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Zero
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import einops
from diffusers import StableDiffusionXLPipeline, IFPipeline
from typing import List, Dict, Callable, Union
import torch
from .hooked_scheduler import HookedNoiseScheduler
def retrieve(io):
if isinstance(io, tuple):
if len(io) == 1:
return io[0]
else:
raise ValueError("A tuple should have length of 1")
elif isinstance(io, torch.Tensor):
return io
else:
raise ValueError("Input/Output must be a tensor, or 1-element tuple")
class HookedDiffusionAbstractPipeline:
parent_cls = None
pipe = None
def __init__(self, pipe: parent_cls, use_hooked_scheduler: bool = False):
if use_hooked_scheduler:
pipe.scheduler = HookedNoiseScheduler(pipe.scheduler)
self.__dict__['pipe'] = pipe
self.use_hooked_scheduler = use_hooked_scheduler
@classmethod
def from_pretrained(cls, *args, **kwargs):
return cls(cls.parent_cls.from_pretrained(*args, **kwargs))
def run_with_hooks(self,
*args,
position_hook_dict: Dict[str, Union[Callable, List[Callable]]],
**kwargs
):
'''
Run the pipeline with hooks at specified positions.
Returns the final output.
Args:
*args: Arguments to pass to the pipeline.
position_hook_dict: A dictionary mapping positions to hooks.
The keys are positions in the pipeline where the hooks should be registered.
The values are either a single hook or a list of hooks to be registered at the specified position.
Each hook should be a callable that takes three arguments: (module, input, output).
**kwargs: Keyword arguments to pass to the pipeline.
'''
hooks = []
for position, hook in position_hook_dict.items():
if isinstance(hook, list):
for h in hook:
hooks.append(self._register_general_hook(position, h))
else:
hooks.append(self._register_general_hook(position, hook))
hooks = [hook for hook in hooks if hook is not None]
try:
output = self.pipe(*args, **kwargs)
finally:
for hook in hooks:
hook.remove()
if self.use_hooked_scheduler:
self.pipe.scheduler.pre_hooks = []
self.pipe.scheduler.post_hooks = []
return output
def run_with_cache(self,
*args,
positions_to_cache: List[str],
save_input: bool = False,
save_output: bool = True,
**kwargs
):
'''
Run the pipeline with caching at specified positions.
This method allows you to cache the intermediate inputs and/or outputs of the pipeline
at certain positions. The final output of the pipeline and a dictionary of cached values
are returned.
Args:
*args: Arguments to pass to the pipeline.
positions_to_cache (List[str]): A list of positions in the pipeline where intermediate
inputs/outputs should be cached.
save_input (bool, optional): If True, caches the input at each specified position.
Defaults to False.
save_output (bool, optional): If True, caches the output at each specified position.
Defaults to True.
**kwargs: Keyword arguments to pass to the pipeline.
Returns:
final_output: The final output of the pipeline after execution.
cache_dict (Dict[str, Dict[str, Any]]): A dictionary where keys are the specified positions
and values are dictionaries containing the cached 'input' and/or 'output' at each position,
depending on the flags `save_input` and `save_output`.
'''
cache_input, cache_output = dict() if save_input else None, dict() if save_output else None
hooks = [
self._register_cache_hook(position, cache_input, cache_output) for position in positions_to_cache
]
hooks = [hook for hook in hooks if hook is not None]
output = self.pipe(*args, **kwargs)
for hook in hooks:
hook.remove()
if self.use_hooked_scheduler:
self.pipe.scheduler.pre_hooks = []
self.pipe.scheduler.post_hooks = []
cache_dict = {}
if save_input:
for position, block in cache_input.items():
cache_input[position] = torch.stack(block, dim=1)
cache_dict['input'] = cache_input
if save_output:
for position, block in cache_output.items():
cache_output[position] = torch.stack(block, dim=1)
cache_dict['output'] = cache_output
return output, cache_dict
def run_with_hooks_and_cache(self,
*args,
position_hook_dict: Dict[str, Union[Callable, List[Callable]]],
positions_to_cache: List[str] = [],
save_input: bool = False,
save_output: bool = True,
**kwargs
):
'''
Run the pipeline with hooks and caching at specified positions.
This method allows you to register hooks at certain positions in the pipeline and
cache intermediate inputs and/or outputs at specified positions. Hooks can be used
for inspecting or modifying the pipeline's execution, and caching stores intermediate
values for later inspection or use.
Args:
*args: Arguments to pass to the pipeline.
position_hook_dict Dict[str, Union[Callable, List[Callable]]]:
A dictionary where the keys are the positions in the pipeline, and the values
are hooks (either a single hook or a list of hooks) to be registered at those positions.
Each hook should be a callable that accepts three arguments: (module, input, output).
positions_to_cache (List[str], optional): A list of positions in the pipeline where
intermediate inputs/outputs should be cached. Defaults to an empty list.
save_input (bool, optional): If True, caches the input at each specified position.
Defaults to False.
save_output (bool, optional): If True, caches the output at each specified position.
Defaults to True.
**kwargs: Additional keyword arguments to pass to the pipeline.
Returns:
final_output: The final output of the pipeline after execution.
cache_dict (Dict[str, Dict[str, Any]]): A dictionary where keys are the specified positions
and values are dictionaries containing the cached 'input' and/or 'output' at each position,
depending on the flags `save_input` and `save_output`.
'''
cache_input, cache_output = dict() if save_input else None, dict() if save_output else None
hooks = [
self._register_cache_hook(position, cache_input, cache_output) for position in positions_to_cache
]
for position, hook in position_hook_dict.items():
if isinstance(hook, list):
for h in hook:
hooks.append(self._register_general_hook(position, h))
else:
hooks.append(self._register_general_hook(position, hook))
hooks = [hook for hook in hooks if hook is not None]
output = self.pipe(*args, **kwargs)
for hook in hooks:
hook.remove()
if self.use_hooked_scheduler:
self.pipe.scheduler.pre_hooks = []
self.pipe.scheduler.post_hooks = []
cache_dict = {}
if save_input:
for position, block in cache_input.items():
cache_input[position] = torch.stack(block, dim=1)
cache_dict['input'] = cache_input
if save_output:
for position, block in cache_output.items():
cache_output[position] = torch.stack(block, dim=1)
cache_dict['output'] = cache_output
return output, cache_dict
def _locate_block(self, position: str):
'''
Locate the block at the specified position in the pipeline.
'''
block = self.pipe
for step in position.split('.'):
if step.isdigit():
step = int(step)
block = block[step]
else:
block = getattr(block, step)
return block
def _register_cache_hook(self, position: str, cache_input: Dict, cache_output: Dict):
if position.endswith('$self_attention') or position.endswith('$cross_attention'):
return self._register_cache_attention_hook(position, cache_output)
if position == 'noise':
def hook(model_output, timestep, sample, generator):
if position not in cache_output:
cache_output[position] = []
cache_output[position].append(sample)
if self.use_hooked_scheduler:
self.pipe.scheduler.post_hooks.append(hook)
else:
raise ValueError('Cannot cache noise without using hooked scheduler')
return
block = self._locate_block(position)
def hook(module, input, kwargs, output):
if cache_input is not None:
if position not in cache_input:
cache_input[position] = []
cache_input[position].append(retrieve(input))
if cache_output is not None:
if position not in cache_output:
cache_output[position] = []
cache_output[position].append(retrieve(output))
return block.register_forward_hook(hook, with_kwargs=True)
def _register_cache_attention_hook(self, position, cache):
attn_block = self._locate_block(position.split('$')[0])
if position.endswith('$self_attention'):
attn_block = attn_block.attn1
elif position.endswith('$cross_attention'):
attn_block = attn_block.attn2
else:
raise ValueError('Wrong attention type')
def hook(module, args, kwargs, output):
hidden_states = args[0]
encoder_hidden_states = kwargs['encoder_hidden_states']
attention_mask = kwargs['attention_mask']
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn_block.prepare_attention_mask(attention_mask, sequence_length, batch_size)
query = attn_block.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn_block.norm_cross is not None:
encoder_hidden_states = attn_block.norm_cross(encoder_hidden_states)
key = attn_block.to_k(encoder_hidden_states)
value = attn_block.to_v(encoder_hidden_states)
query = attn_block.head_to_batch_dim(query)
key = attn_block.head_to_batch_dim(key)
value = attn_block.head_to_batch_dim(value)
attention_probs = attn_block.get_attention_scores(query, key, attention_mask)
attention_probs = attention_probs.view(
batch_size,
attention_probs.shape[0] // batch_size,
attention_probs.shape[1],
attention_probs.shape[2]
)
if position not in cache:
cache[position] = []
cache[position].append(attention_probs)
return attn_block.register_forward_hook(hook, with_kwargs=True)
def _register_general_hook(self, position, hook):
if position == 'scheduler_pre':
if not self.use_hooked_scheduler:
raise ValueError('Cannot register hooks on scheduler without using hooked scheduler')
self.pipe.scheduler.pre_hooks.append(hook)
return
elif position == 'scheduler_post':
if not self.use_hooked_scheduler:
raise ValueError('Cannot register hooks on scheduler without using hooked scheduler')
self.pipe.scheduler.post_hooks.append(hook)
return
block = self._locate_block(position)
return block.register_forward_hook(hook)
def to(self, *args, **kwargs):
self.pipe = self.pipe.to(*args, **kwargs)
return self
def __getattr__(self, name):
return getattr(self.pipe, name)
def __setattr__(self, name, value):
return setattr(self.pipe, name, value)
def __call__(self, *args, **kwargs):
return self.pipe(*args, **kwargs)
class HookedStableDiffusionXLPipeline(HookedDiffusionAbstractPipeline):
parent_cls = StableDiffusionXLPipeline
class HookedIFPipeline(HookedDiffusionAbstractPipeline):
parent_cls = IFPipeline
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