diffusers/src/diffusers/training_utils.py at patch-2 · themrzmaster/diffusers · GitHub

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import copy

import os

import random

from typing import Any, Dict, Iterable, Optional, Union

import numpy as np

import torch

from .utils import deprecate

def enable_full_determinism(seed: int):

"""

Helper function for reproducible behavior during distributed training. See

- https://pytorch.org/docs/stable/notes/randomness.html for pytorch

"""

# set seed first

set_seed(seed)

# Enable PyTorch deterministic mode. This potentially requires either the environment

# variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,

# depending on the CUDA version, so we set them both here

os.environ["CUDA_LAUNCH_BLOCKING"] = "1"

os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"

torch.use_deterministic_algorithms(True)

# Enable CUDNN deterministic mode

torch.backends.cudnn.deterministic = True

torch.backends.cudnn.benchmark = False

def set_seed(seed: int):

"""

Args:

Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.

seed (`int`): The seed to set.

"""

random.seed(seed)

np.random.seed(seed)

torch.manual_seed(seed)

torch.cuda.manual_seed_all(seed)

# ^^ safe to call this function even if cuda is not available

# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14

class EMAModel:

"""

Exponential Moving Average of models weights

"""

def __init__(

self,

parameters: Iterable[torch.nn.Parameter],

decay: float = 0.9999,

min_decay: float = 0.0,

update_after_step: int = 0,

use_ema_warmup: bool = False,

inv_gamma: Union[float, int] = 1.0,

power: Union[float, int] = 2 / 3,

model_cls: Optional[Any] = None,

model_config: Dict[str, Any] = None,

**kwargs,

"""

Args:

parameters (Iterable[torch.nn.Parameter]): The parameters to track.

decay (float): The decay factor for the exponential moving average.

min_decay (float): The minimum decay factor for the exponential moving average.

update_after_step (int): The number of steps to wait before starting to update the EMA weights.

use_ema_warmup (bool): Whether to use EMA warmup.

inv_gamma (float):

Inverse multiplicative factor of EMA warmup. Default: 1. Only used if `use_ema_warmup` is True.

power (float): Exponential factor of EMA warmup. Default: 2/3. Only used if `use_ema_warmup` is True.

device (Optional[Union[str, torch.device]]): The device to store the EMA weights on. If None, the EMA

weights will be stored on CPU.

@crowsonkb's notes on EMA Warmup:

If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan

to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),

gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999

at 215.4k steps).

"""

if isinstance(parameters, torch.nn.Module):

deprecation_message = (

"Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "

"Please pass the parameters of the module instead."

)

deprecate(

"passing a `torch.nn.Module` to `ExponentialMovingAverage`",

"1.0.0",

deprecation_message,

standard_warn=False,

)

parameters = parameters.parameters()

# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility

use_ema_warmup = True

if kwargs.get("max_value", None) is not None:

deprecation_message = "The `max_value` argument is deprecated. Please use `decay` instead."

deprecate("max_value", "1.0.0", deprecation_message, standard_warn=False)

decay = kwargs["max_value"]

if kwargs.get("min_value", None) is not None:

deprecation_message = "The `min_value` argument is deprecated. Please use `min_decay` instead."

deprecate("min_value", "1.0.0", deprecation_message, standard_warn=False)

min_decay = kwargs["min_value"]

parameters = list(parameters)

self.shadow_params = [p.clone().detach() for p in parameters]

if kwargs.get("device", None) is not None:

deprecation_message = "The `device` argument is deprecated. Please use `to` instead."

deprecate("device", "1.0.0", deprecation_message, standard_warn=False)

self.to(device=kwargs["device"])

self.temp_stored_params = None

self.decay = decay

self.min_decay = min_decay

self.update_after_step = update_after_step

self.use_ema_warmup = use_ema_warmup

self.inv_gamma = inv_gamma

self.power = power

self.optimization_step = 0

self.cur_decay_value = None # set in `step()`

self.model_cls = model_cls

self.model_config = model_config

@classmethod

def from_pretrained(cls, path, model_cls) -> "EMAModel":

_, ema_kwargs = model_cls.load_config(path, return_unused_kwargs=True)

model = model_cls.from_pretrained(path)

ema_model = cls(model.parameters(), model_cls=model_cls, model_config=model.config)

ema_model.load_state_dict(ema_kwargs)

return ema_model

def save_pretrained(self, path):

if self.model_cls is None:

raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__.")

if self.model_config is None:

raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__.")

model = self.model_cls.from_config(self.model_config)

state_dict = self.state_dict()

state_dict.pop("shadow_params", None)

model.register_to_config(**state_dict)

self.copy_to(model.parameters())

model.save_pretrained(path)

def get_decay(self, optimization_step: int) -> float:

"""

Compute the decay factor for the exponential moving average.

"""

step = max(0, optimization_step - self.update_after_step - 1)

if step <= 0:

return 0.0

if self.use_ema_warmup:

cur_decay_value = 1 - (1 + step / self.inv_gamma) ** -self.power

else:

cur_decay_value = (1 + step) / (10 + step)

cur_decay_value = min(cur_decay_value, self.decay)

# make sure decay is not smaller than min_decay

cur_decay_value = max(cur_decay_value, self.min_decay)

return cur_decay_value

@torch.no_grad()

def step(self, parameters: Iterable[torch.nn.Parameter]):

if isinstance(parameters, torch.nn.Module):

deprecation_message = (

"Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "

"Please pass the parameters of the module instead."

)

deprecate(

"passing a `torch.nn.Module` to `ExponentialMovingAverage.step`",

"1.0.0",

deprecation_message,

standard_warn=False,

)

parameters = parameters.parameters()

parameters = list(parameters)

self.optimization_step += 1

# Compute the decay factor for the exponential moving average.

decay = self.get_decay(self.optimization_step)

self.cur_decay_value = decay

one_minus_decay = 1 - decay

for s_param, param in zip(self.shadow_params, parameters):

if param.requires_grad:

s_param.sub_(one_minus_decay * (s_param - param))

else:

s_param.copy_(param)

def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:

"""

Copy current averaged parameters into given collection of parameters.

Args:

parameters: Iterable of `torch.nn.Parameter`; the parameters to be

updated with the stored moving averages. If `None`, the parameters with which this

`ExponentialMovingAverage` was initialized will be used.

"""

parameters = list(parameters)

for s_param, param in zip(self.shadow_params, parameters):

param.data.copy_(s_param.to(param.device).data)

def to(self, device=None, dtype=None) -> None:

r"""Move internal buffers of the ExponentialMovingAverage to `device`.

Args:

device: like `device` argument to `torch.Tensor.to`

"""

# .to() on the tensors handles None correctly

self.shadow_params = [

p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)

for p in self.shadow_params

]

def state_dict(self) -> dict:

r"""

Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during

checkpointing to save the ema state dict.

"""

# Following PyTorch conventions, references to tensors are returned:

# "returns a reference to the state and not its copy!" -

# https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict

return {

"decay": self.decay,

"min_decay": self.min_decay,

"optimization_step": self.optimization_step,

"update_after_step": self.update_after_step,

"use_ema_warmup": self.use_ema_warmup,

"inv_gamma": self.inv_gamma,

"power": self.power,

"shadow_params": self.shadow_params,

}

def store(self, parameters: Iterable[torch.nn.Parameter]) -> None:

r"""

Args:

Save the current parameters for restoring later.

parameters: Iterable of `torch.nn.Parameter`; the parameters to be

temporarily stored.

"""

self.temp_stored_params = [param.detach().cpu().clone() for param in parameters]

def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:

r"""

Args:

Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without:

affecting the original optimization process. Store the parameters before the `copy_to()` method. After

validation (or model saving), use this to restore the former parameters.

parameters: Iterable of `torch.nn.Parameter`; the parameters to be

updated with the stored parameters. If `None`, the parameters with which this

`ExponentialMovingAverage` was initialized will be used.

"""

if self.temp_stored_params is None:

raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`")

for c_param, param in zip(self.temp_stored_params, parameters):

param.data.copy_(c_param.data)

# Better memory-wise.

self.temp_stored_params = None

def load_state_dict(self, state_dict: dict) -> None:

r"""

Args:

Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the

ema state dict.

state_dict (dict): EMA state. Should be an object returned

from a call to :meth:`state_dict`.

"""

# deepcopy, to be consistent with module API

state_dict = copy.deepcopy(state_dict)

self.decay = state_dict.get("decay", self.decay)

if self.decay < 0.0 or self.decay > 1.0:

raise ValueError("Decay must be between 0 and 1")

self.min_decay = state_dict.get("min_decay", self.min_decay)

if not isinstance(self.min_decay, float):

raise ValueError("Invalid min_decay")

self.optimization_step = state_dict.get("optimization_step", self.optimization_step)

if not isinstance(self.optimization_step, int):

raise ValueError("Invalid optimization_step")

self.update_after_step = state_dict.get("update_after_step", self.update_after_step)

if not isinstance(self.update_after_step, int):

raise ValueError("Invalid update_after_step")

self.use_ema_warmup = state_dict.get("use_ema_warmup", self.use_ema_warmup)

if not isinstance(self.use_ema_warmup, bool):

raise ValueError("Invalid use_ema_warmup")

self.inv_gamma = state_dict.get("inv_gamma", self.inv_gamma)

if not isinstance(self.inv_gamma, (float, int)):

raise ValueError("Invalid inv_gamma")

self.power = state_dict.get("power", self.power)

if not isinstance(self.power, (float, int)):

raise ValueError("Invalid power")

shadow_params = state_dict.get("shadow_params", None)

if shadow_params is not None:

self.shadow_params = shadow_params

if not isinstance(self.shadow_params, list):

raise ValueError("shadow_params must be a list")

if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):

raise ValueError("shadow_params must all be Tensors")

diffusers/src/diffusers/training_utils.py at patch-2 · themrzmaster/diffusers · GitHub

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