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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import os
import random
import re
import numpy as np
import torch
from diffusion.utils.logger import get_root_logger
from tools.download import find_model
def save_checkpoint(
work_dir,
epoch,
model,
model_ema=None,
optimizer=None,
lr_scheduler=None,
generator=torch.Generator(device="cpu").manual_seed(42),
keep_last=False,
step=None,
add_symlink=False,
):
os.makedirs(work_dir, exist_ok=True)
state_dict = dict(state_dict=model.state_dict())
if model_ema is not None:
state_dict["state_dict_ema"] = model_ema.state_dict()
if optimizer is not None:
state_dict["optimizer"] = optimizer.state_dict()
if lr_scheduler is not None:
state_dict["scheduler"] = lr_scheduler.state_dict()
if epoch is not None:
state_dict["epoch"] = epoch
file_path = os.path.join(work_dir, f"epoch_{epoch}.pth")
if step is not None:
file_path = file_path.split(".pth")[0] + f"_step_{step}.pth"
rng_state = {
"torch": torch.get_rng_state(),
"torch_cuda": torch.cuda.get_rng_state_all(),
"numpy": np.random.get_state(),
"python": random.getstate(),
"generator": generator.get_state(),
}
state_dict["rng_state"] = rng_state
logger = get_root_logger()
torch.save(state_dict, file_path)
logger.info(f"Saved checkpoint of epoch {epoch} to {file_path.format(epoch)}.")
if keep_last:
for i in range(epoch):
previous_ckgt = file_path.format(i)
if os.path.exists(previous_ckgt):
os.remove(previous_ckgt)
if add_symlink:
link_path = os.path.join(os.path.dirname(file_path), "latest.pth")
if os.path.exists(link_path) or os.path.islink(link_path):
os.remove(link_path)
os.symlink(os.path.abspath(file_path), link_path)
return file_path
def load_checkpoint(
checkpoint,
model,
model_ema=None,
optimizer=None,
lr_scheduler=None,
load_ema=False,
resume_optimizer=True,
resume_lr_scheduler=True,
null_embed_path=None,
):
assert isinstance(checkpoint, str)
logger = get_root_logger()
ckpt_file = checkpoint
checkpoint = find_model(ckpt_file)
state_dict_keys = ["pos_embed", "base_model.pos_embed", "model.pos_embed"]
for key in state_dict_keys:
if key in checkpoint["state_dict"]:
del checkpoint["state_dict"][key]
if "state_dict_ema" in checkpoint and key in checkpoint["state_dict_ema"]:
del checkpoint["state_dict_ema"][key]
break
if load_ema:
state_dict = checkpoint["state_dict_ema"]
else:
state_dict = checkpoint.get("state_dict", checkpoint) # to be compatible with the official checkpoint
null_embed = torch.load(null_embed_path, map_location="cpu")
state_dict["y_embedder.y_embedding"] = null_embed["uncond_prompt_embeds"][0]
rng_state = checkpoint.get("rng_state", None)
missing, unexpect = model.load_state_dict(state_dict, strict=False)
if model_ema is not None:
model_ema.load_state_dict(checkpoint["state_dict_ema"], strict=False)
if optimizer is not None and resume_optimizer:
optimizer.load_state_dict(checkpoint["optimizer"])
if lr_scheduler is not None and resume_lr_scheduler:
lr_scheduler.load_state_dict(checkpoint["scheduler"])
epoch = 0
if optimizer is not None:
epoch = checkpoint.get("epoch", re.match(r".*epoch_(\d*).*.pth", ckpt_file).group()[0])
logger.info(
f"Resume checkpoint of epoch {epoch} from {ckpt_file}. Load ema: {load_ema}, "
f"resume optimizer： {resume_optimizer}, resume lr scheduler: {resume_lr_scheduler}."
)
return epoch, missing, unexpect, rng_state
logger.info(f"Load checkpoint from {ckpt_file}. Load ema: {load_ema}.")
return epoch, missing, unexpect, rng_state
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