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[Core] better support offloading when side loading is enabled. #4855

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merged 13 commits into from
Sep 5, 2023
Merged

[Core] better support offloading when side loading is enabled. #4855

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c810d48
better support offloading when side loading is enabled.
sayakpaul Aug 31, 2023
c14fc20
load_textual_inversion
sayakpaul Aug 31, 2023
46b0874
better messaging for textual inversion.
sayakpaul Aug 31, 2023
6c842c7
fixes
sayakpaul Aug 31, 2023
2a27542
address PR feedback.
sayakpaul Aug 31, 2023
b3fb9a7
sdxl support.
sayakpaul Aug 31, 2023
773ff91
improve messaging
sayakpaul Aug 31, 2023
cd2d963
Merge branch 'main' into improve-side-offloading
sayakpaul Sep 4, 2023
b8b5422
recursive removal when cpu sequential offloading is enabled.
sayakpaul Sep 4, 2023
3d06c51
add: lora tests
sayakpaul Sep 4, 2023
340887e
recruse.
sayakpaul Sep 4, 2023
7bcf71d
add: offload tests for textual inversion.
sayakpaul Sep 4, 2023
6b88f4e
Merge branch 'main' into improve-side-offloading
sayakpaul Sep 5, 2023
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39 changes: 39 additions & 0 deletions src/diffusers/loaders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,7 @@

if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
from accelerate.utils import set_module_tensor_to_device

logger = logging.get_logger(__name__)
Expand Down Expand Up @@ -763,6 +764,19 @@ def load_textual_inversion(
f" `{self.load_textual_inversion.__name__}`"
)

# Remove any existing hooks.
is_model_cpu_offload = False
is_sequential_cpu_offload = False
for _, component in self.components.items():
if isinstance(component, nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
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is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
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nice!

logger.info(
"Accelerate hooks detected. Since you have called `load_textual_inversion()`, the previous hooks will be first removed. Then the textual inversion parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component)

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Doesn't one of these two hooks styles hook into every sub module as well, so shouldn't one of the checks be recursive?

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@muellerzr to help here a bit.

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@muellerzr to help here a bit.

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You should be able to do remove_hook_from_module(component, recursive=True)

CC @SunMarc too for a second glance :)

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But is it required here? Sorry for not making my comment clear.

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I guess trying to understand just what we're aiming to achieve (solid guess based on context, let me know if I'm accurate):

  • Given a model that may be loaded in via device_map="auto" or some form of device_map
  • We wish to remove the hooks when wanting to load the full model into memory/remove it from BMI (big model inference)
  • With the potential of placing it back later

Is this accurate? Otherwise may need a bit more info/context I'm missing somehow

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#3922 (comment)

We want to be able to detect if a torch.nn.Module has hooks and we want to remove them. That is the bit relevant to accelerate. Then after loading some auxiliary weights, we want to load the appropriate hooks back in.

Let me know if that helps?

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From what I understood from the codebase, if we have is_sequential_cpu_offload, it means that the components were offloaded using cpu_offload which places recursively the hooks on each submodules. In the case of is_model_cpu_offload, we use cpu_offload_from_hook which place only one hook on the module, so that the entire model will be offloaded when another hook is triggered.
I would then suggest using remove_hook_from_module(component, recursive=True) for the first case and remove_hook_from_module(component, recursive=False) for the second case if you don't want to just recursively remove all the hooks for both cases !

cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
Expand Down Expand Up @@ -916,6 +930,12 @@ def load_textual_inversion(
for token_id, embedding in token_ids_and_embeddings:
self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding

# offload back
if is_model_cpu_offload:
self.enable_model_cpu_offload()
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elif is_sequential_cpu_offload:
self.enable_sequential_cpu_offload()


class LoraLoaderMixin:
r"""
Expand Down Expand Up @@ -946,6 +966,19 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
kwargs (`dict`, *optional*):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
"""
# Remove any existing hooks.
is_model_cpu_offload = False
is_sequential_cpu_offload = False
for _, component in self.components.items():
if isinstance(component, nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
logger.info(
"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component)

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state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
self.load_lora_into_text_encoder(
Expand All @@ -955,6 +988,12 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
lora_scale=self.lora_scale,
)

# Offload back.
if is_model_cpu_offload:
self.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
self.enable_sequential_cpu_offload()

@classmethod
def lora_state_dict(
cls,
Expand Down
23 changes: 23 additions & 0 deletions src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1221,6 +1221,23 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
# We could have accessed the unet config from `lora_state_dict()` too. We pass
# it here explicitly to be able to tell that it's coming from an SDXL
# pipeline.

# Remove any existing hooks.
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
else:
raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
is_model_cpu_offload = False
is_sequential_cpu_offload = False
for _, component in self.components.items():
if isinstance(component, torch.nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
logger.info(
"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
)
Comment on lines +1230 to +1232
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I think the log statement might be a bit noisy. It'd be nice if we expected the user to do additional things with the placed accelerate hooks and should be aware if they expected some state to be maintained or something but we definitely don't want the user to touch the hooks.

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I think it's relatively simple given the context the message is being raised from. If you have a better suggestion, let me know.

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Sorry, I think my main point is the log is a bit noisy given that it leaks what is supposed to be an internal implementation detail, I think it's not really something that should be exposed to an end user

remove_hook_from_module(component)
state_dict, network_alphas = self.lora_state_dict(
pretrained_model_name_or_path_or_dict,
unet_config=self.unet.config,
Expand Down Expand Up @@ -1248,6 +1265,12 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
lora_scale=self.lora_scale,
)

# Offload back.
if is_model_cpu_offload:
self.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
self.enable_sequential_cpu_offload()

@classmethod
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
def save_lora_weights(
Expand Down
23 changes: 23 additions & 0 deletions src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -907,6 +907,23 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
# We could have accessed the unet config from `lora_state_dict()` too. We pass
# it here explicitly to be able to tell that it's coming from an SDXL
# pipeline.

# Remove any existing hooks.
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
else:
raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
is_model_cpu_offload = False
is_sequential_cpu_offload = False
for _, component in self.components.items():
if isinstance(component, torch.nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
logger.info(
"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component)
state_dict, network_alphas = self.lora_state_dict(
pretrained_model_name_or_path_or_dict,
unet_config=self.unet.config,
Expand Down Expand Up @@ -934,6 +951,12 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
lora_scale=self.lora_scale,
)

# Offload back.
if is_model_cpu_offload:
self.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
self.enable_sequential_cpu_offload()

@classmethod
def save_lora_weights(
self,
Expand Down
23 changes: 23 additions & 0 deletions src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -1065,6 +1065,23 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
# We could have accessed the unet config from `lora_state_dict()` too. We pass
# it here explicitly to be able to tell that it's coming from an SDXL
# pipeline.

# Remove any existing hooks.
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
else:
raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
is_model_cpu_offload = False
is_sequential_cpu_offload = False
for _, component in self.components.items():
if isinstance(component, torch.nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
logger.info(
"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component)
state_dict, network_alphas = self.lora_state_dict(
pretrained_model_name_or_path_or_dict,
unet_config=self.unet.config,
Expand Down Expand Up @@ -1092,6 +1109,12 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
lora_scale=self.lora_scale,
)

# Offload back.
if is_model_cpu_offload:
self.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
self.enable_sequential_cpu_offload()

@classmethod
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
def save_lora_weights(
Expand Down
23 changes: 23 additions & 0 deletions src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1370,6 +1370,23 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
# We could have accessed the unet config from `lora_state_dict()` too. We pass
# it here explicitly to be able to tell that it's coming from an SDXL
# pipeline.

# Remove any existing hooks.
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
else:
raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
is_model_cpu_offload = False
is_sequential_cpu_offload = False
for _, component in self.components.items():
if isinstance(component, torch.nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
logger.info(
"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component)
state_dict, network_alphas = self.lora_state_dict(
pretrained_model_name_or_path_or_dict,
unet_config=self.unet.config,
Expand Down Expand Up @@ -1397,6 +1414,12 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
lora_scale=self.lora_scale,
)

# Offload back.
if is_model_cpu_offload:
self.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
self.enable_sequential_cpu_offload()

@classmethod
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
def save_lora_weights(
Expand Down