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import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability import distributions as tfd
# 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 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 samples.mean(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 -logprob.mean(0)
class OneHotDist(tfd.OneHotCategorical):
def __init__(self, logits=None, probs=None, dtype=None):
self._sample_dtype = dtype or tf.float32
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 = self._pad(super().probs_parameter(), sample.shape)
sample += tf.cast(probs - tf.stop_gradient(probs), self._sample_dtype)
return sample
def _pad(self, tensor, shape):
tensor = super().probs_parameter()
while len(tensor.shape) < len(shape):
tensor = tensor[None]
return tensor
class TruncNormalDist(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.),
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))
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