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Add OneDiffCheckpointLoader #457

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merged 7 commits into from
Dec 26, 2023
Merged

Add OneDiffCheckpointLoader #457

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0055a42
init
ccssu Dec 26, 2023
35f57ae
Update onediff_comfy_nodes/__init__.py
ccssu Dec 26, 2023
05db45a
Update onediff_comfy_nodes/_nodes.py
ccssu Dec 26, 2023
8a811ad
refine
ccssu Dec 26, 2023
9e396e2
refine
ccssu Dec 26, 2023
22ef3e3
Merge branch 'main' into add_onediffcheckpointloadersimple
ccssu Dec 26, 2023
0f491f5
Update _nodes.py
ccssu Dec 26, 2023
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3 changes: 3 additions & 0 deletions onediff_comfy_nodes/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
ControlNetGraphSaver,
SVDSpeedup,
ModuleDeepCacheSpeedup,
OneDiffCheckpointLoaderSimple,
)
from ._compare_node import CompareModel, ShowImageDiff

Expand All @@ -30,6 +31,7 @@
"ControlNetGraphSaver": ControlNetGraphSaver,
"SVDSpeedup": SVDSpeedup,
"ModuleDeepCacheSpeedup": ModuleDeepCacheSpeedup,
"OneDiffCheckpointLoaderSimple": OneDiffCheckpointLoaderSimple,
}

NODE_DISPLAY_NAME_MAPPINGS = {
Expand All @@ -46,6 +48,7 @@
"ControlNetGraphSaver": "ControlNet Graph Saver",
"SVDSpeedup": "SVD Speedup",
"ModuleDeepCacheSpeedup": "Model DeepCache Speedup",
"OneDiffCheckpointLoaderSimple": "OneDiff Load Checkpoint",
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}

if _USE_UNET_INT8:
Expand Down
38 changes: 38 additions & 0 deletions onediff_comfy_nodes/_nodes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
import folder_paths
from comfy import model_management
from comfy.cli_args import args
from folder_paths import get_input_directory

from .utils import (
OneFlowSpeedUpModelPatcher,
Expand Down Expand Up @@ -577,3 +578,40 @@ def apply_model(model_function, kwargs):

oneflow_model.set_model_unet_function_wrapper(apply_model)
return (oneflow_model,)


def get_guess_graph_path(ckpt_name, model):
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input_dir = get_input_directory()
input_dir = Path(input_dir)
graph_dir = input_dir / "graphs" / ckpt_name
graph_file_path = graph_dir / (type(model).__name__ + ".graph")
return graph_file_path


from nodes import CheckpointLoaderSimple
class OneDiffCheckpointLoaderSimple(CheckpointLoaderSimple):
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def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
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model, clip, vae = super().load_checkpoint(ckpt_name, output_vae, output_clip)
offload_device = model_management.unet_offload_device()

diffusion_model = model.model.diffusion_model
file_path = get_guess_graph_path(ckpt_name, diffusion_model)
print(f" OneDiffCheckpointLoaderSimple load_checkpoint file_path {file_path}")
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oneflow_model = OneFlowSpeedUpModelPatcher(
model.model,
load_device=model_management.get_torch_device(),
offload_device=offload_device,
use_graph=True,
graph_path=file_path,
graph_device=model_management.get_torch_device(),
)

file_path = get_guess_graph_path(ckpt_name, vae.first_stage_model)
vae.first_stage_model = oneflow_compile(
vae.first_stage_model,
use_graph=True,
graph_path=file_path,
graph_device=model_management.get_torch_device(),
)
return oneflow_model, clip, vae
18 changes: 13 additions & 5 deletions onediff_comfy_nodes/utils/model_patcher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,8 @@ def __init__(
weight_inplace_update=False,
*,
use_graph=None,
graph_path=None,
graph_device=None,
):
from onediff.infer_compiler import oneflow_compile
from onediff.infer_compiler.with_oneflow_compile import DeployableModule
Expand All @@ -46,7 +48,10 @@ def __init__(
] = self.model.diffusion_model
else:
self.model.__dict__["_modules"]["diffusion_model"] = oneflow_compile(
self.model.diffusion_model, use_graph=use_graph
self.model.diffusion_model,
use_graph=use_graph,
graph_path=graph_path,
graph_device=graph_device,
)
self.model._register_state_dict_hook(state_dict_hook)
self.patches = {}
Expand Down Expand Up @@ -495,7 +500,6 @@ def __init__(
):
from onediff.infer_compiler import oneflow_compile
from onediff.infer_compiler.with_oneflow_compile import DeployableModule


self.weight_inplace_update = weight_inplace_update
self.object_patches = {}
Expand All @@ -504,10 +508,14 @@ def __init__(
self.model = copy.copy(model)
self.model.__dict__["_modules"] = copy.copy(model.__dict__["_modules"])
self.deep_cache_unet = oneflow_compile(
DeepCacheUNet(self.model.diffusion_model, cache_layer_id, cache_block_id), use_graph=use_graph
DeepCacheUNet(self.model.diffusion_model, cache_layer_id, cache_block_id),
use_graph=use_graph,
)
self.fast_deep_cache_unet =oneflow_compile(
FastDeepCacheUNet(self.model.diffusion_model, cache_layer_id, cache_block_id), use_graph=use_graph
self.fast_deep_cache_unet = oneflow_compile(
FastDeepCacheUNet(
self.model.diffusion_model, cache_layer_id, cache_block_id
),
use_graph=use_graph,
)
self.model._register_state_dict_hook(state_dict_hook)
self.patches = {}
Expand Down
113 changes: 96 additions & 17 deletions src/onediff/infer_compiler/with_oneflow_compile.py
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Expand Up @@ -56,10 +56,12 @@ def _align_tensor(torch_module, oneflow_module):
[x for x, _ in oneflow_module.named_parameters()]
+ [x for x, _ in oneflow_module.named_buffers()]
)
for name, tensor in chain.from_iterable([
torch_module.named_parameters(),
torch_module.named_buffers(),
]):
for name, tensor in chain.from_iterable(
[
torch_module.named_parameters(),
torch_module.named_buffers(),
]
):
if name not in oneflow_tensor_list:
tensor.data = tensor.to(*args, **kwargs)
else:
Expand All @@ -76,7 +78,6 @@ def _align_tensor(torch_module, oneflow_module):
else:
_align_tensor(module, self._oneflow_module.get_submodule(name))


def __getattr__(self, name):
if name == "_torch_module":
return self._modules[name]
Expand Down Expand Up @@ -124,7 +125,11 @@ def __init__(self, torch_modules, oneflow_modules):
for torch_module, oneflow_module in zip(
self._torch_modules, self._oneflow_modules
):
dual_modules.append(get_mixed_dual_module(torch_module.__class__)(torch_module, oneflow_module))
dual_modules.append(
get_mixed_dual_module(torch_module.__class__)(
torch_module, oneflow_module
)
)
# clear self._modules since `self._torch_modules = torch_modules` will append a module to self._modules
self._modules.clear()
self += dual_modules
Expand All @@ -147,6 +152,7 @@ def __setattr__(self, key, value):
setattr(self._oneflow_modules, key, value)
return object.__setattr__(self, key, value)


def get_mixed_dual_module(module_cls):
class MixedDualModule(DualModule, module_cls):
def __init__(self, torch_module, oneflow_module):
Expand All @@ -158,29 +164,77 @@ def __init__(self, torch_module, oneflow_module):
def handle_deployable_exception(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
def load_graph_from_config():
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try:
if self._graph_config is not None:
graph_path = self._graph_config[0]
if not os.path.exists(graph_path):
logger.warning(
f"Graph file {graph_path} not exists, skip load graph."
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)
return
graph_device = torch2oflow(self._graph_config[1])
self.load_graph(graph_path, graph_device)
self._graph_config = None
except Exception as e:
logger.error(f"Exception in load_graph_from_config: {e=}")

def save_graph_to_config():
try:
if self._graph_config is not None:
graph_file = self._graph_config[0]
os.makedirs(os.path.dirname(graph_file), exist_ok=True)
self.save_graph(self._graph_config[0])
logger.info(f"Save graph to {self._graph_config[0]} done!")
except Exception as e:
logger.error(f"Exception in save_graph_to_config: {e=}")

finally:
self._graph_config = None

def _run_func():
load_graph_from_config()
result = func(self, *args, **kwargs)
save_graph_to_config()
return result

if transform_mgr.debug_mode:
return func(self, *args, **kwargs)
return _run_func()
else:
try:
return func(self, *args, **kwargs)
return _run_func()
except Exception as e:
logger.error(f"Exception in {func.__name__}: {e=}")
logger.warning("Recompile oneflow module ...")
del self._deployable_module_model.oneflow_module
self._deployable_module_dpl_graph = None
return func(self, *args, **kwargs)
return _run_func()

return wrapper


class DeployableModule(torch.nn.Module):
def __init__(self, torch_module, oneflow_module, use_graph=True, options={}):
def __init__(
self,
torch_module,
oneflow_module,
use_graph=True,
options={},
graph_path=None,
graph_device=None,
):
torch.nn.Module.__init__(self)
self._deployable_module_model = get_mixed_dual_module(torch_module.__class__)(torch_module, oneflow_module)
self._deployable_module_model = get_mixed_dual_module(torch_module.__class__)(
torch_module, oneflow_module
)
self._deployable_module_use_graph = use_graph
self._deployable_module_options = options
self._deployable_module_dpl_graph = None
self._is_raw_deployable_module = True
if graph_path is not None:
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self._graph_config = (graph_path, graph_device)
else:
self._graph_config = None

@classmethod
def from_existing(cls, existing_module, use_graph=None, options=None):
Expand All @@ -190,6 +244,7 @@ def from_existing(cls, existing_module, use_graph=None, options=None):
instance._deployable_module_dpl_graph = (
existing_module._deployable_module_dpl_graph if use_graph else None
)
instance._graph_config = existing_module._graph_config
return instance

def get_graph(self):
Expand Down Expand Up @@ -245,7 +300,10 @@ def to(self, *args, **kwargs):

# assert the target device is same as graph device
target_device = parse_device(args, kwargs)
if target_device is not None and len(self._deployable_module_dpl_graph._blocks) > 0:
if (
target_device is not None
and len(self._deployable_module_dpl_graph._blocks) > 0
):
current_device = next(self._deployable_module_dpl_graph._state()).device
if not check_device(current_device, target_device):
raise RuntimeError(
Expand Down Expand Up @@ -357,11 +415,25 @@ def state_dict_hook(module, state_dict, prefix, local_metadata):


# Return a DeployableModule that using module_cls as it's parent class.
def get_mixed_deployable_module(module_cls):
def get_mixed_deployable_module(module_cls, graph_path=None, graph_device=None):
class MixedDeployableModule(DeployableModule, module_cls):
def __init__(self, torch_module, oneflow_module, use_graph=True, options={}):
def __init__(
self,
torch_module,
oneflow_module,
use_graph=True,
options={},
graph_path=None,
graph_device=None,
):
DeployableModule.__init__(
self, torch_module, oneflow_module, use_graph, options
self,
torch_module,
oneflow_module,
use_graph,
options,
graph_path,
graph_device,
)
self._is_raw_deployable_module = False

Expand All @@ -378,7 +450,14 @@ def from_existing(cls, existing_module, use_graph=None, options=None):
return MixedDeployableModule


def oneflow_compile(torch_module: torch.nn.Module, *, use_graph=True, options={}):
def oneflow_compile(
torch_module: torch.nn.Module,
*,
use_graph=True,
options={},
graph_path=None,
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这些放到 options 里面吧，oneflow_compile 要谨慎扩展参数

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@ccssu ccssu Dec 26, 2023 •
edited
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好的就是传入 options = { "graph_config": (graph_path, graph_device)} 这种吗 还是 options = { "graph_path":graph_path, "graph_device":graph_device}

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graph_file, graph_file_device

graph_device=None,
):
set_default_registry()

def wrap_module(module):
Expand All @@ -387,7 +466,7 @@ def wrap_module(module):
return module.__class__.from_existing(module, use_graph, options)
else:
return get_mixed_deployable_module(module.__class__)(
module, None, use_graph, options
module, None, use_graph, options, graph_path, graph_device
)

model = wrap_module(torch_module)
Expand Down
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