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Made MultiGPU Work Units node more robust by forcing ModelPatcher clones to match at sample time, reuse loaded MultiGPU clones, finalize MultiGPU Work Units node ID and name, small refactors/cleanup of logging and multigpu-related code

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This commit is contained in:
Jedrzej Kosinski
2025-03-03 22:56:13 -06:00
parent 605893d3cf
commit 093914a247
7 changed files with 188 additions and 87 deletions
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67
comfy/model_patcher.py
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@@ -243,7 +243,7 @@ class ModelPatcher:
self.is_clip = False
self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed
self.is_multigpu_clone = False
self.is_multigpu_base_clone = False
self.clone_base_uuid = uuid.uuid4()
if not hasattr(self.model, 'model_loaded_weight_memory'):
@@ -324,14 +324,16 @@ class ModelPatcher:
n.is_clip = self.is_clip
n.hook_mode = self.hook_mode
n.is_multigpu_clone = self.is_multigpu_clone
n.is_multigpu_base_clone = self.is_multigpu_base_clone
n.clone_base_uuid = self.clone_base_uuid
for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE):
callback(self, n)
return n
def multigpu_deepclone(self, new_load_device=None, models_cache: dict[ModelPatcher,ModelPatcher]=None):
def deepclone_multigpu(self, new_load_device=None, models_cache: dict[uuid.UUID,ModelPatcher]=None):
logging.info(f"Creating deepclone of {self.model.__class__.__name__} for {new_load_device if new_load_device else self.load_device}.")
comfy.model_management.unload_model_and_clones(self)
n = self.clone()
# set load device, if present
if new_load_device is not None:
@@ -350,19 +352,64 @@ class ModelPatcher:
for key, model_list in n.additional_models.items():
for i in range(len(model_list)):
add_model = n.additional_models[key][i]
if i not in models_cache:
models_cache[add_model] = add_model.multigpu_deepclone(new_load_device=new_load_device, models_cache=models_cache)
n.additional_models[key][i] = models_cache[add_model]
if add_model.clone_base_uuid not in models_cache:
models_cache[add_model.clone_base_uuid] = add_model.deepclone_multigpu(new_load_device=new_load_device, models_cache=models_cache)
n.additional_models[key][i] = models_cache[add_model.clone_base_uuid]
for callback in self.get_all_callbacks(CallbacksMP.ON_DEEPCLONE_MULTIGPU):
callback(self, n)
return n
def match_multigpu_clones(self):
multigpu_models = self.get_additional_models_with_key("multigpu")
if len(multigpu_models) > 0:
new_multigpu_models = []
for mm in multigpu_models:
# clone main model, but bring over relevant props from existing multigpu clone
n = self.clone()
n.load_device = mm.load_device
n.backup = mm.backup
n.object_patches_backup = mm.object_patches_backup
n.hook_backup = mm.hook_backup
n.model = mm.model
n.is_multigpu_base_clone = mm.is_multigpu_base_clone
n.remove_additional_models("multigpu")
orig_additional_models: dict[str, list[ModelPatcher]] = comfy.patcher_extension.copy_nested_dicts(n.additional_models)
n.additional_models = comfy.patcher_extension.copy_nested_dicts(mm.additional_models)
# figure out which additional models are not present in multigpu clone
models_cache = {}
for mm_add_model in mm.get_additional_models():
models_cache[mm_add_model.clone_base_uuid] = mm_add_model
remove_models_uuids = set(list(models_cache.keys()))
for key, model_list in orig_additional_models.items():
for orig_add_model in model_list:
if orig_add_model.clone_base_uuid not in models_cache:
models_cache[orig_add_model.clone_base_uuid] = orig_add_model.deepclone_multigpu(new_load_device=n.load_device, models_cache=models_cache)
existing_list = n.get_additional_models_with_key(key)
existing_list.append(models_cache[orig_add_model.clone_base_uuid])
n.set_additional_models(key, existing_list)
if orig_add_model.clone_base_uuid in remove_models_uuids:
remove_models_uuids.remove(orig_add_model.clone_base_uuid)
# remove duplicate additional models
for key, model_list in n.additional_models.items():
new_model_list = [x for x in model_list if x.clone_base_uuid not in remove_models_uuids]
n.set_additional_models(key, new_model_list)
for callback in self.get_all_callbacks(CallbacksMP.ON_MATCH_MULTIGPU_CLONES):
callback(self, n)
new_multigpu_models.append(n)
self.set_additional_models("multigpu", new_multigpu_models)
def is_clone(self, other):
if hasattr(other, 'model') and self.model is other.model:
return True
return False
def clone_has_same_weights(self, clone: 'ModelPatcher'):
if not self.is_clone(clone):
return False
def clone_has_same_weights(self, clone: ModelPatcher, allow_multigpu=False):
if allow_multigpu:
if self.clone_base_uuid != clone.clone_base_uuid:
return False
else:
if not self.is_clone(clone):
return False
if self.current_hooks != clone.current_hooks:
return False
@@ -957,7 +1004,7 @@ class ModelPatcher:
return self.additional_models.get(key, [])
def get_additional_models(self):
all_models = []
all_models: list[ModelPatcher] = []
for models in self.additional_models.values():
all_models.extend(models)
return all_models