diffusers_plus_plus/src/diffusers/training_utils.py at main · ModelsLab/diffusers_plus_plus · GitHub

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import contextlib

import copy

import gc

import math

import random

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

import numpy as np

import torch

from .models import UNet2DConditionModel

from .schedulers import SchedulerMixin

from .utils import (

convert_state_dict_to_diffusers,

convert_state_dict_to_peft,

deprecate,

is_peft_available,

is_torch_npu_available,

is_torchvision_available,

is_transformers_available,

)

if is_transformers_available():

import transformers

if is_peft_available():

from peft import set_peft_model_state_dict

if is_torchvision_available():

from torchvision import transforms

if is_torch_npu_available():

import torch_npu # noqa: F401

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)

if is_torch_npu_available():

torch.npu.manual_seed_all(seed)

else:

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 not is_torchvision_available():

raise ImportError(

"Please make sure to install `torchvision` to be able to use the `resolve_interpolation_mode()` function."

)

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 compute_dream_and_update_latents(

unet: UNet2DConditionModel,

noise_scheduler: SchedulerMixin,

timesteps: torch.Tensor,

noise: torch.Tensor,

noisy_latents: torch.Tensor,

target: torch.Tensor,

encoder_hidden_states: torch.Tensor,

dream_detail_preservation: float = 1.0,

) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:

"""

Implements "DREAM (Diffusion Rectification and Estimation-Adaptive Models)" from http://arxiv.org/abs/2312.00210.

DREAM helps align training with sampling to help training be more efficient and accurate at the cost of an extra

forward step without gradients.

Args:

`unet`: The state unet to use to make a prediction.

`noise_scheduler`: The noise scheduler used to add noise for the given timestep.

`timesteps`: The timesteps for the noise_scheduler to user.

`noise`: A tensor of noise in the shape of noisy_latents.

`noisy_latents`: Previously noise latents from the training loop.

`target`: The ground-truth tensor to predict after eps is removed.

`encoder_hidden_states`: Text embeddings from the text model.

`dream_detail_preservation`: A float value that indicates detail preservation level.

See reference.

Returns:

`tuple[torch.Tensor, torch.Tensor]`: Adjusted noisy_latents and target.

"""

alphas_cumprod = noise_scheduler.alphas_cumprod.to(timesteps.device)[timesteps, None, None, None]

sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5

# The paper uses lambda = sqrt(1 - alpha) ** p, with p = 1 in their experiments.

dream_lambda = sqrt_one_minus_alphas_cumprod**dream_detail_preservation

pred = None

with torch.no_grad():

pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample

_noisy_latents, _target = (None, None)

if noise_scheduler.config.prediction_type == "epsilon":

predicted_noise = pred

delta_noise = (noise - predicted_noise).detach()

delta_noise.mul_(dream_lambda)

_noisy_latents = noisy_latents.add(sqrt_one_minus_alphas_cumprod * delta_noise)

_target = target.add(delta_noise)

elif noise_scheduler.config.prediction_type == "v_prediction":

raise NotImplementedError("DREAM has not been implemented for v-prediction")

else:

raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")

return _noisy_latents, _target

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")

def compute_density_for_timestep_sampling(

weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None

"""Compute the density for sampling the timesteps when doing SD3 training.

Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.

SD3 paper reference: https://arxiv.org/abs/2403.03206v1.

"""

if weighting_scheme == "logit_normal":

# See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).

u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")

u = torch.nn.functional.sigmoid(u)

elif weighting_scheme == "mode":

u = torch.rand(size=(batch_size,), device="cpu")

u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)

else:

u = torch.rand(size=(batch_size,), device="cpu")

return u

def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):

"""Computes loss weighting scheme for SD3 training.

Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.

SD3 paper reference: https://arxiv.org/abs/2403.03206v1.

"""

if weighting_scheme == "sigma_sqrt":

weighting = (sigmas**-2.0).float()

elif weighting_scheme == "cosmap":

bot = 1 - 2 * sigmas + 2 * sigmas**2

weighting = 2 / (math.pi * bot)

else:

weighting = torch.ones_like(sigmas)

return weighting

def clear_objs_and_retain_memory(objs: List[Any]):

"""Deletes `objs` and runs garbage collection. Then clears the cache of the available accelerator."""

if len(objs) >= 1:

for obj in objs:

del obj

gc.collect()

if torch.cuda.is_available():

torch.cuda.empty_cache()

elif torch.backends.mps.is_available():

torch.mps.empty_cache()

elif is_torch_npu_available():

torch_npu.empty_cache()

# 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,

foreach: bool = False,

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.

foreach (bool): Use torch._foreach functions for updating shadow parameters. Should be faster.

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.foreach = foreach

self.model_cls = model_cls

self.model_config = model_config

@classmethod

def from_pretrained(cls, path, model_cls, foreach=False) -> "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, foreach=foreach)

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.integrations.deepspeed.is_deepspeed_zero3_enabled():

import deepspeed

if self.foreach:

if is_transformers_available() and transformers.integrations.deepspeed.is_deepspeed_zero3_enabled():

context_manager = deepspeed.zero.GatheredParameters(parameters, modifier_rank=None)

with context_manager():

params_grad = [param for param in parameters if param.requires_grad]

s_params_grad = [

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

]

if len(params_grad) < len(parameters):

torch._foreach_copy_(

[s_param for s_param, param in zip(self.shadow_params, parameters) if not param.requires_grad],

[param for param in parameters if not param.requires_grad],

non_blocking=True,

)

torch._foreach_sub_(

s_params_grad, torch._foreach_sub(s_params_grad, params_grad), alpha=one_minus_decay

)

else:

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

if is_transformers_available() and transformers.integrations.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)

if self.foreach:

torch._foreach_copy_(

[param.data for param in parameters],

[s_param.to(param.device).data for s_param, param in zip(self.shadow_params, parameters)],

)

else:

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

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

def pin_memory(self) -> None:

r"""

Move internal buffers of the ExponentialMovingAverage to pinned memory. Useful for non-blocking transfers for

offloading EMA params to the host.

"""

self.shadow_params = [p.pin_memory() for p in self.shadow_params]

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

if p.is_floating_point()

else p.to(device=device, non_blocking=non_blocking)

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()`")

if self.foreach:

torch._foreach_copy_(

[param.data for param in parameters], [c_param.data for c_param in self.temp_stored_params]

)

else:

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_plus_plus/src/diffusers/training_utils.py at main · ModelsLab/diffusers_plus_plus · GitHub

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