157 lines
6.6 KiB
Python
157 lines
6.6 KiB
Python
from __future__ import annotations
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from typing import Callable
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class CallbacksMP:
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ON_CLONE = "on_clone"
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ON_LOAD = "on_load_after"
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ON_DETACH = "on_detach_after"
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ON_CLEANUP = "on_cleanup"
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ON_PRE_RUN = "on_pre_run"
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ON_PREPARE_STATE = "on_prepare_state"
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ON_APPLY_HOOKS = "on_apply_hooks"
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ON_REGISTER_ALL_HOOK_PATCHES = "on_register_all_hook_patches"
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ON_INJECT_MODEL = "on_inject_model"
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ON_EJECT_MODEL = "on_eject_model"
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# callbacks dict is in the format:
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# {"call_type": {"key": [Callable1, Callable2, ...]} }
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@classmethod
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def init_callbacks(cls) -> dict[str, dict[str, list[Callable]]]:
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return {}
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def add_callback(call_type: str, callback: Callable, transformer_options: dict, is_model_options=False):
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add_callback_with_key(call_type, None, callback, transformer_options, is_model_options)
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def add_callback_with_key(call_type: str, key: str, callback: Callable, transformer_options: dict, is_model_options=False):
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if is_model_options:
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transformer_options = transformer_options.setdefault("transformer_options", {})
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callbacks: dict[str, dict[str, list]] = transformer_options.setdefault("callbacks", {})
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c = callbacks.setdefault(call_type, {}).setdefault(key, [])
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c.append(callback)
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def get_callbacks_with_key(call_type: str, key: str, transformer_options: dict, is_model_options=False):
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if is_model_options:
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transformer_options = transformer_options.get("transformer_options", {})
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c_list = []
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callbacks: dict[str, list] = transformer_options.get("callbacks", {})
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c_list.extend(callbacks.get(call_type, {}).get(key, []))
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return c_list
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def get_all_callbacks(call_type: str, transformer_options: dict, is_model_options=False):
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if is_model_options:
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transformer_options = transformer_options.get("transformer_options", {})
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c_list = []
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callbacks: dict[str, list] = transformer_options.get("callbacks", {})
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for c in callbacks.get(call_type, {}).values():
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c_list.extend(c)
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return c_list
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class WrappersMP:
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OUTER_SAMPLE = "outer_sample"
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SAMPLER_SAMPLE = "sampler_sample"
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CALC_COND_BATCH = "calc_cond_batch"
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APPLY_MODEL = "apply_model"
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DIFFUSION_MODEL = "diffusion_model"
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# wrappers dict is in the format:
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# {"wrapper_type": {"key": [Callable1, Callable2, ...]} }
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@classmethod
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def init_wrappers(cls) -> dict[str, dict[str, list[Callable]]]:
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return {}
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def add_wrapper(wrapper_type: str, wrapper: Callable, transformer_options: dict, is_model_options=False):
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add_wrapper_with_key(wrapper_type, None, wrapper, transformer_options, is_model_options)
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def add_wrapper_with_key(wrapper_type: str, key: str, wrapper: Callable, transformer_options: dict, is_model_options=False):
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if is_model_options:
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transformer_options = transformer_options.setdefault("transformer_options", {})
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wrappers: dict[str, dict[str, list]] = transformer_options.setdefault("wrappers", {})
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w = wrappers.setdefault(wrapper_type, {}).setdefault(key, [])
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w.append(wrapper)
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def get_wrappers_with_key(wrapper_type: str, key: str, transformer_options: dict, is_model_options=False):
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if is_model_options:
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transformer_options = transformer_options.get("transformer_options", {})
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w_list = []
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wrappers: dict[str, list] = transformer_options.get("wrappers", {})
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w_list.extend(wrappers.get(wrapper_type, {}).get(key, []))
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return w_list
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def get_all_wrappers(wrapper_type: str, transformer_options: dict, is_model_options=False):
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if is_model_options:
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transformer_options = transformer_options.get("transformer_options", {})
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w_list = []
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wrappers: dict[str, list] = transformer_options.get("wrappers", {})
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for w in wrappers.get(wrapper_type, {}).values():
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w_list.extend(w)
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return w_list
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class WrapperExecutor:
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"""Handles call stack of wrappers around a function in an ordered manner."""
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def __init__(self, original: Callable, class_obj: object, wrappers: list[Callable], idx: int):
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# NOTE: class_obj exists so that wrappers surrounding a class method can access
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# the class instance at runtime via executor.class_obj
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self.original = original
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self.class_obj = class_obj
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self.wrappers = wrappers.copy()
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self.idx = idx
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self.is_last = idx == len(wrappers)
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def __call__(self, *args, **kwargs):
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"""Calls the next wrapper or original function, whichever is appropriate."""
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new_executor = self._create_next_executor()
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return new_executor.execute(*args, **kwargs)
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def execute(self, *args, **kwargs):
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"""Used to initiate executor internally - DO NOT use this if you received executor in wrapper."""
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args = list(args)
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kwargs = dict(kwargs)
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if self.is_last:
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return self.original(*args, **kwargs)
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return self.wrappers[self.idx](self, *args, **kwargs)
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def _create_next_executor(self) -> 'WrapperExecutor':
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new_idx = self.idx + 1
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if new_idx > len(self.wrappers):
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raise Exception(f"Wrapper idx exceeded available wrappers; something went very wrong.")
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if self.class_obj is None:
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return WrapperExecutor.new_executor(self.original, self.wrappers, new_idx)
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return WrapperExecutor.new_class_executor(self.original, self.class_obj, self.wrappers, new_idx)
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@classmethod
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def new_executor(cls, original: Callable, wrappers: list[Callable], idx=0):
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return cls(original, class_obj=None, wrappers=wrappers, idx=idx)
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@classmethod
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def new_class_executor(cls, original: Callable, class_obj: object, wrappers: list[Callable], idx=0):
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return cls(original, class_obj, wrappers, idx=idx)
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class PatcherInjection:
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def __init__(self, inject: Callable, eject: Callable):
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self.inject = inject
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self.eject = eject
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def copy_nested_dicts(input_dict: dict):
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new_dict = input_dict.copy()
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for key, value in input_dict.items():
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if isinstance(value, dict):
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new_dict[key] = copy_nested_dicts(value)
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elif isinstance(value, list):
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new_dict[key] = value.copy()
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return new_dict
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def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True):
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if copy_dict1:
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merged_dict = copy_nested_dicts(dict1)
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else:
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merged_dict = dict1
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for key, value in dict2.items():
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if isinstance(value, dict):
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curr_value = merged_dict.setdefault(key, {})
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merged_dict[key] = merge_nested_dicts(value, curr_value)
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elif isinstance(value, list):
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merged_dict.setdefault(key, []).extend(value)
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else:
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merged_dict[key] = value
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return merged_dict
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