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import contextlib
import copy
import random
from typing import Any, Dict, Iterable, List, Optional, Union
import numpy as np
import torch
from torchvision import transforms
from .models import UNet2DConditionModel
from .utils import (
convert_state_dict_to_diffusers,
convert_state_dict_to_peft,
deprecate,
is_peft_available,
is_transformers_available,
)
if is_transformers_available():
import transformers
if is_peft_available():
from peft import set_peft_model_state_dict
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
def compute_snr(noise_scheduler, timesteps):
"""
Computes SNR as per
https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
"""
alphas_cumprod = noise_scheduler.alphas_cumprod
sqrt_alphas_cumprod = alphas_cumprod**0.5
sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
# Expand the tensors.
# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
# Compute SNR.
snr = (alpha / sigma) ** 2
return snr
def resolve_interpolation_mode(interpolation_type: str):
"""
Maps a string describing an interpolation function to the corresponding torchvision `InterpolationMode` enum. The
full list of supported enums is documented at
https://pytorch.org/vision/0.9/transforms.html#torchvision.transforms.functional.InterpolationMode.
Args:
interpolation_type (`str`):
A string describing an interpolation method. Currently, `bilinear`, `bicubic`, `box`, `nearest`,
`nearest_exact`, `hamming`, and `lanczos` are supported, corresponding to the supported interpolation modes
in torchvision.
Returns:
`torchvision.transforms.InterpolationMode`: an `InterpolationMode` enum used by torchvision's `resize`
transform.
"""
if interpolation_type == "bilinear":
interpolation_mode = transforms.InterpolationMode.BILINEAR
elif interpolation_type == "bicubic":
interpolation_mode = transforms.InterpolationMode.BICUBIC
elif interpolation_type == "box":
interpolation_mode = transforms.InterpolationMode.BOX
elif interpolation_type == "nearest":
interpolation_mode = transforms.InterpolationMode.NEAREST
elif interpolation_type == "nearest_exact":
interpolation_mode = transforms.InterpolationMode.NEAREST_EXACT
elif interpolation_type == "hamming":
interpolation_mode = transforms.InterpolationMode.HAMMING
elif interpolation_type == "lanczos":
interpolation_mode = transforms.InterpolationMode.LANCZOS
else:
raise ValueError(
f"The given interpolation mode {interpolation_type} is not supported. Currently supported interpolation"
f" modes are `bilinear`, `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`."
)
return interpolation_mode
def unet_lora_state_dict(unet: UNet2DConditionModel) -> Dict[str, torch.Tensor]:
r"""
Returns:
A state dict containing just the LoRA parameters.
"""
lora_state_dict = {}
for name, module in unet.named_modules():
if hasattr(module, "set_lora_layer"):
lora_layer = getattr(module, "lora_layer")
if lora_layer is not None:
current_lora_layer_sd = lora_layer.state_dict()
for lora_layer_matrix_name, lora_param in current_lora_layer_sd.items():
# The matrix name can either be "down" or "up".
lora_state_dict[f"{name}.lora.{lora_layer_matrix_name}"] = lora_param
return lora_state_dict
def cast_training_params(model: Union[torch.nn.Module, List[torch.nn.Module]], dtype=torch.float32):
if not isinstance(model, list):
model = [model]
for m in model:
for param in m.parameters():
# only upcast trainable parameters into fp32
if param.requires_grad:
param.data = param.to(dtype)
def _set_state_dict_into_text_encoder(
lora_state_dict: Dict[str, torch.Tensor], prefix: str, text_encoder: torch.nn.Module
):
"""
Sets the `lora_state_dict` into `text_encoder` coming from `transformers`.
Args:
lora_state_dict: The state dictionary to be set.
prefix: String identifier to retrieve the portion of the state dict that belongs to `text_encoder`.
text_encoder: Where the `lora_state_dict` is to be set.
"""
text_encoder_state_dict = {
f'{k.replace(prefix, "")}': v for k, v in lora_state_dict.items() if k.startswith(prefix)
}
text_encoder_state_dict = convert_state_dict_to_peft(convert_state_dict_to_diffusers(text_encoder_state_dict))
set_peft_model_state_dict(text_encoder, text_encoder_state_dict, adapter_name="default")
# 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
context_manager = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params, parameters):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled():
context_manager = deepspeed.zero.GatheredParameters(param, modifier_rank=None)
with context_manager():
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")
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