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tools.py
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tools.py
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import datetime
import io
import json
import pathlib
import pickle
import re
import time
import uuid
import warnings
import base64
import numpy as np
import tensorflow as tf
import tensorflow.compat.v1 as tf1
import tensorflow_probability as tfp
from tensorflow.keras.mixed_precision import experimental as prec
from tensorflow_probability import distributions as tfd
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
from PIL import Image
# Patch to ignore seed to avoid synchronization across GPUs.
_orig_random_categorical = tf.random.categorical
def random_categorical(*args, **kwargs):
kwargs["seed"] = None
return _orig_random_categorical(*args, **kwargs)
tf.random.categorical = random_categorical
# Patch to ignore seed to avoid synchronization across GPUs.
_orig_random_normal = tf.random.normal
def random_normal(*args, **kwargs):
kwargs["seed"] = None
return _orig_random_normal(*args, **kwargs)
tf.random.normal = random_normal
class AttrDict(dict):
__setattr__ = dict.__setitem__
__getattr__ = dict.__getitem__
class Module(tf.Module):
def save(self, filename):
values = tf.nest.map_structure(lambda x: x.numpy(), self.variables)
amount = len(tf.nest.flatten(values))
count = int(sum(np.prod(x.shape) for x in tf.nest.flatten(values)))
tqdm.write(f"Save checkpoint with {amount} tensors and {count} parameters.")
with pathlib.Path(filename).open("wb") as f:
pickle.dump(values, f)
def load(self, filename):
with pathlib.Path(filename).open("rb") as f:
values = pickle.load(f)
amount = len(tf.nest.flatten(values))
count = int(sum(np.prod(x.shape) for x in tf.nest.flatten(values)))
tqdm.write(f"Load checkpoint with {amount} tensors and {count} parameters.")
tf.nest.map_structure(lambda x, y: x.assign(y), self.variables, values)
def get(self, name, ctor, *args, **kwargs):
# Create or get layer by name to avoid mentioning it in the constructor.
if not hasattr(self, "_modules"):
self._modules = {}
if name not in self._modules:
self._modules[name] = ctor(*args, **kwargs)
return self._modules[name]
class BaseClass(Module):
@tf.function
def observe(self, embed, action, state=None):
swap = lambda x: tf.transpose(x, [1, 0] + list(range(2, len(x.shape))))
if state is None:
state = self.initial(tf.shape(action)[0])
embed, action = swap(embed), swap(action)
post, prior = static_scan(
lambda prev, inputs: self.obs_step(prev[0], *inputs),
(action, embed), (state, state))
post = {k: swap(v) for k, v in post.items()}
prior = {k: swap(v) for k, v in prior.items()}
return post, prior
@tf.function
def imagine(self, action, state=None):
swap = lambda x: tf.transpose(x, [1, 0] + list(range(2, len(x.shape))))
if state is None:
state = self.initial(tf.shape(action)[0])
assert isinstance(state, dict), state
action = swap(action)
prior = static_scan(self.img_step, action, state)
prior = {k: swap(v) for k, v in prior.items()}
return prior
def sparsity_loss(self, post, prior_prob, free, scale):
kld = tfd.kl_divergence
u_post = tfd.Independent(
BernoulliDist(probs=tf.cast(post["u_logit"], tf.float32)), 1
)
u_prior = tfd.Independent(
BernoulliDist(probs=tf.ones(post["u_logit"].shape) * prior_prob), 1
)
loss = kld(u_post, u_prior)
loss = tf.maximum(tf.reduce_mean(loss), free)
return loss * scale
def kl_post_loss(self, post, scale):
kld = tfd.kl_divergence
dist = lambda x: self.get_dist(x, tf.float32)
prior_dist = tfd.MultivariateNormalDiag(
tf.zeros(post["mean"].shape), tf.ones(post["std"].shape)
)
loss = kld(dist(post), prior_dist)
return tf.reduce_mean(loss) * scale
def kl_loss(self, post, prior, forward, balance, free, scale, mask_kl=False):
kld = tfd.kl_divergence
dist = lambda x: self.get_dist(x, tf.float32)
sg = lambda x: tf.nest.map_structure(tf.stop_gradient, x)
lhs, rhs = (prior, post) if forward else (post, prior)
mix = balance if forward else (1 - balance)
#FIXME: change the 0.5 case
if balance == 0.5:
value = kld(dist(lhs), dist(rhs))
loss = tf.reduce_mean(tf.maximum(value, free))
else:
if mask_kl:
value_lhs = value = tf.reduce_sum(
tf.cast(post["u_sample"], tf.float32)
* kld(dist(lhs), dist(sg(rhs))),
axis=-1,
)
value_rhs = tf.reduce_sum(
tf.cast(post["u_sample"], tf.float32)
* kld(dist(sg(lhs)), dist(rhs)),
axis=-1,
)
else:
value_lhs = value = tf.reduce_sum(
kld(dist(lhs), dist(sg(rhs))), axis=-1
)
value_rhs = tf.reduce_sum(
kld(dist(sg(lhs)), dist(rhs)), axis=-1)
loss_lhs = tf.maximum(tf.reduce_mean(value_lhs), free)
loss_rhs = tf.maximum(tf.reduce_mean(value_rhs), free)
loss = mix * loss_lhs + (1 - mix) * loss_rhs
loss *= scale
return loss, value
def var_nest_names(nest):
if isinstance(nest, dict):
items = " ".join(f"{k}:{var_nest_names(v)}" for k, v in nest.items())
return "{" + items + "}"
if isinstance(nest, (list, tuple)):
items = " ".join(var_nest_names(v) for v in nest)
return "[" + items + "]"
if hasattr(nest, "name") and hasattr(nest, "shape"):
return nest.name + str(nest.shape).replace(", ", "x")
if hasattr(nest, "shape"):
return str(nest.shape).replace(", ", "x")
return "?"
def compute_scores(percents):
# Geometric mean with an offset of 1%.
with warnings.catch_warnings(): # Empty seeds become NaN.
warnings.simplefilter('ignore', category=RuntimeWarning)
scores = np.exp(np.nanmean(np.log(1 + percents), -1)) - 1
return scores
class Logger:
def __init__(self, logdir, step, only_eval=False):
self._logdir = logdir if not only_eval else logdir / 'eval_data'
self._writer = tf.summary.create_file_writer(str(logdir), max_queue=1000)
self._last_step = None
self._last_time = None
self._scalars = {}
self._images = {}
self._videos = {}
self.step = step
self.video_step = 0
self.only_eval = only_eval
def scalar(self, name, value):
if name in self._scalars:
if type(self._scalars[name]) != list:
old_v = self._scalars[name]
self._scalars[name] = [old_v]
self._scalars[name].append(value)
else:
self._scalars[name] = float(value)
def image(self, name, value):
self._images[name] = np.array(value)
img_dir = self._logdir / 'images'
img_dir.mkdir(parents=True, exist_ok=True)
if value.shape[-1] == 1:
value = np.array(value).squeeze(-1)
im = Image.fromarray(np.clip(255 * value[0], 0, 255).astype(np.uint8))
im.save(img_dir/f'{name}.png')
def plot(self, name, figure):
buf = io.BytesIO()
image_dir = self._logdir / 'plots'
image_dir.mkdir(parents=True, exist_ok=True)
plt.savefig(image_dir / f'{name}.png')
plt.savefig(buf, format='png')
plt.close(figure)
buf.seek(0)
image = tf.image.decode_png(buf.getvalue(), channels=4)
image = tf.expand_dims(image, 0)
self.image(name, image)
def video(self, name, value):
if not self.only_eval:
self._videos[name] = np.array(value)
else:
if name in self._videos:
print("Concating video")
self._videos[name] = np.concatenate([self._videos[name], np.array(value)], 0)
else:
print("No Concat")
self._videos[name] = np.array(value)
def write(self, fps=False):
scalars = list(self._scalars.items())
if fps:
scalars.append(("fps", self._compute_fps(self.step)))
message = f"[{self.step}]", " / ".join(f"{k} {np.mean(v):.1f}" for k, v in scalars)
tqdm.write(str(message))
if not self.only_eval:
with (self._logdir / "metrics.jsonl").open("a") as f:
f.write(json.dumps({"step": self.step, **dict(scalars)}) + "\n")
with self._writer.as_default():
if not self.only_eval:
for name, value in scalars:
if type(value) == list:
value = np.mean(value)
tf.summary.scalar("scalars/" + name, value, self.step)
for name, value in self._images.items():
tf.summary.image(name, value, self.step)
for name, value in self._videos.items():
video_summary(name, value, self.step, logdir=None, only_eval=self.only_eval)
video_summary(name, value, self.step, logdir=self._logdir, only_eval=self.only_eval)
self.video_step = (self.video_step + 1) % 3
self._writer.flush()
self._scalars = {}
self._images = {}
self._videos = {}
def _compute_fps(self, step):
if self._last_step is None:
self._last_time = time.time()
self._last_step = step
return 0
steps = step - self._last_step
duration = time.time() - self._last_time
self._last_time += duration
self._last_step = step
return steps / duration
def graph_summary(writer, step, fn, *args):
def inner(*args):
tf.summary.experimental.set_step(step.numpy().item())
with writer.as_default():
fn(*args)
return tf.numpy_function(inner, args, [])
def video_summary(name, video, step=None, fps=20, logdir=None, only_eval=False):
name = name if isinstance(name, str) else name.decode("utf-8")
if np.issubdtype(video.dtype, np.floating):
video = np.clip(255 * video, 0, 255).astype(np.uint8)
B, T, H, W, C = video.shape
try:
frames = video.transpose((1, 2, 0, 3, 4)).reshape((T, H, B * W, C))
if logdir is not None:
video_dir = logdir / 'gifs'
video_dir.mkdir(parents=True, exist_ok=True)
filename = video_dir / f'{name}.gif'
write_gif_to_disk(frames, filename)
elif not only_eval:
summary = tf1.Summary()
image = tf1.Summary.Image(height=B * H, width=T * W, colorspace=C)
# image.encoded_image_string = encode_gif(frames, fps)
base64_frames = encode_gif(frames, fps)
image.encoded_image_string = base64_frames
summary.value.add(tag=name, image=image)
tf.summary.experimental.write_raw_pb(summary.SerializeToString(), step)
except (IOError, OSError) as e:
print("GIF summaries require ffmpeg in $PATH.", e)
frames = video.transpose((0, 2, 1, 3, 4)).reshape((1, B * H, T * W, C))
tf.summary.image(name, frames, step)
def write_gif_to_disk(frames, filename):
# frames = frames.transpose((0, 3, 1, 2))
try:
imgs = [Image.fromarray(img) for img in frames]
imgs[0].save(filename, save_all=True, append_images=imgs[1:], duration=50, loop=0)
tqdm.write(f"GIF saved to {filename}")
except Exception as e:
print(frames.shape)
print("GIF Saving failed.", e)
def encode_gif(frames, fps):
from subprocess import Popen, PIPE
h, w, c = frames[0].shape
pxfmt = {1: "gray", 3: "rgb24"}[c]
cmd = " ".join(
[
f"ffmpeg -y -f rawvideo -vcodec rawvideo",
f"-r {fps:.02f} -s {w}x{h} -pix_fmt {pxfmt} -i - -filter_complex",
f"[0:v]split[x][z];[z]palettegen[y];[x]fifo[x];[x][y]paletteuse",
f"-r {fps:.02f} -f gif -",
]
)
proc = Popen(cmd.split(" "), stdin=PIPE, stdout=PIPE, stderr=PIPE)
for image in frames:
proc.stdin.write(image.tostring())
out, err = proc.communicate()
if proc.returncode:
raise IOError("\n".join([" ".join(cmd), err.decode("utf8")]))
del proc
return out
def create_update_plot(update, plot_title):
figure = plt.figure(figsize=(10, 5))
update_sum = update[0].numpy().sum(-1)
plt.plot(np.arange(len(update_sum)), update_sum)
plt.xlabel("Steps")
plt.ylabel("No. of Updates")
plt.xlim((0, len(update_sum)))
plt.title(plot_title)
plt.grid()
plt.tight_layout()
# plt.legend()
return figure
def simulate(agent, envs, steps=0, episodes=0, state=None):
# Initialize or unpack simulation state.
if state is None:
step, episode = 0, 0
done = np.ones(len(envs), np.bool)
length = np.zeros(len(envs), np.int32)
obs = [None] * len(envs)
agent_state = None
else:
step, episode, done, length, obs, agent_state = state
while (steps and step < steps) or (episodes and episode < episodes):
# Reset envs if necessary.
if done.any():
indices = [index for index, d in enumerate(done) if d]
results = [envs[i].reset() for i in indices]
for index, result in zip(indices, results):
obs[index] = result
# Step agents.
obs = {k: np.stack([o[k] for o in obs]) for k in obs[0]}
action, agent_state = agent(obs, done, agent_state)
if isinstance(action, dict):
action = [
{k: np.array(action[k][i]) for k in action} for i in range(len(envs))
]
else:
action = np.array(action)
assert len(action) == len(envs)
# Step envs.
results = [e.step(a) for e, a in zip(envs, action)]
obs, _, done = zip(*[p[:3] for p in results])
obs = list(obs)
done = np.stack(done)
episode += int(done.sum())
length += 1
step += (done * length).sum()
length *= 1 - done
# Return new state to allow resuming the simulation.
return (step - steps, episode - episodes, done, length, obs, agent_state)
def save_episodes(directory, episodes, save_eps):
directory = pathlib.Path(directory).expanduser()
directory.mkdir(parents=True, exist_ok=True)
timestamp = datetime.datetime.now().strftime("%Y%m%dT%H%M%S")
filenames = []
for episode in episodes:
identifier = str(uuid.uuid4().hex)
length = len(episode["reward"])
filename = directory / f"{timestamp}-{identifier}-{length}.npz"
if save_eps:
with io.BytesIO() as f1:
np.savez_compressed(f1, **episode)
f1.seek(0)
with filename.open("wb") as f2:
f2.write(f1.read())
filenames.append(filename)
return filenames
def sample_episodes(episodes, length=None, balance=False, seed=0):
random = np.random.RandomState(seed)
while True:
episode = random.choice(list(episodes.values()))
if length:
total = len(next(iter(episode.values())))
available = total - length
if available < 1:
# print(f'Skipped short episode of length {available}.')
continue
if balance:
index = min(random.randint(0, total), available)
else:
index = int(random.randint(0, available + 1))
episode = {k: v[index : index + length] for k, v in episode.items()}
yield episode
def load_episodes(directory, limit=None):
directory = pathlib.Path(directory).expanduser()
episodes = {}
total = 0
for filename in reversed(sorted(directory.glob("*.npz"))):
try:
with filename.open("rb") as f:
episode = np.load(f)
episode = {k: episode[k] for k in episode.keys()}
except Exception as e:
print(f"Could not load episode: {e}")
continue
episodes[str(filename)] = episode
total += len(episode["reward"]) - 1
if limit and total >= limit:
break
return episodes
class DtypeDist:
def __init__(self, dist, dtype=None):
self._dist = dist
self._dtype = dtype or prec.global_policy().compute_dtype
@property
def name(self):
return "DtypeDist"
def __getattr__(self, name):
return getattr(self._dist, name)
def mean(self):
return tf.cast(self._dist.mean(), self._dtype)
def mode(self):
return tf.cast(self._dist.mode(), self._dtype)
def entropy(self):
return tf.cast(self._dist.entropy(), self._dtype)
def sample(self, *args, **kwargs):
return tf.cast(self._dist.sample(*args, **kwargs), self._dtype)
class SampleDist:
def __init__(self, dist, samples=100):
self._dist = dist
self._samples = samples
@property
def name(self):
return "SampleDist"
def __getattr__(self, name):
return getattr(self._dist, name)
def mean(self):
samples = self._dist.sample(self._samples)
return tf.reduce_mean(samples, 0)
def mode(self):
sample = self._dist.sample(self._samples)
logprob = self._dist.log_prob(sample)
return tf.gather(sample, tf.argmax(logprob))[0]
def entropy(self):
sample = self._dist.sample(self._samples)
logprob = self.log_prob(sample)
return -tf.reduce_mean(logprob, 0)
class BernoulliDist(tfd.Bernoulli):
def __init__(self, logits=None, probs=None, dtype=None):
self._sample_dtype = dtype or prec.global_policy().compute_dtype
super().__init__(logits=logits, probs=probs)
def mode(self):
return tf.cast(super().mode(), self._sample_dtype)
def sample(self, sample_shape=(), seed=None):
# Straight through biased gradient estimator.
sample = tf.cast(super().sample(sample_shape, seed), self._sample_dtype)
probs = super().probs_parameter()
while len(probs.shape) < len(sample.shape):
probs = probs[None]
sample += tf.cast(probs - tf.stop_gradient(probs), self._sample_dtype)
return sample
class OneHotDist(tfd.OneHotCategorical):
def __init__(self, logits=None, probs=None, dtype=None):
self._sample_dtype = dtype or prec.global_policy().compute_dtype
super().__init__(logits=logits, probs=probs)
def mode(self):
return tf.cast(super().mode(), self._sample_dtype)
def sample(self, sample_shape=(), seed=None):
# Straight through biased gradient estimator.
sample = tf.cast(super().sample(sample_shape, seed), self._sample_dtype)
probs = super().probs_parameter()
while len(probs.shape) < len(sample.shape):
probs = probs[None]
sample += tf.cast(probs - tf.stop_gradient(probs), self._sample_dtype)
return sample
class GumbleDist(tfd.RelaxedOneHotCategorical):
def __init__(self, temp, logits=None, probs=None, dtype=None):
self._sample_dtype = dtype or prec.global_policy().compute_dtype
self._exact = tfd.OneHotCategorical(logits=logits, probs=probs)
super().__init__(temp, logits=logits, probs=probs)
def mode(self):
return tf.cast(self._exact.mode(), self._sample_dtype)
def entropy(self):
return tf.cast(self._exact.entropy(), self._sample_dtype)
def sample(self, sample_shape=(), seed=None):
return tf.cast(super().sample(sample_shape, seed), self._sample_dtype)
class UnnormalizedHuber(tfd.Normal):
def __init__(self, loc, scale, threshold=1, **kwargs):
self._threshold = tf.cast(threshold, loc.dtype)
super().__init__(loc, scale, **kwargs)
def log_prob(self, event):
return -(
tf.math.sqrt((event - self.mean()) ** 2 + self._threshold ** 2)
- self._threshold
)
class SafeTruncatedNormal(tfd.TruncatedNormal):
def __init__(self, loc, scale, low, high, clip=1e-6, mult=1):
super().__init__(loc, scale, low, high)
self._clip = clip
self._mult = mult
def sample(self, *args, **kwargs):
event = super().sample(*args, **kwargs)
if self._clip:
clipped = tf.clip_by_value(
event, self.low + self._clip, self.high - self._clip
)
event = event - tf.stop_gradient(event) + tf.stop_gradient(clipped)
if self._mult:
event *= self._mult
return event
class TanhBijector(tfp.bijectors.Bijector):
def __init__(self, validate_args=False, name="tanh"):
super().__init__(
forward_min_event_ndims=0, validate_args=validate_args, name=name
)
def _forward(self, x):
return tf.nn.tanh(x)
def _inverse(self, y):
dtype = y.dtype
y = tf.cast(y, tf.float32)
y = tf.where(
tf.less_equal(tf.abs(y), 1.0),
tf.clip_by_value(y, -0.99999997, 0.99999997),
y,
)
y = tf.atanh(y)
y = tf.cast(y, dtype)
return y
def _forward_log_det_jacobian(self, x):
log2 = tf.math.log(tf.constant(2.0, dtype=x.dtype))
return 2.0 * (log2 - x - tf.nn.softplus(-2.0 * x))
def lambda_return(reward, value, pcont, bootstrap, lambda_, axis):
# Setting lambda=1 gives a discounted Monte Carlo return.
# Setting lambda=0 gives a fixed 1-step return.
assert reward.shape.ndims == value.shape.ndims, (reward.shape, value.shape)
if isinstance(pcont, (int, float)):
pcont = pcont * tf.ones_like(reward)
dims = list(range(reward.shape.ndims))
dims = [axis] + dims[1:axis] + [0] + dims[axis + 1 :]
if axis != 0:
reward = tf.transpose(reward, dims)
value = tf.transpose(value, dims)
pcont = tf.transpose(pcont, dims)
if bootstrap is None:
bootstrap = tf.zeros_like(value[-1])
next_values = tf.concat([value[1:], bootstrap[None]], 0)
inputs = reward + pcont * next_values * (1 - lambda_)
returns = static_scan(
lambda agg, cur: cur[0] + cur[1] * lambda_ * agg,
(inputs, pcont),
bootstrap,
reverse=True,
)
if axis != 0:
returns = tf.transpose(returns, dims)
return returns
class Optimizer(tf.Module):
def __init__(
self, name, lr, eps=1e-4, clip=None, wd=None, wd_pattern=r".*", opt="adam"
):
assert 0 <= wd < 1
assert not clip or 1 <= clip
self._name = name
self._clip = clip
self._wd = wd
self._wd_pattern = wd_pattern
self._opt = {
"adam": lambda: tf.optimizers.Adam(lr, epsilon=eps),
"nadam": lambda: tf.optimizers.Nadam(lr, epsilon=eps),
"adamax": lambda: tf.optimizers.Adamax(lr, epsilon=eps),
"sgd": lambda: tf.optimizers.SGD(lr),
"momentum": lambda: tf.optimizers.SGD(lr, 0.9),
}[opt]()
self._mixed = prec.global_policy().compute_dtype == tf.float16
if self._mixed:
self._opt = prec.LossScaleOptimizer(self._opt, "dynamic")
self._should_log_params = Once()
@property
def variables(self):
return self._opt.variables()
def __call__(self, tape, loss, modules):
assert loss.dtype is tf.float32, self._name
modules = modules if hasattr(modules, "__len__") else (modules,)
varibs = tf.nest.flatten([module.variables for module in modules])
count = sum(np.prod(x.shape) for x in varibs)
if self._should_log_params:
tqdm.write(f"Found {count} {self._name} parameters.")
assert len(loss.shape) == 0, loss.shape
tf.debugging.check_numerics(loss, self._name + "_loss")
metrics = {}
metrics[f"{self._name}_loss"] = loss
if self._mixed:
with tape:
loss = self._opt.get_scaled_loss(loss)
grads = tape.gradient(loss, varibs)
if self._mixed:
grads = self._opt.get_unscaled_gradients(grads)
norm = tf.linalg.global_norm(grads)
if not self._mixed:
tf.debugging.check_numerics(norm, self._name + "_norm")
if self._clip:
grads, _ = tf.clip_by_global_norm(grads, self._clip, norm)
if self._wd:
self._apply_weight_decay(varibs)
self._opt.apply_gradients(zip(grads, varibs))
metrics[f"{self._name}_grad_norm"] = norm
if self._mixed:
try:
metrics[f"{self._name}_loss_scale"] = float(self._opt.loss_scale)
except TypeError:
metrics[f"{self._name}_loss_scale"] = float(
self._opt.loss_scale._current_loss_scale
)
return metrics
def _apply_weight_decay(self, varibs):
nontrivial = self._wd_pattern != r".*"
if nontrivial:
print("Applied weight decay to variables:")
for var in varibs:
if re.search(self._wd_pattern, self._name + "/" + var.name):
if nontrivial:
print("- " + self._name + "/" + var.name)
var.assign((1 - self._wd) * var)
def args_type(default):
def parse_string(x):
if default is None:
return x
if isinstance(default, bool):
return bool(["False", "True"].index(x))
if isinstance(default, int):
return float(x) if ("e" in x or "." in x) else int(x)
if isinstance(default, (list, tuple)):
return tuple(args_type(default[0])(y) for y in x.split(","))
return type(default)(x)
def parse_object(x):
if isinstance(default, (list, tuple)):
return tuple(x)
return x
return lambda x: parse_string(x) if isinstance(x, str) else parse_object(x)
def static_scan(fn, inputs, start, reverse=False):
last = start
outputs = [[] for _ in tf.nest.flatten(start)]
indices = range(len(tf.nest.flatten(inputs)[0]))
if reverse:
indices = reversed(indices)
for index in indices:
inp = tf.nest.map_structure(lambda x: x[index], inputs)
last = fn(last, inp)
[o.append(l) for o, l in zip(outputs, tf.nest.flatten(last))]
if reverse:
outputs = [list(reversed(x)) for x in outputs]
outputs = [tf.stack(x, 0) for x in outputs]
return tf.nest.pack_sequence_as(start, outputs)
def uniform_mixture(dist, dtype=None):
if dist.batch_shape[-1] == 1:
return tfd.BatchReshape(dist, dist.batch_shape[:-1])
dtype = dtype or prec.global_policy().compute_dtype
weights = tfd.Categorical(tf.zeros(dist.batch_shape, dtype))
return tfd.MixtureSameFamily(weights, dist)
def cat_mixture_entropy(dist):
if isinstance(dist, tfd.MixtureSameFamily):
probs = dist.components_distribution.probs_parameter()
else:
probs = dist.probs_parameter()
return -tf.reduce_mean(
tf.reduce_mean(probs, 2) * tf.math.log(tf.reduce_mean(probs, 2) + 1e-8), -1
)
@tf.function
def cem_planner(
state, num_actions, horizon, proposals, topk, iterations, imagine, objective
):
dtype = prec.global_policy().compute_dtype
B, P = list(state.values())[0].shape[0], proposals
H, A = horizon, num_actions
flat_state = {k: tf.repeat(v, P, 0) for k, v in state.items()}
mean = tf.zeros((B, H, A), dtype)
std = tf.ones((B, H, A), dtype)
for _ in range(iterations):
proposals = tf.random.normal((B, P, H, A), dtype=dtype)
proposals = proposals * std[:, None] + mean[:, None]
proposals = tf.clip_by_value(proposals, -1, 1)
flat_proposals = tf.reshape(proposals, (B * P, H, A))
states = imagine(flat_proposals, flat_state)
scores = objective(states)
scores = tf.reshape(tf.reduce_sum(scores, -1), (B, P))
_, indices = tf.math.top_k(scores, topk, sorted=False)
best = tf.gather(proposals, indices, axis=1, batch_dims=1)
mean, var = tf.nn.moments(best, 1)
std = tf.sqrt(var + 1e-6)
return mean[:, 0, :]
@tf.function
def grad_planner(
state,
num_actions,
horizon,
proposals,
iterations,
imagine,
objective,
kl_scale,
step_size,
):
dtype = prec.global_policy().compute_dtype
B, P = list(state.values())[0].shape[0], proposals
H, A = horizon, num_actions
flat_state = {k: tf.repeat(v, P, 0) for k, v in state.items()}
mean = tf.zeros((B, H, A), dtype)
rawstd = 0.54 * tf.ones((B, H, A), dtype)
for _ in range(iterations):
proposals = tf.random.normal((B, P, H, A), dtype=dtype)
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(mean)
tape.watch(rawstd)
std = tf.nn.softplus(rawstd)
proposals = proposals * std[:, None] + mean[:, None]
proposals = (
tf.stop_gradient(tf.clip_by_value(proposals, -1, 1))
+ proposals
- tf.stop_gradient(proposals)
)
flat_proposals = tf.reshape(proposals, (B * P, H, A))
states = imagine(flat_proposals, flat_state)
scores = objective(states)
scores = tf.reshape(tf.reduce_sum(scores, -1), (B, P))
div = tfd.kl_divergence(
tfd.Normal(mean, std),
tfd.Normal(tf.zeros_like(mean), tf.ones_like(std)),
)
elbo = tf.reduce_sum(scores) - kl_scale * div
elbo /= tf.cast(tf.reduce_prod(tf.shape(scores)), dtype)
grad_mean, grad_rawstd = tape.gradient(elbo, [mean, rawstd])
e, v = tf.nn.moments(grad_mean, [1, 2], keepdims=True)
grad_mean /= tf.sqrt(e * e + v + 1e-4)
e, v = tf.nn.moments(grad_rawstd, [1, 2], keepdims=True)
grad_rawstd /= tf.sqrt(e * e + v + 1e-4)
mean = tf.clip_by_value(mean + step_size * grad_mean, -1, 1)
rawstd = rawstd + step_size * grad_rawstd
return mean[:, 0, :]
class Every:
def __init__(self, every):
self._every = every
self._last = None
def __call__(self, step):
if not self._every:
return False
if self._last is None:
self._last = step
return True
if step >= self._last + self._every:
self._last += self._every
return True
return False
class Once:
def __init__(self):
self._once = True
def __call__(self):
if self._once:
self._once = False
return True
return False
class Until:
def __init__(self, until):
self._until = until
def __call__(self, step):
if not self._until:
return True
return step < self._until
def schedule(string, step):
try:
return float(string)
except ValueError:
step = tf.cast(step, tf.float32)
match = re.match(r"linear\((.+),(.+),(.+)\)", string)
if match:
initial, final, duration = [float(group) for group in match.groups()]
mix = tf.clip_by_value(step / duration, 0, 1)
return (1 - mix) * initial + mix * final
match = re.match(r"warmup\((.+),(.+)\)", string)
if match:
warmup, value = [float(group) for group in match.groups()]
scale = tf.clip_by_value(step / warmup, 0, 1)
return scale * value
match = re.match(r"exp\((.+),(.+),(.+)\)", string)
if match:
initial, final, halflife = [float(group) for group in match.groups()]
return (initial - final) * 0.5 ** (step / halflife) + final
match = re.match(r"horizon\((.+),(.+),(.+)\)", string)
if match:
initial, final, duration = [float(group) for group in match.groups()]
mix = tf.clip_by_value(step / duration, 0, 1)
horizon = (1 - mix) * initial + mix * final
return 1 - 1 / horizon
raise NotImplementedError(string)
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import random_ops
def random_exp_initializer(minval=0, maxval=None, seed=None, dtype=dtypes.float32):
"""Returns an initializer that generates tensors with an exponential distribution.
Args:
minval: A python scalar or a scalar tensor. Lower bound of the range
of random values to generate.
maxval: A python scalar or a scalar tensor. Upper bound of the range
of random values to generate. Defaults to 1 for float types.
seed: A Python integer. Used to create random seeds. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
dtype: The data type.
Returns:
An initializer that generates tensors with an exponential distribution.
"""
def _initializer(shape, dtype=dtype, partition_info=None):
return tf.exp(
random_ops.random_uniform(shape, minval, maxval, dtype, seed=seed)
)
return _initializer
def phi(times, s, tau):
# return tf.div(tf.mod(tf.mod(times - s, tau) + tau, tau), tau)
# print ('in phi', tf.math.floormod(times - s, tau).dtype)
return tf.math.divide(tf.math.floormod(times - s, tau), tau)
def time_gate_fast_2(phase, r_on, leak_rate, training_phase):
if not training_phase:
leak_rate = 0.0
is_up = tf.math.less(phase, (r_on * 0.5))
is_down = tf.math.logical_and(tf.math.less(phase, r_on), tf.math.logical_not(is_up))
time_gate = tf.where(
is_up,
2 * phase / r_on,
tf.where(is_down, 2.0 - 2.0 * (phase / r_on), leak_rate * phase),
)
return time_gate
def time_gate_fast(phase, r_on, leak_rate, training_phase, dtype=tf.float16):
if not training_phase:
leak_rate = 0.0
cond_1 = tf.cast(tf.less_equal(phase, 0.5 * r_on), dtype=dtype)
cond_2 = tf.cast(
tf.logical_and(tf.less(0.5 * r_on, phase), tf.less(phase, r_on)), dtype=dtype
)
cond_3 = tf.cast(tf.greater_equal(phase, r_on), dtype=dtype)
term_1 = tf.multiply(cond_1, 2.0 * phase / r_on)
term_2 = tf.multiply(cond_2, 2.0 - 2.0 * phase / r_on)
term_3 = tf.multiply(cond_3, leak_rate * phase)
return term_1 + term_2 + term_3