import copy
import torch
from torch import nn
import networks
import tools
to_np = lambda x: x.detach().cpu().numpy()
class RewardEMA:
"""running mean and std"""
def __init__(self, device, alpha=1e-2):
self.device = device
self.alpha = alpha
self.range = torch.tensor([0.05, 0.95]).to(device)
def __call__(self, x, ema_vals):
flat_x = torch.flatten(x.detach())
x_quantile = torch.quantile(input=flat_x, q=self.range)
# this should be in-place operation
ema_vals[:] = self.alpha * x_quantile + (1 - self.alpha) * ema_vals
scale = torch.clip(ema_vals[1] - ema_vals[0], min=1.0)
offset = ema_vals[0]
return offset.detach(), scale.detach()
class WorldModel(nn.Module):
def __init__(self, obs_space, act_space, step, config):
super(WorldModel, self).__init__()
self._step = step
self._use_amp = True if config.precision == 16 else False
self._config = config
shapes = {k: tuple(v.shape) for k, v in obs_space.spaces.items()}
self.encoder = networks.MultiEncoder(shapes, **config.encoder)
self.embed_size = self.encoder.outdim
self.dynamics = networks.RSSM(
config.dyn_stoch,
config.dyn_deter,
config.dyn_hidden,
config.dyn_rec_depth,
config.dyn_discrete,
config.act,
config.norm,
config.dyn_mean_act,
config.dyn_std_act,
config.dyn_min_std,
config.unimix_ratio,
config.initial,
config.num_actions,
self.embed_size,
config.device,
)
self.heads = nn.ModuleDict()
if config.dyn_discrete:
feat_size = config.dyn_stoch * config.dyn_discrete + config.dyn_deter
else:
feat_size = config.dyn_stoch + config.dyn_deter
self.heads["decoder"] = networks.MultiDecoder(
feat_size, shapes, **config.decoder
)
self.heads["reward"] = networks.MLP(
feat_size,
(255,) if config.reward_head["dist"] == "symlog_disc" else (),
config.reward_head["layers"],
config.units,
config.act,
config.norm,
dist=config.reward_head["dist"],
outscale=config.reward_head["outscale"],
device=config.device,
name="Reward",
)
self.heads["cont"] = networks.MLP(
feat_size,
(),
config.cont_head["layers"],
config.units,
config.act,
config.norm,
dist="binary",
outscale=config.cont_head["outscale"],
device=config.device,
name="Cont",
)
for name in config.grad_heads:
assert name in self.heads, name
self._model_opt = tools.Optimizer(
"model",
self.parameters(),
config.model_lr,
config.opt_eps,
config.grad_clip,
config.weight_decay,
opt=config.opt,
use_amp=self._use_amp,
)
print(
f"Optimizer model_opt has {sum(param.numel() for param in self.parameters())} variables."
)
# other losses are scaled by 1.0.
self._scales = dict(
reward=config.reward_head["loss_scale"],
cont=config.cont_head["loss_scale"],
)
def _train(self, data):
# action (batch_size, batch_length, act_dim)
# image (batch_size, batch_length, h, w, ch)
# reward (batch_size, batch_length)
# discount (batch_size, batch_length)
data = self.preprocess(data)
with tools.RequiresGrad(self):
with torch.cuda.amp.autocast(self._use_amp):
embed = self.encoder(data)
post, prior = self.dynamics.observe(
embed, data["action"], data["is_first"]
)
kl_free = self._config.kl_free
dyn_scale = self._config.dyn_scale
rep_scale = self._config.rep_scale
kl_loss, kl_value, dyn_loss, rep_loss = self.dynamics.kl_loss(
post, prior, kl_free, dyn_scale, rep_scale
)
assert kl_loss.shape == embed.shape[:2], kl_loss.shape
preds = {}
for name, head in self.heads.items():
grad_head = name in self._config.grad_heads
feat = self.dynamics.get_feat(post)
feat = feat if grad_head else feat.detach()
pred = head(feat)
if type(pred) is dict:
preds.update(pred)
else:
preds[name] = pred
losses = {}
for name, pred in preds.items():
loss = -pred.log_prob(data[name])
assert loss.shape == embed.shape[:2], (name, loss.shape)
losses[name] = loss
scaled = {
key: value * self._scales.get(key, 1.0)
for key, value in losses.items()
}
model_loss = sum(scaled.values()) + kl_loss
metrics = self._model_opt(torch.mean(model_loss), self.parameters())
metrics.update({f"{name}_loss": to_np(loss) for name, loss in losses.items()})
metrics["kl_free"] = kl_free
metrics["dyn_scale"] = dyn_scale
metrics["rep_scale"] = rep_scale
metrics["dyn_loss"] = to_np(dyn_loss)
metrics["rep_loss"] = to_np(rep_loss)
metrics["kl"] = to_np(torch.mean(kl_value))
with torch.cuda.amp.autocast(self._use_amp):
metrics["prior_ent"] = to_np(
torch.mean(self.dynamics.get_dist(prior).entropy())
)
metrics["post_ent"] = to_np(
torch.mean(self.dynamics.get_dist(post).entropy())
)
context = dict(
embed=embed,
feat=self.dynamics.get_feat(post),
kl=kl_value,
postent=self.dynamics.get_dist(post).entropy(),
)
post = {k: v.detach() for k, v in post.items()}
return post, context, metrics
# this function is called during both rollout and training
def preprocess(self, obs):
obs = obs.copy()
obs["image"] = torch.Tensor(obs["image"]) / 255.0
if "discount" in obs:
obs["discount"] *= self._config.discount
# (batch_size, batch_length) -> (batch_size, batch_length, 1)
obs["discount"] = torch.Tensor(obs["discount"]).unsqueeze(-1)
# 'is_first' is necesarry to initialize hidden state at training
assert "is_first" in obs
# 'is_terminal' is necesarry to train cont_head
assert "is_terminal" in obs
obs["cont"] = torch.Tensor(1.0 - obs["is_terminal"]).unsqueeze(-1)
obs = {k: torch.Tensor(v).to(self._config.device) for k, v in obs.items()}
return obs
def video_pred(self, data):
data = self.preprocess(data)
embed = self.encoder(data)
states, _ = self.dynamics.observe(
embed[:6, :5], data["action"][:6, :5], data["is_first"][:6, :5]
)
recon = self.heads["decoder"](self.dynamics.get_feat(states))["image"].mode()[
:6
]
reward_post = self.heads["reward"](self.dynamics.get_feat(states)).mode()[:6]
init = {k: v[:, -1] for k, v in states.items()}
prior = self.dynamics.imagine_with_action(data["action"][:6, 5:], init)
openl = self.heads["decoder"](self.dynamics.get_feat(prior))["image"].mode()
reward_prior = self.heads["reward"](self.dynamics.get_feat(prior)).mode()
# observed image is given until 5 steps
model = torch.cat([recon[:, :5], openl], 1)
truth = data["image"][:6]
model = model
error = (model - truth + 1.0) / 2.0
return torch.cat([truth, model, error], 2)
class ImagBehavior(nn.Module):
def __init__(self, config, world_model):
super(ImagBehavior, self).__init__()
self._use_amp = True if config.precision == 16 else False
self._config = config
self._world_model = world_model
if config.dyn_discrete:
feat_size = config.dyn_stoch * config.dyn_discrete + config.dyn_deter
else:
feat_size = config.dyn_stoch + config.dyn_deter
self.actor = networks.MLP(
feat_size,
(config.num_actions,),
config.actor["layers"],
config.units,
config.act,
config.norm,
config.actor["dist"],
config.actor["std"],
config.actor["min_std"],
config.actor["max_std"],
absmax=1.0,
temp=config.actor["temp"],
unimix_ratio=config.actor["unimix_ratio"],
outscale=config.actor["outscale"],
name="Actor",
)
self.value = networks.MLP(
feat_size,
(255,) if config.critic["dist"] == "symlog_disc" else (),
config.critic["layers"],
config.units,
config.act,
config.norm,
config.critic["dist"],
outscale=config.critic["outscale"],
device=config.device,
name="Value",
)
if config.critic["slow_target"]:
self._slow_value = copy.deepcopy(self.value)
self._updates = 0
kw = dict(wd=config.weight_decay, opt=config.opt, use_amp=self._use_amp)
self._actor_opt = tools.Optimizer(
"actor",
self.actor.parameters(),
config.actor["lr"],
config.actor["eps"],
config.actor["grad_clip"],
**kw,
)
print(
f"Optimizer actor_opt has {sum(param.numel() for param in self.actor.parameters())} variables."
)
self._value_opt = tools.Optimizer(
"value",
self.value.parameters(),
config.critic["lr"],
config.critic["eps"],
config.critic["grad_clip"],
**kw,
)
print(
f"Optimizer value_opt has {sum(param.numel() for param in self.value.parameters())} variables."
)
if self._config.reward_EMA:
# register ema_vals to nn.Module for enabling torch.save and torch.load
self.register_buffer("ema_vals", torch.zeros((2,)).to(self._config.device))
self.reward_ema = RewardEMA(device=self._config.device)
def _train(
self,
start,
objective,
):
self._update_slow_target()
metrics = {}
with tools.RequiresGrad(self.actor):
with torch.cuda.amp.autocast(self._use_amp):
imag_feat, imag_state, imag_action = self._imagine(
start, self.actor, self._config.imag_horizon
)
reward = objective(imag_feat, imag_state, imag_action)
actor_ent = self.actor(imag_feat).entropy()
state_ent = self._world_model.dynamics.get_dist(imag_state).entropy()
# this target is not scaled by ema or sym_log.
target, weights, base = self._compute_target(
imag_feat, imag_state, reward
)
actor_loss, mets = self._compute_actor_loss(
imag_feat,
imag_action,
target,
weights,
base,
)
actor_loss -= self._config.actor["entropy"] * actor_ent[:-1, ..., None]
actor_loss = torch.mean(actor_loss)
metrics.update(mets)
value_input = imag_feat
with tools.RequiresGrad(self.value):
with torch.cuda.amp.autocast(self._use_amp):
value = self.value(value_input[:-1].detach())
target = torch.stack(target, dim=1)
# (time, batch, 1), (time, batch, 1) -> (time, batch)
value_loss = -value.log_prob(target.detach())
slow_target = self._slow_value(value_input[:-1].detach())
if self._config.critic["slow_target"]:
value_loss -= value.log_prob(slow_target.mode().detach())
# (time, batch, 1), (time, batch, 1) -> (1,)
value_loss = torch.mean(weights[:-1] * value_loss[:, :, None])
metrics.update(tools.tensorstats(value.mode(), "value"))
metrics.update(tools.tensorstats(target, "target"))
metrics.update(tools.tensorstats(reward, "imag_reward"))
if self._config.actor["dist"] in ["onehot"]:
metrics.update(
tools.tensorstats(
torch.argmax(imag_action, dim=-1).float(), "imag_action"
)
)
else:
metrics.update(tools.tensorstats(imag_action, "imag_action"))
metrics["actor_entropy"] = to_np(torch.mean(actor_ent))
with tools.RequiresGrad(self):
metrics.update(self._actor_opt(actor_loss, self.actor.parameters()))
metrics.update(self._value_opt(value_loss, self.value.parameters()))
return imag_feat, imag_state, imag_action, weights, metrics
def _imagine(self, start, policy, horizon):
dynamics = self._world_model.dynamics
flatten = lambda x: x.reshape([-1] + list(x.shape[2:]))
start = {k: flatten(v) for k, v in start.items()}
def step(prev, _):
state, _, _ = prev
feat = dynamics.get_feat(state)
inp = feat.detach()
action = policy(inp).sample()
succ = dynamics.img_step(state, action)
return succ, feat, action
succ, feats, actions = tools.static_scan(
step, [torch.arange(horizon)], (start, None, None)
)
states = {k: torch.cat([start[k][None], v[:-1]], 0) for k, v in succ.items()}
return feats, states, actions
def _compute_target(self, imag_feat, imag_state, reward):
if "cont" in self._world_model.heads:
inp = self._world_model.dynamics.get_feat(imag_state)
discount = self._config.discount * self._world_model.heads["cont"](inp).mean
else:
discount = self._config.discount * torch.ones_like(reward)
value = self.value(imag_feat).mode()
target = tools.lambda_return(
reward[1:],
value[:-1],
discount[1:],
bootstrap=value[-1],
lambda_=self._config.discount_lambda,
axis=0,
)
weights = torch.cumprod(
torch.cat([torch.ones_like(discount[:1]), discount[:-1]], 0), 0
).detach()
return target, weights, value[:-1]
def _compute_actor_loss(
self,
imag_feat,
imag_action,
target,
weights,
base,
):
metrics = {}
inp = imag_feat.detach()
policy = self.actor(inp)
# Q-val for actor is not transformed using symlog
target = torch.stack(target, dim=1)
if self._config.reward_EMA:
offset, scale = self.reward_ema(target, self.ema_vals)
normed_target = (target - offset) / scale
normed_base = (base - offset) / scale
adv = normed_target - normed_base
metrics.update(tools.tensorstats(normed_target, "normed_target"))
metrics["EMA_005"] = to_np(self.ema_vals[0])
metrics["EMA_095"] = to_np(self.ema_vals[1])
if self._config.imag_gradient == "dynamics":
actor_target = adv
elif self._config.imag_gradient == "reinforce":
actor_target = (
policy.log_prob(imag_action)[:-1][:, :, None]
* (target - self.value(imag_feat[:-1]).mode()).detach()
)
elif self._config.imag_gradient == "both":
actor_target = (
policy.log_prob(imag_action)[:-1][:, :, None]
* (target - self.value(imag_feat[:-1]).mode()).detach()
)
mix = self._config.imag_gradient_mix
actor_target = mix * target + (1 - mix) * actor_target
metrics["imag_gradient_mix"] = mix
else:
raise NotImplementedError(self._config.imag_gradient)
actor_loss = -weights[:-1] * actor_target
return actor_loss, metrics
def _update_slow_target(self):
if self._config.critic["slow_target"]:
if self._updates % self._config.critic["slow_target_update"] == 0:
mix = self._config.critic["slow_target_fraction"]
for s, d in zip(self.value.parameters(), self._slow_value.parameters()):
d.data = mix * s.data + (1 - mix) * d.data
self._updates += 1