * mp: only count the model proper for loaded_ram and vram Exclude live loras from the numbers to avoid the case where the reported loaded memory exceeds the size of the model. This causes me confusion in the Kijai visualizer when it looked fully loaded but was hitting disk due to this accounding disrepency. * utils: add bit reverse utility useful for max scattering something ordered. * pinned_memory: Implement offload balancing Use a max scatter alogorithm to prioritize pins of the same size such that when doing a little bit of offloading it gets scattered, allowing the prefetcher to more evenly swollow the offload. * comfy-aimdo 0.4.7 Aimdo 0.4.7 implement VRAM buffer exhaustion predection to avoid early speculative load of weights that definately wont fix once the inference gets further in. * model-prefetch: consolidate pin ensures on the sync point This could happen mid prefetch block, cause a sync of the entire block and lose overlap. Get ahead of the problem with a free down at the natural compute stream sync point. * mm: Put a 2GB min on the pin ceiling This is reasonably bad if it starts causing swap pressure, moreso than during normal ram-cache proceedings. Clamp it. * add --fast-disk
78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
import comfy_aimdo.model_vbar
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import comfy.memory_management
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import comfy.model_management
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import comfy.ops
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PREFETCH_QUEUES = []
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def cleanup_prefetched_modules(comfy_modules):
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for s in comfy_modules:
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prefetch = getattr(s, "_prefetch", None)
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if prefetch is None:
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continue
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for param_key in ("weight", "bias"):
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lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
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if lowvram_fn is not None:
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lowvram_fn.clear_prepared()
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if prefetch["signature"] is not None:
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comfy_aimdo.model_vbar.vbar_unpin(s._v)
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delattr(s, "_prefetch")
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def cleanup_prefetch_queues():
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global PREFETCH_QUEUES
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for queue in PREFETCH_QUEUES:
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for entry in queue:
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if entry is None or not isinstance(entry, tuple):
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continue
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_, prefetch_state = entry
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comfy_modules = prefetch_state[1]
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if comfy_modules is not None:
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cleanup_prefetched_modules(comfy_modules)
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PREFETCH_QUEUES = []
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def prefetch_queue_pop(queue, device, module):
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if queue is None:
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return
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consumed = queue.pop(0)
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if consumed is not None:
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offload_stream, prefetch_state = consumed
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if offload_stream is not None:
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offload_stream.wait_stream(comfy.model_management.current_stream(device))
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_, comfy_modules = prefetch_state
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if comfy_modules is not None:
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cleanup_prefetched_modules(comfy_modules)
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prefetch = queue[0]
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if prefetch is not None:
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comfy_modules = []
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for s in prefetch.modules():
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if hasattr(s, "_v"):
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comfy_modules.append(s)
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registerable_size = 0
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for s in comfy_modules:
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registerable_size += comfy.memory_management.vram_aligned_size([s.weight, s.bias])
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for param_key in ("weight", "bias"):
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lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
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if lowvram_fn is not None:
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registerable_size += lowvram_fn.memory_required()
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offload_stream = comfy.ops.cast_modules_with_vbar(comfy_modules, None, device, None, True)
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if not comfy.model_management.args.fast_disk:
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comfy.model_management.ensure_pin_registerable(registerable_size)
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comfy.model_management.sync_stream(device, offload_stream)
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queue[0] = (offload_stream, (prefetch, comfy_modules))
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def make_prefetch_queue(queue, device, transformer_options):
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if (not transformer_options.get("prefetch_dynamic_vbars", False)
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or comfy.model_management.NUM_STREAMS == 0
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or comfy.model_management.is_device_cpu(device)
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or not comfy.model_management.device_supports_non_blocking(device)):
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return None
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queue = [None] + queue + [None]
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PREFETCH_QUEUES.append(queue)
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return queue
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