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import sys, os, json
root = os.sep + os.sep.join(__file__.split(os.sep)[1:__file__.split(os.sep).index("Recurrent-Parameter-Generation")+1])
sys.path.append(root)
os.chdir(root)
with open("./workspace/config.json", "r") as f:
additional_config = json.load(f)
USE_WANDB = additional_config["use_wandb"]
# set global seed
import random
import numpy as np
import torch
seed = SEED = 999
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
np.random.seed(seed)
random.seed(seed)
# other
import math
import random
import warnings
from _thread import start_new_thread
warnings.filterwarnings("ignore", category=UserWarning)
if USE_WANDB: import wandb
# torch
import torch
import torch.nn as nn
import bitsandbytes.optim as optim
from torch.nn import functional as F
from torch.cuda.amp import autocast
# model
from model import MambaDiffusion as Model
from model.diffusion import DDPMSampler, DDIMSampler
from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR
from accelerate.utils import DistributedDataParallelKwargs
from accelerate.utils import AutocastKwargs
from accelerate import Accelerator
# dataset
from dataset import Permutation_ViTTiny as Dataset
from torch.utils.data import DataLoader
config = {
"seed": SEED,
# dataset setting
"dataset": Dataset,
"dim_per_token": 8192,
"sequence_length": 'auto',
# train setting
"batch_size": 8,
"num_workers": 8,
"total_steps": 120000,
"learning_rate": 0.00003,
"weight_decay": 0.0,
"save_every": 120000//25,
"print_every": 50,
"autocast": lambda i: 5000 < i < 45000,
"checkpoint_save_path": "./checkpoint",
# test setting
"test_batch_size": 1, # fixed, don't change this
"generated_path": Dataset.generated_path,
"test_command": Dataset.test_command,
# to log
"model_config": {
"num_permutation": 20,
# mamba config
"d_condition": 1,
"d_model": 8192,
"d_state": 128,
"d_conv": 4,
"expand": 2,
"num_layers": 2,
# diffusion config
"diffusion_batch": 512,
"layer_channels": [1, 32, 64, 128, 64, 32, 1],
"model_dim": "auto",
"condition_dim": "auto",
"kernel_size": 7,
"sample_mode": DDPMSampler,
"beta": (0.0001, 0.02),
"T": 1000,
"forward_once": True,
},
"tag": "permutation_20",
}
# Data
print('==> Preparing data..')
train_set = config["dataset"](dim_per_token=config["dim_per_token"])
print("Dataset length:", train_set.real_length)
print("input shape:", train_set[0][0].shape)
if config["model_config"]["num_permutation"] == "auto":
config["model_config"]["num_permutation"] = train_set.max_permutation_state
if config["model_config"]["condition_dim"] == "auto":
config["model_config"]["condition_dim"] = config["model_config"]["d_model"]
if config["model_config"]["model_dim"] == "auto":
config["model_config"]["model_dim"] = config["dim_per_token"]
if config["sequence_length"] == "auto":
config["sequence_length"] = train_set.sequence_length
print(f"sequence length: {config['sequence_length']}")
else: # set fixed sequence_length
assert train_set.sequence_length == config["sequence_length"], f"sequence_length={train_set.sequence_length}"
train_loader = DataLoader(
dataset=train_set,
batch_size=config["batch_size"],
num_workers=config["num_workers"],
persistent_workers=True,
drop_last=True,
shuffle=True,
)
# Model
print('==> Building model..')
Model.config = config["model_config"]
model = Model(
sequence_length=config["sequence_length"],
positional_embedding=train_set.get_position_embedding(
positional_embedding_dim=config["model_config"]["d_model"]
) # positional_embedding
) # model setting is in model
# Optimizer
print('==> Building optimizer..')
optimizer = optim.AdamW8bit(
params=model.parameters(),
lr=config["learning_rate"],
weight_decay=config["weight_decay"],
)
scheduler = CosineAnnealingLR(
optimizer=optimizer,
T_max=config["total_steps"],
)
# accelerator
if __name__ == "__main__":
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(kwargs_handlers=[kwargs,])
model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader)
# wandb
if __name__ == "__main__" and USE_WANDB and accelerator.is_main_process:
wandb.login(key=additional_config["wandb_api_key"])
wandb.init(project="Recurrent-Parameter-Generation", name=config['tag'], config=config,)
# Training
print('==> Defining training..')
def train():
if not USE_WANDB:
train_loss = 0
this_steps = 0
print("==> Start training..")
model.train()
for batch_idx, (param, permutation_state) in enumerate(train_loader):
optimizer.zero_grad()
# train
# noinspection PyArgumentList
with accelerator.autocast(autocast_handler=AutocastKwargs(enabled=config["autocast"](batch_idx))):
loss = model(output_shape=param.shape, x_0=param, permutation_state=permutation_state)
accelerator.backward(loss)
optimizer.step()
if accelerator.is_main_process:
scheduler.step()
# to logging losses and print and save
if USE_WANDB and accelerator.is_main_process:
wandb.log({"train_loss": loss.item()})
elif USE_WANDB:
pass # don't print
else: # not use wandb
train_loss += loss.item()
this_steps += 1
if this_steps % config["print_every"] == 0:
print('Loss: %.6f' % (train_loss/this_steps))
this_steps = 0
train_loss = 0
if batch_idx % config["save_every"] == 0 and accelerator.is_main_process:
os.makedirs(config["checkpoint_save_path"], exist_ok=True)
state = accelerator.unwrap_model(model).state_dict()
torch.save(state, os.path.join(config["checkpoint_save_path"], config["tag"]+".pth"))
generate(save_path=config["generated_path"], need_test=True)
if batch_idx >= config["total_steps"]:
break
def generate(save_path=config["generated_path"], need_test=True):
print("\n==> Generating..")
model.eval()
with torch.no_grad():
prediction = model(sample=True)
generated_norm = prediction.abs().mean()
print("Generated_norm:", generated_norm.item())
if USE_WANDB:
wandb.log({"generated_norm": generated_norm.item()})
train_set.save_params(prediction, save_path=save_path)
if need_test:
start_new_thread(os.system, (config["test_command"],))
model.train()
return prediction
if __name__ == '__main__':
train()
del train_loader # deal problems by dataloader
print("Finished Training!")
exit(0) |