import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os
os.environ['MKL_SERVICE_FORCE_INTEL'] = '1'
from pathlib import Path
from collections import defaultdict
import hydra
import numpy as np
import torch
import wandb
from dm_env import specs
import tools.utils as utils
from tools.logger import Logger
from tools.replay import ReplayBuffer, make_replay_loader
torch.backends.cudnn.benchmark = True
def make_agent(obs_type, obs_spec, action_spec, num_expl_steps, cfg):
cfg.obs_type = obs_type
cfg.obs_shape = obs_spec.shape
cfg.action_shape = action_spec.shape
cfg.num_expl_steps = num_expl_steps
return hydra.utils.instantiate(cfg)
def make_dreamer_agent(obs_space, action_spec, cur_config, cfg):
from copy import deepcopy
cur_config = deepcopy(cur_config)
if hasattr(cur_config, 'agent'):
del cur_config.agent
return hydra.utils.instantiate(cfg, cfg=cur_config, obs_space=obs_space, act_spec=action_spec)
class Workspace:
def __init__(self, cfg, savedir=None, workdir=None,):
self.workdir = Path.cwd() if workdir is None else workdir
print(f'workspace: {self.workdir}')
self.cfg = cfg
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
# create logger
self.logger = Logger(self.workdir,
use_tb=cfg.use_tb,
use_wandb=cfg.use_wandb)
# create envs
self.task = task = cfg.task
img_size = cfg.img_size
import envs.main as envs
self.train_env = envs.make(task, cfg.obs_type, cfg.action_repeat, cfg.seed, img_size=img_size, viclip_encode=cfg.viclip_encode, clip_hd_rendering=cfg.clip_hd_rendering)
# # create agent
sample_agent = make_dreamer_agent(self.train_env.obs_space, self.train_env.act_space['action'], cfg, cfg.agent)
# create replay buffer
data_specs = (self.train_env.obs_space,
self.train_env.act_space,
specs.Array((1,), np.float32, 'reward'),
specs.Array((1,), np.float32, 'discount'))
if cfg.train_from_data:
# Loading replay buffer
if cfg.replay_from_wandb_project is not None:
api = wandb.Api()
project_name = cfg.replay_from_wandb_project
params2search = {
"task" : cfg.task if cfg.task_snapshot is None else cfg.task_snapshot,
"seed" : cfg.seed if cfg.seed_snapshot is None else cfg.seed_snapshot,
}
runs = api.runs(f"PUT_YOUR_USER_HERE/{project_name}")
found = False
for run in runs:
if np.all([ v == run.config.get(k, None) for k,v in params2search.items()]):
found = True
found_path = Path(run.config['workdir'].replace('/code', ''))
break
if not found:
raise Exception("Replay from wandb buffer not found")
replay_dir = found_path / 'code' / 'buffer'
else:
replay_dir = Path(cfg.replay_load_dir)
# create data storage
self.replay_storage = ReplayBuffer(data_specs, [],
replay_dir,
length=cfg.batch_length, **cfg.replay,
device=cfg.device, ignore_extra_keys=True, load_recursive=True)
print('Loaded ', self.replay_storage._loaded_episodes, 'episodes from ', str(replay_dir))
# create replay buffer
self.replay_loader = make_replay_loader(self.replay_storage,
cfg.batch_size,)
self._replay_iter = None
# Loading snapshot
if cfg.snapshot_from_wandb_project is not None:
api = wandb.Api()
project_name = cfg.snapshot_from_wandb_project
params2search = {
"task" : cfg.task if cfg.task_snapshot is None else cfg.task_snapshot,
"agent_name" : cfg.agent.name if cfg.agent_name_snapshot is None else cfg.agent_name_snapshot,
"seed" : cfg.seed if cfg.seed_snapshot is None else cfg.seed_snapshot,
}
if cfg.agent.clip_lafite_noise > 0.:
params2search['clip_lafite_noise'] = cfg.agent.clip_lafite_noise
if cfg.agent.clip_add_noise > 0.:
params2search['clip_add_noise'] = cfg.agent.clip_add_noise
if cfg.reset_connector:
del params2search['clip_add_noise']
runs = api.runs(f"PUT_YOUR_USER_HERE/{project_name}")
found = False
for run in runs:
if np.all([ v == run.config.get(k, None) for k,v in params2search.items()]):
found = True
found_path = Path(run.config['workdir'].replace('/code', ''))
break
if not found:
raise Exception("Snapshot from wandb not found")
if cfg.snapshot_step is None:
snapshot_dir = found_path / 'code' / 'last_snapshot.pt'
else:
snapshot_dir = found_path / 'code' / f'snapshot_{cfg.snapshot_step}.pt'
elif cfg.snapshot_load_dir is not None:
snapshot_dir = Path(cfg.snapshot_load_dir)
else:
snapshot_dir = None
if snapshot_dir is not None:
self.load_snapshot(snapshot_dir, resume=False)
if self.cfg.reset_world_model:
self.agent.wm = sample_agent.wm
# To reset optimization
from agent import dreamer_utils as common
self.agent.wm.model_opt = common.Optimizer('model', self.agent.wm.parameters(), **self.agent.wm.cfg.model_opt, use_amp=self.agent.wm._use_amp)
if self.cfg.reset_connector:
self.agent.wm.connector = sample_agent.wm.connector
# To reset optimization
from agent import dreamer_utils as common
self.agent.wm.model_opt = common.Optimizer('model', self.agent.wm.parameters(), **self.agent.wm.cfg.model_opt, use_amp=self.agent.wm._use_amp)
# overwriting cfg
self.agent.cfg = sample_agent.cfg
self.agent.wm.cfg = sample_agent.wm.cfg
if self.cfg.reset_imag_behavior:
self.agent.instantiate_imag_behavior()
else:
self.agent = sample_agent
self.eval_env = envs.make(self.task, self.cfg.obs_type, self.cfg.action_repeat, self.cfg.seed, img_size=64, )
if hasattr(self.eval_env, 'eval_mode'):
self.eval_env.eval_mode()
eval_specs = (self.eval_env.obs_space,
self.eval_env.act_space,
specs.Array((1,), np.float32, 'reward'),
specs.Array((1,), np.float32, 'discount'))
self.eval_storage = ReplayBuffer(eval_specs, {},
self.workdir / 'eval_buffer',
length=cfg.batch_length, **cfg.replay,
device=cfg.device, ignore_extra_keys=True,)
self.eval_storage._minlen = 1
self.timer = utils.Timer()
self._global_step = 0
self._global_episode = 0
@property
def global_step(self):
return self._global_step
@property
def global_episode(self):
return self._global_episode
@property
def global_frame(self):
return self.global_step * self.cfg.action_repeat
@property
def replay_iter(self):
if self._replay_iter is None:
self._replay_iter = iter(self.replay_loader)
return self._replay_iter
def eval(self):
import envs.main as envs
eval_until_episode = utils.Until(self.cfg.num_eval_episodes)
episode_reward = []
while eval_until_episode(len(episode_reward)):
if len(episode_reward) > 0 and self.global_step == 0:
return
episode_reward.append(0)
step, episode = 0, defaultdict(list)
meta = self.agent.init_meta()
time_step, dreamer_obs = self.eval_env.reset()
data = dreamer_obs
if 'clip_video' in data:
del data['clip_video']
self.eval_storage.add(data, meta)
agent_state = None
while not time_step.last():
with torch.no_grad(), utils.eval_mode(self.agent):
action, agent_state = self.agent.act(dreamer_obs,
meta,
self.global_step,
eval_mode=True,
state=agent_state)
time_step, dreamer_obs = self.eval_env.step(action)
for k in dreamer_obs:
episode[k].append(dreamer_obs[k])
episode_reward[-1] += time_step.reward
if time_step.last():
if episode_reward[-1] == np.max(episode_reward):
best_episode = {**episode}
if episode_reward[-1] == np.min(episode_reward):
worst_episode = {**episode}
data = dreamer_obs
if 'clip_video' in data:
del data['clip_video']
self.eval_storage.add(data, meta)
step += 1
if self.global_step > 0 and self.global_frame % self.cfg.log_episodes_every_frames == 0:
# B, T, C, H, W = video.shape
videos = {'best_episode' : np.stack(best_episode['observation'], axis=0),
'worst_episode' : np.stack(worst_episode['observation'], axis=0),}
self.logger.log_visual(videos, self.global_frame)
with self.logger.log_and_dump_ctx(self.global_frame, ty='eval') as log:
log('episode_reward', np.mean(episode_reward))
log('episode_length', step * self.cfg.action_repeat)
log('episode', self.global_episode)
log('step', self.global_step)
def eval_imag_behavior(self,):
self.agent._backup_acting_behavior = self.agent._acting_behavior
self.agent._acting_behavior = self.agent._imag_behavior
self.eval()
self.agent._acting_behavior = self.agent._backup_acting_behavior
def train(self):
# predicates
train_until_step = utils.Until(self.cfg.num_train_frames, 1)
eval_every_step = utils.Every(self.cfg.eval_every_frames, 1)
should_log_scalars = utils.Every(self.cfg.log_every_frames, 1)
should_save_model = utils.Every(self.cfg.save_every_frames, 1)
should_log_visual = utils.Every(self.cfg.visual_every_frames, 1)
metrics = None
while train_until_step(self.global_step):
# try to evaluate
if eval_every_step(self.global_step):
if self.cfg.eval_modality == 'task':
self.eval()
if self.cfg.eval_modality == 'task_imag':
self.eval_imag_behavior()
if self.cfg.eval_modality == 'from_text':
self.logger.log('eval_total_time', self.timer.total_time(), self.global_frame)
self.eval_from_text()
if self.cfg.train_from_data:
# Sampling data
batch_data = next(self.replay_iter)
if self.cfg.train_world_model:
state, outputs, metrics = self.agent.update_wm(batch_data, self.global_step)
else:
with torch.no_grad():
outputs, metrics = self.agent.wm.observe_data(batch_data,)
if self.cfg.train_connector:
_, metrics = self.agent.wm.update_additional_detached_modules(batch_data, outputs, metrics)
else:
imag_warmup_steps = self.cfg.imag_warmup_steps
metrics, batch_data = {}, None
with torch.no_grad():
# fake actions
mix = self.cfg.mix_random_actions
random = False
# num warmup steps
if mix:
init = self.agent.wm.rssm.initial(self.cfg.batch_size * (self.cfg.batch_length // 2))
else:
init = self.agent.wm.rssm.initial(self.cfg.batch_size * self.cfg.batch_length)
unif_dist = self.agent.wm.rssm.get_unif_dist(init)
if 'logit' in init:
init['logit'] = unif_dist.mean
else:
init['mean'] = unif_dist.mean
init['std'] = unif_dist.std
init['stoch'] = unif_dist.sample()
if self.cfg.start_from_video in [True, 'mix']:
T = self.agent.wm.connector.n_frames * 2 # should this be an hyperparam?
B = init['deter'].shape[0] // T
text_feat_dim = self.agent.wm.connector.viclip_emb_dim
video_embed = torch.randn((B, T, text_feat_dim), device=self.agent.device)
video_embed = torch.nn.functional.normalize(video_embed, dim=-1)
# Get initial state
video_init = self.agent.wm.connector.video_imagine(video_embed, dreamer_init=None, sample=True, reset_every_n_frames=False, denoise=True)
video_init = { k : v.reshape(B * T, *v.shape[2:]) for k, v in video_init.items()}
if self.cfg.start_from_video == 'mix':
probs = torch.rand((B * T, 1,1), device=init['stoch'].device) > 0.5 # should this be an hyperparam?
init['stoch'] = (probs * init['stoch']) + ( (~probs) * video_init['stoch'] )
else:
init['stoch'] = video_init['stoch']
if random:
fake_action = torch.rand(self.cfg.batch_size * self.cfg.batch_length, imag_warmup_steps, self.agent.act_dim, device=self.agent.device) * 2 - 1
post = self.agent.wm.rssm.imagine(fake_action, init, sample=True)
post = { k : v[:, -1].reshape([self.cfg.batch_size, self.cfg.batch_length, ] + list(v.shape[2:])) for k,v in post.items() }
elif mix:
fake_action = torch.rand(self.cfg.batch_size * self.cfg.batch_length // 2, imag_warmup_steps, self.agent.act_dim, device=self.agent.device) * 2 - 1
post1 = self.agent.wm.rssm.imagine(fake_action, init, sample=True)
post1 = { k : v[:, -1].reshape([self.cfg.batch_size, self.cfg.batch_length // 2, ] + list(v.shape[2:])) for k,v in post1.items() }
init2 = { k : v.reshape([self.cfg.batch_size, self.cfg.batch_length // 2, ] + list(v.shape[1:])) for k,v in init.items() }
post2 = self.agent.wm.imagine(self.agent._imag_behavior.actor, init2, None, imag_warmup_steps)
post2 = { k : v[-1, :].reshape([self.cfg.batch_size, self.cfg.batch_length // 2, ] + list(v.shape[2:])) for k,v in post2.items() }
post = { k: torch.cat([post1[k], post2[k]], dim=1) for k in post1 }
else:
init = { k : v.reshape([self.cfg.batch_size, self.cfg.batch_length, ] + list(v.shape[1:])) for k,v in init.items() }
post = self.agent.wm.imagine(self.agent._imag_behavior.actor, init, None, imag_warmup_steps)
post = { k : v[-1, :].reshape([self.cfg.batch_size, self.cfg.batch_length, ] + list(v.shape[2:])) for k,v in post.items() }
is_terminal = torch.zeros(self.cfg.batch_size, self.cfg.batch_length, device=self.agent.device)
outputs = dict(post=post, is_terminal=is_terminal)
if getattr(self.cfg.agent, 'imag_reward_fn', None) is not None:
metrics.update(self.agent.update_imag_behavior(state=None, outputs=outputs, metrics=metrics, seq_data=batch_data,)[1])
if self.global_step > 0:
if should_log_scalars(self.global_step):
if hasattr(self, 'replay_storage'):
metrics.update(self.replay_storage.stats)
self.logger.log_metrics(metrics, self.global_frame, ty='train')
if should_log_visual(self.global_step) and self.cfg.train_from_data and hasattr(self.agent, 'report'):
with torch.no_grad(), utils.eval_mode(self.agent):
videos = self.agent.report(next(self.replay_iter))
self.logger.log_visual(videos, self.global_frame)
if should_log_scalars(self.global_step):
elapsed_time, total_time = self.timer.reset()
with self.logger.log_and_dump_ctx(self.global_frame, ty='train') as log:
log('fps', self.cfg.log_every_frames / elapsed_time)
log('step', self.global_step)
if 'model_loss' in metrics:
log('episode_reward', metrics['model_loss'].item())
# save last model
if should_save_model(self.global_step):
self.save_last_model()
self._global_step += 1
# == 1000 is to make sure everything is going well since the start
if (self.global_frame == 1000) or (self.global_frame % self.cfg.snapshot_every_frames == 0):
self.save_snapshot()
@utils.retry
def save_snapshot(self):
snapshot = self.root_dir / f'snapshot_{self.global_frame}.pt'
keys_to_save = ['agent', '_global_step', '_global_episode']
payload = {k: self.__dict__[k] for k in keys_to_save}
with snapshot.open('wb') as f:
torch.save(payload, f)
def setup_wandb(self):
cfg = self.cfg
exp_name = '_'.join([
cfg.experiment, cfg.agent.name, cfg.task, cfg.obs_type,
str(cfg.seed)
])
wandb.init(project=cfg.project_name, group=cfg.agent.name, name=exp_name)
flat_cfg = utils.flatten_dict(cfg)
wandb.config.update(flat_cfg)
self.wandb_run_id = wandb.run.id
@utils.retry
def save_last_model(self):
snapshot = self.root_dir / 'last_snapshot.pt'
if snapshot.is_file():
temp = Path(str(snapshot).replace("last_snapshot.pt", "second_last_snapshot.pt"))
os.replace(snapshot, temp)
keys_to_save = ['agent', '_global_step', '_global_episode']
if self.cfg.use_wandb:
keys_to_save.append('wandb_run_id')
payload = {k: self.__dict__[k] for k in keys_to_save}
with snapshot.open('wb') as f:
torch.save(payload, f)
@utils.retry
def load_snapshot(self, snapshot_dir, resume=True):
print('Loading snapshot from: ', str(snapshot_dir))
try:
snapshot = snapshot_dir / 'last_snapshot.pt' if resume else snapshot_dir
with snapshot.open('rb') as f:
payload = torch.load(f)
except:
snapshot = Path(str(snapshot_dir).replace('last_snapshot', 'second_last_snapshot'))
with snapshot.open('rb') as f:
payload = torch.load(f)
if type(payload) != dict:
self.agent = payload
self.agent.requires_grad_(requires_grad=False)
return
for k,v in payload.items():
setattr(self, k, v)
if k == 'wandb_run_id' and resume:
assert wandb.run is None
cfg = self.cfg
exp_name = '_'.join([
cfg.experiment, cfg.agent.name, cfg.task, cfg.obs_type,
str(cfg.seed)
])
wandb.init(project=cfg.project_name, group=cfg.agent.name, name=exp_name, id=v, resume="must")
def get_snapshot_dir(self):
snap_dir = self.cfg.snapshot_dir
snapshot_dir = self.workdir / Path(snap_dir)
snapshot_dir.mkdir(exist_ok=True, parents=True)
return snapshot_dir
def start_training(cfg, savedir, workdir):
from train import Workspace as W
root_dir = Path.cwd()
cfg.workdir = str(root_dir)
workspace = W(cfg, savedir, workdir)
workspace.root_dir = root_dir
snapshot = workspace.root_dir / 'last_snapshot.pt'
if snapshot.exists():
print(f'resuming: {snapshot}')
workspace.load_snapshot(workspace.root_dir)
if cfg.use_wandb and wandb.run is None:
# otherwise it was resumed
workspace.setup_wandb()
workspace.train()
@hydra.main(config_path='.', config_name='train')
def main(cfg):
start_training(cfg, None, None)
if __name__ == '__main__':
main()