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train_transformerL.py
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train_transformerL.py
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import os
import matplotlib
if os.path.expandvars("$MACHINE_NAME") in ["leonhard", "euler"]:
matplotlib.use('agg')
import logging
import os
import math
import time
from datetime import datetime
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
import torch.optim as optim
import torch.nn as nn
from torch.utils.data.dataloader import DataLoader
from pyniel.python_tools.path_tools import make_dir_if_not_exists
from strictfire import StrictFire
from navrep.models.gpt import save_checkpoint, set_seed
from navdreams.auto_debug import enable_auto_debug
from navdreams.transformerL import TransformerLWMConf, TransformerLWorldModel, version, _S
from train_gpt import N3DWorldModelDataset, gpt_worldmodel_error
def main(max_steps=222222, dataset="SCR", dry_run=False, gpu=True):
namestring = "TransformerL_V{}".format(version)
START_TIME = datetime.now().strftime("%Y_%m_%d__%H_%M_%S")
discrete_actions = False
if dataset == "SCR":
dataset_dir = [os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/navrep3dalt"),
os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/navrep3dcity"),
os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/navrep3doffice"),
os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/navrep3dasl"),
os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/rosbag")]
log_path = os.path.expanduser(
"~/navdreams_data/wm_experiments/logs/W/{}_SCR_train_log_{}.csv".format(namestring, START_TIME))
checkpoint_path = os.path.expanduser("~/navdreams_data/wm_experiments/models/W/{}_SCR".format(namestring))
plot_path = os.path.expanduser("~/tmp_navrep3d/{}_SCR_step".format(namestring))
elif dataset == "staticasl":
dataset_dir = [os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/navrep3dasl")]
log_path = os.path.expanduser(
"~/navdreams_data/wm_experiments/logs/W/{}_staticasl_train_log_{}.csv".format(namestring, START_TIME))
checkpoint_path = os.path.expanduser("~/navdreams_data/wm_experiments/models/W/{}_staticasl".format(namestring))
plot_path = os.path.expanduser("~/tmp_navrep3d/{}_staticasl_step".format(namestring))
elif dataset == "Salt":
dataset_dir = [os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/navrep3dalt")]
log_path = os.path.expanduser(
"~/navdreams_data/wm_experiments/logs/W/{}_Salt_train_log_{}.csv".format(namestring, START_TIME))
checkpoint_path = os.path.expanduser("~/navdreams_data/wm_experiments/models/W/{}_Salt".format(namestring))
plot_path = os.path.expanduser("~/tmp_navrep3d/{}_Salt_step".format(namestring))
elif dataset == "dSalt":
dataset_dir = [os.path.expanduser("~/navdreams_data/wm_experiments/datasets/V/discrete_navrep3dalt")]
log_path = os.path.expanduser(
"~/navdreams_data/wm_experiments/logs/W/{}_dSalt_train_log_{}.csv".format(namestring, START_TIME))
checkpoint_path = os.path.expanduser("~/navdreams_data/wm_experiments/models/W/{}_dSalt".format(namestring))
plot_path = os.path.expanduser("~/tmp_navrep3d/{}_dSalt_step".format(namestring))
discrete_actions = True
else:
raise NotImplementedError(dataset)
if dry_run:
log_path = log_path.replace(os.path.expanduser("~/navdreams_data"), "/tmp/navdreams_data")
checkpoint_path = checkpoint_path.replace(os.path.expanduser("~/navdreams_data"), "/tmp/navdreams_data")
make_dir_if_not_exists(os.path.dirname(checkpoint_path))
make_dir_if_not_exists(os.path.dirname(log_path))
make_dir_if_not_exists(os.path.expanduser("~/tmp_navrep3d"))
# make deterministic
set_seed(42)
# set up logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger = logging.getLogger(__name__)
mconf = TransformerLWMConf()
if discrete_actions:
mconf.n_action = 4 # actions passed directly as onehot
train_dataset = N3DWorldModelDataset(
dataset_dir, _S,
pre_convert_obs=False,
regen=dataset,
lidar_mode="images",
)
if dry_run:
train_dataset._partial_regen()
# training params
# optimization parameters
max_epochs = max_steps # don't stop based on epoch
batch_size = 64
learning_rate = 6e-4
betas = (0.9, 0.95)
grad_norm_clip = 1.0
lr_decay = True # learning rate decay params: linear warmup followed by cosine decay to 10% of original
weight_decay = 0.1 # only applied on matmul weights
warmup_tokens = 512 * 20
final_tokens = 200 * len(train_dataset) * _S
num_workers = 0 # for DataLoader
# create model
model = TransformerLWorldModel(mconf, gpu=gpu)
print("TransformerL trainable params: {}".format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
# increase stddev in random model weights
if dataset == "Random":
def randomize_weights(module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.2)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
model.apply(randomize_weights)
# take over whatever gpus are on the system
device = "cpu"
if torch.cuda.is_available():
device = torch.cuda.current_device()
model = torch.nn.DataParallel(model).to(device)
# create the optimizer
no_decay = ["bias", "LayerNorm.weight"]
params_decay = [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
]
params_nodecay = [
p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)
]
optim_groups = [
{"params": params_decay, "weight_decay": weight_decay},
{"params": params_nodecay, "weight_decay": 0.0},
]
optimizer = optim.AdamW(optim_groups, lr=learning_rate, betas=betas)
if mconf.amp:
from torch.cuda.amp import GradScaler
optimizer = torch.optim.AdamW(model.parameters(), lr=mconf.adam_lr, eps=mconf.adam_eps)
scaler = GradScaler(enabled=mconf.amp)
global_step = 0
tokens = 0 # counter used for learning rate decay
values_logs = None
start = time.time()
for epoch in range(max_epochs):
is_train = True
model.train(is_train)
loader = DataLoader(
train_dataset,
shuffle=is_train,
batch_size=batch_size,
num_workers=num_workers,
)
losses = []
pbar = tqdm(enumerate(loader), total=len(loader)) if is_train else enumerate(loader)
for it, (x, a, y, x_rs, y_rs, dones) in pbar:
global_step += 1
# place data on the correct device
x = x.to(device)
x_rs = x_rs.to(device)
a = a.to(device)
y = y.to(device)
y_rs = y_rs.to(device)
dones = dones.to(device)
# forward the model
with torch.set_grad_enabled(is_train):
y_pred, y_rs_pred, loss = model(x, x_rs, a, dones, targets=(y, y_rs))
loss = (
loss.mean()
) # collapse all losses if they are scattered on multiple gpus
losses.append(loss.item())
if is_train:
# backprop and update the parameters
if mconf.amp:
optimizer.zero_grad()
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), mconf.grad_clip)
scaler.step(optimizer)
scaler.update()
lr = mconf.adam_lr
else:
model.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), grad_norm_clip)
optimizer.step()
# decay the learning rate based on our progress
if lr_decay:
tokens += (
a.shape[0] * a.shape[1]
) # number of tokens processed this step
if tokens < warmup_tokens:
# linear warmup
lr_mult = float(tokens) / float(max(1, warmup_tokens))
else:
# cosine learning rate decay
progress = float(tokens - warmup_tokens) / float(
max(1, final_tokens - warmup_tokens)
)
lr_mult = max(0.1, 0.5 * (1.0 + math.cos(math.pi * progress)))
lr = learning_rate * lr_mult
for param_group in optimizer.param_groups:
param_group["lr"] = lr
else:
lr = learning_rate
# report progress
pbar.set_description(
f"epoch {epoch}: train loss {loss.item():.5f}. lr {lr:e}"
)
if global_step == 1 or global_step % 1000 == 0:
# save plot
from matplotlib import pyplot as plt
plt.figure("training_status")
plt.clf()
plt.suptitle("training step {}".format(global_step))
f, axes = plt.subplots(3, 5, num="training_status", sharex=True, sharey=True)
for i, (ax0, ax1, ax2) in enumerate(axes.T):
ax0.imshow(np.moveaxis(x.cpu().numpy()[0, 5 + i], 0, -1))
ax1.imshow(np.moveaxis(y.cpu().numpy()[0, 5 + i], 0, -1))
ax2.imshow(np.moveaxis(y_pred.detach().cpu().numpy()[0, 5 + i], 0, -1))
ax2.set_xlabel("Done {}".format(dones.cpu()[0, 5 + 1]))
plt.savefig(plot_path + "{:07}.png".format(global_step))
lidar_e = None
state_e = None
if epoch % 20 == 0:
lidar_e, state_e = gpt_worldmodel_error(model, dataset_dir, device, batch_size)
save_checkpoint(model, checkpoint_path)
# log
end = time.time()
time_taken = end - start
start = time.time()
values_log = pd.DataFrame(
[[global_step, loss.item(), lidar_e, state_e, time_taken]],
columns=["step", "cost", "lidar_test_error", "state_test_error", "train_time_taken"],
)
if values_logs is None:
values_logs = values_log.copy()
else:
values_logs = values_logs.append(values_log, ignore_index=True)
if log_path is not None:
values_logs.to_csv(log_path)
if not is_train:
logger.info("test loss: %f", np.mean(losses))
if global_step >= max_steps:
break
print("Final evaluation")
lidar_e, state_e = gpt_worldmodel_error(model, dataset_dir, device)
save_checkpoint(model, checkpoint_path)
if __name__ == "__main__":
enable_auto_debug()
StrictFire(main)