mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-10 11:35:40 +00:00
Progress on scaffolding for an EasyCache style implementation of Sortblock
This commit is contained in:
Jedrzej Kosinski
@@ -128,6 +128,7 @@ class Flux(nn.Module):
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blocks_replace = patches_replace.get("dit", {})
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for i, block in enumerate(self.double_blocks):
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transformer_options["block"] = ("double_block", i)
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if ("double_block", i) in blocks_replace:
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def block_wrap(args):
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out = {}
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@@ -169,6 +170,7 @@ class Flux(nn.Module):
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img = torch.cat((txt, img), 1)
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for i, block in enumerate(self.single_blocks):
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transformer_options["block"] = ("single_block", i)
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if ("single_block", i) in blocks_replace:
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def block_wrap(args):
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out = {}
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300
comfy_extras/nodes_sortblock.py
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300
comfy_extras/nodes_sortblock.py
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@@ -0,0 +1,300 @@
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from __future__ import annotations
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from typing import TYPE_CHECKING, Union
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from scipy.sparse.linalg._dsolve.linsolve import is_pydata_spmatrix
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from comfy_api.latest import io, ComfyExtension
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import comfy.patcher_extension
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import logging
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import torch
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import comfy.model_patcher
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if TYPE_CHECKING:
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from uuid import UUID
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def outer_sample_wrapper(executor, *args, **kwargs):
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try:
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logging.info("Sortblock: inside outer_sample!")
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guider = executor.class_obj
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orig_model_options = guider.model_options
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guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options)
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# clone and prepare timesteps
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guider.model_options["transformer_options"]["sortblock"] = guider.model_options["transformer_options"]["sortblock"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
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sb_holder: SortblockHolder = guider.model_options["transformer_options"]["sortblock"]
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logging.info(f"Sortblock: enabled - threshold: {sb_holder.reuse_threshold}, start_percent: {sb_holder.start_percent}, end_percent: {sb_holder.end_percent}")
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return executor(*args, **kwargs)
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finally:
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sb_holder = guider.model_options["transformer_options"]["sortblock"]
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sb_holder.reset()
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guider.model_options = orig_model_options
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def model_forward_wrapper(executor, *args, **kwargs):
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timestep: float = args[1]
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transformer_options: dict[str] = args[-1]
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if not isinstance(transformer_options, dict):
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transformer_options = kwargs.get("transformer_options")
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if not transformer_options:
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transformer_options = args[-2]
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logging.info(f"Sortblock: inside model {executor.class_obj.__class__.__name__}")
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sb_holder: SortblockHolder = transformer_options["sortblock"]
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sb_holder.update_should_do_sortblock(timestep)
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sb_holder.update_is_past_end_timestep(timestep)
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if sb_holder.initial_step:
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transformer_options["total_double_block"] = len(executor.class_obj.double_blocks)
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transformer_options["total_single_block"] = len(executor.class_obj.single_blocks)
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# save the original forwards on the blocks
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logging.info(f"Sortblock: saving preparing {transformer_options['total_double_block']} double blocks and {transformer_options['total_single_block']} single blocks")
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for block in executor.class_obj.double_blocks:
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prepare_block(block, sb_holder)
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for block in executor.class_obj.single_blocks:
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prepare_block(block, sb_holder)
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sb_holder.initial_step = False
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return executor(*args, **kwargs)
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def prepare_block(block, sb_holder: SortblockHolder, stream_count: int=1):
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sb_holder.add_block(block)
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block.__original_forward = block.forward
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block.forward = block_forward_factory(block.__original_forward, block)
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block.__block_cache = BlockCache(subsample_factor=sb_holder.subsample_factor, verbose=sb_holder.verbose)
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def clean_block(block):
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block.forward = block.__original_forward
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del block.__original_forward
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del block.__block_cache
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def block_forward_factory(func, block):
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def block_forward_wrapper(*args, **kwargs):
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transformer_options: dict[str] = kwargs.get("transformer_options", None)
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logging.info(f"Sortblock: inside block {transformer_options['block']}")
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sb_holder: SortblockHolder = transformer_options["sortblock"]
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if sb_holder.is_past_end_timestep():
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return func(*args, **kwargs)
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# do sortblock stuff
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cache: BlockCache = block.__block_cache
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keys = list(kwargs.keys())
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x: torch.Tensor = kwargs[keys[0]]
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timestep: float = sb_holder.curr_t
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# prepare next_x_prev
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next_x_prev = cache.get_next_x_prev(kwargs)
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input_change = None
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do_sortblock = sb_holder.should_do_sortblock()
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if do_sortblock:
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# TODO: checkmetadata
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if cache.has_x_prev_subsampled():
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input_change = (cache.subsample(x, clone=False) - cache.x_prev_subsampled).flatten().abs().mean()
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if cache.has_output_prev_norm() and cache.has_relative_transformation_rate():
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approx_output_change_rate = (cache.relative_transformation_rate * input_change) / cache.output_prev_norm
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cache.cumulative_change_rate += approx_output_change_rate
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if cache.cumulative_change_rate < sb_holder.reuse_threshold:
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# accumulate error + skip block
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pass
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else:
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# reset error; NOT skipping block and recalculating
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cache.cumulative_change_rate = 0.0
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# output_raw is expected to have cache.stream_count elements if count is greaater than 1 (double block, etc.)
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output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]] = func(*args, **kwargs)
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# if more than one stream from block, only use first one
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if isinstance(output_raw, tuple):
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output = output_raw[0]
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else:
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output = output_raw
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if cache.has_output_prev_norm():
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output_change = (cache.subsample(output, clone=False) - cache.output_prev_subsampled).flatten().abs().mean()
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# if verbose in future
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output_change_rate = output_change / cache.output_prev_norm
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cache.output_change_rates.append(output_change_rate.item())
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if cache.has_relative_transformation_rate():
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approx_output_change_rate = (cache.relative_transformation_rate * input_change) / cache.output_prev_norm
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cache.approx_output_change_rates.append(approx_output_change_rate.item())
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if input_change is not None:
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cache.relative_transformation_rate = output_change / input_change
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# TODO: allow cache_diff to be offloaded
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cache.update_cache_diff(output_raw, next_x_prev)
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cache.x_prev_subsampled = cache.subsample(next_x_prev)
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cache.output_prev_subsampled = cache.subsample(output)
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cache.output_prev_norm = output.flatten().abs().mean()
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return output_raw
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return block_forward_wrapper
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class SortblockHolder:
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def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int=8, verbose: bool=False):
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self.reuse_threshold = reuse_threshold
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self.start_percent = start_percent
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self.end_percent = end_percent
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self.subsample_factor = subsample_factor
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self.verbose = verbose
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# timestep values
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self.start_t = 0.0
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self.end_t = 0.0
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self.curr_t = 0.0
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# control values
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self.past_timestep = False
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self.do_sortblock = False
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self.initial_step = True
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# cache values
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self.blocks = []
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def add_block(self, block):
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self.blocks.append(block)
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def prepare_timesteps(self, model_sampling):
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self.start_t = model_sampling.percent_to_sigma(self.start_percent)
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self.end_t = model_sampling.percent_to_sigma(self.end_percent)
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return self
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def update_is_past_end_timestep(self, timestep: float) -> bool:
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self.past_timestep = not (timestep[0] > self.end_t).item()
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return self.past_timestep
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def is_past_end_timestep(self) -> bool:
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return self.past_timestep
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def update_should_do_sortblock(self, timestep: float) -> bool:
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self.do_sortblock = (timestep[0] <= self.start_t).item()
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self.curr_t = timestep
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return self.do_sortblock
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def should_do_sortblock(self) -> bool:
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return self.do_sortblock
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def reset(self):
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self.initial_step = True
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self.curr_t = 0.0
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logging.info(f"Sortblock: resetting {len(self.blocks)} blocks")
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for block in self.blocks:
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clean_block(block)
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self.blocks = []
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return self
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def clone(self):
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return SortblockHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.verbose)
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class BlockCache:
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def __init__(self, subsample_factor: int=8, stream_count: int=1, verbose: bool=False):
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self.subsample_factor = subsample_factor
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self.stream_count = stream_count
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self.verbose = verbose
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# control values
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self.relative_transformation_rate: float = None
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self.cumulative_change_rate = 0.0
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self.initial_step = True
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# cache values
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self.x_prev_subsampled: torch.Tensor = None
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self.output_prev_subsampled: torch.Tensor = None
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self.output_prev_norm: torch.Tensor = None
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self.cache_diff: list[torch.Tensor] = [None for _ in range(stream_count)]
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self.output_change_rates = []
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self.approx_output_change_rates = []
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self.total_steps_skipped = 0
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self.state_metadata = None
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def has_cache_diff(self) -> bool:
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return self.cache_diff[0] is not None
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def has_x_prev_subsampled(self) -> bool:
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return self.x_prev_subsampled is not None
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def has_output_prev_subsampled(self) -> bool:
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return self.output_prev_subsampled is not None
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def has_output_prev_norm(self) -> bool:
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return self.output_prev_norm is not None
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def has_relative_transformation_rate(self) -> bool:
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return self.relative_transformation_rate is not None
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def get_next_x_prev(self, d_kwargs: dict[str, torch.Tensor]) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
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keys = list(d_kwargs.keys())
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if self.stream_count == 1:
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return d_kwargs[keys[0]]
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return tuple([d_kwargs[keys[i]] for i in range(self.stream_count)])
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def subsample(self, x: Union[torch.Tensor, tuple[torch.Tensor, ...]], clone: bool = True) -> torch.Tensor:
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# subsample only the first compoenent
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if isinstance(x, tuple):
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return self.subsample(x[0], clone)
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if self.subsample_factor > 1:
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to_return = x[..., ::self.subsample_factor, ::self.subsample_factor]
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if clone:
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return to_return.clone()
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return to_return
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if clone:
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return x.clone()
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return x
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def apply_cache_diff(self, x: torch.Tensor):
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self.total_steps_skipped += 1
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return x + self.cache_diff.to(x.device)
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def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor):
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self.cache_diff = output - x
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def check_metadata(self, x: torch.Tensor) -> bool:
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# TODO: make sure shapes are correct
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metadata = (x.device, x.dtype, x.shape)
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if self.state_metadata is None:
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self.state_metadata = metadata
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return True
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if metadata == self.state_metadata:
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return True
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logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
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self.reset()
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return False
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def reset(self):
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self.relative_transformation_rate = 0.0
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self.cumulative_change_rate = 0.0
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self.initial_step = True
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self.cache_diff = [None for _ in range(self.stream_count)]
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self.output_change_rates = []
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self.approx_output_change_rates = []
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self.total_steps_skipped = 0
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self.state_metadata = None
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class SortblockNode(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="Sortblock",
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display_name="Sortblock",
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description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
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category="advanced/debug/model",
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is_experimental=True,
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inputs=[
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io.Model.Input("model", tooltip="The model to add Sortblock to."),
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io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached blocks."),
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io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of Sortblock."),
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io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of Sortblock."),
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io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
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],
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outputs=[
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io.Model.Output(tooltip="The model with Sortblock."),
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],
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)
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@classmethod
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def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
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# TODO: check for specific flavors of supported models
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model = model.clone()
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model.model_options["transformer_options"]["sortblock"] = SortblockHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, verbose=verbose)
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model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "sortblock", outer_sample_wrapper)
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model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "sortblock", model_forward_wrapper)
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# model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper)
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# model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper)
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return io.NodeOutput(model)
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class SortblockExtension(ComfyExtension):
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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SortblockNode,
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]
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def comfy_entrypoint():
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return SortblockExtension()
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