4cf7dc9644
* massive revision inspired by soup: algo folder works * porting rl commands, WIP * various * git subrepo push --remote=git@github.com:openai/codegen.git --branch=refactor codegen subrepo: subdir: "codegen" merged: "aa27e069" upstream: origin: "git@github.com:openai/codegen.git" branch: "refactor" commit: "aa27e069" git-subrepo: version: "0.4.0" origin: "git@github.com:ingydotnet/git-subrepo.git" commit: "74339e8" * various * rewrite RL stuff in new framework * fix almost everything * woohoo tests pass * more tests * reformatting * fixes * write tests for embeddings * re-remove cg2 * pylint * minor * move smooth_helpers import; seems to cause nondeterministic failure in parallel pytest
343 lines
13 KiB
Python
343 lines
13 KiB
Python
import tensorflow as tf
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import numpy as np
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import baselines.common.tf_util as U
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from baselines.a2c.utils import fc
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from tensorflow.python.ops import math_ops
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class Pd(object):
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"""
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A particular probability distribution
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"""
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def flatparam(self):
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raise NotImplementedError
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def mode(self):
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raise NotImplementedError
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def neglogp(self, x):
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# Usually it's easier to define the negative logprob
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raise NotImplementedError
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def kl(self, other):
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raise NotImplementedError
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def entropy(self):
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raise NotImplementedError
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def sample(self):
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raise NotImplementedError
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def logp(self, x):
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return - self.neglogp(x)
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def get_shape(self):
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return self.flatparam().shape
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@property
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def shape(self):
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return self.get_shape()
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def __getitem__(self, idx):
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return self.__class__(self.flatparam()[idx])
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class PdType(object):
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"""
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Parametrized family of probability distributions
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"""
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def pdclass(self):
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raise NotImplementedError
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def pdfromflat(self, flat):
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return self.pdclass()(flat)
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def pdfromlatent(self, latent_vector):
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raise NotImplementedError
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def param_shape(self):
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raise NotImplementedError
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def sample_shape(self):
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raise NotImplementedError
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def sample_dtype(self):
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raise NotImplementedError
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def param_placeholder(self, prepend_shape, name=None):
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return tf.placeholder(dtype=tf.float32, shape=prepend_shape+self.param_shape(), name=name)
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def sample_placeholder(self, prepend_shape, name=None):
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return tf.placeholder(dtype=self.sample_dtype(), shape=prepend_shape+self.sample_shape(), name=name)
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def __eq__(self, other):
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return (type(self) == type(other)) and (self.__dict__ == other.__dict__)
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class CategoricalPdType(PdType):
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def __init__(self, ncat):
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self.ncat = ncat
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def pdclass(self):
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return CategoricalPd
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def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
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pdparam = fc(latent_vector, 'pi', self.ncat, init_scale=init_scale, init_bias=init_bias)
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return self.pdfromflat(pdparam), pdparam
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def param_shape(self):
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return [self.ncat]
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def sample_shape(self):
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return []
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def sample_dtype(self):
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return tf.int32
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class MultiCategoricalPdType(PdType):
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def __init__(self, nvec):
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self.ncats = nvec
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def pdclass(self):
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return MultiCategoricalPd
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def pdfromflat(self, flat):
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return MultiCategoricalPd(self.ncats, flat)
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def param_shape(self):
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return [sum(self.ncats)]
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def sample_shape(self):
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return [len(self.ncats)]
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def sample_dtype(self):
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return tf.int32
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class DiagGaussianPdType(PdType):
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def __init__(self, size):
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self.size = size
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def pdclass(self):
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return DiagGaussianPd
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def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
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mean = fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
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logstd = tf.get_variable(name='pi/logstd', shape=[1, self.size], initializer=tf.zeros_initializer())
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pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
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return self.pdfromflat(pdparam), mean
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def param_shape(self):
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return [2*self.size]
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def sample_shape(self):
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return [self.size]
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def sample_dtype(self):
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return tf.float32
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class BernoulliPdType(PdType):
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def __init__(self, size):
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self.size = size
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def pdclass(self):
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return BernoulliPd
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def param_shape(self):
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return [self.size]
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def sample_shape(self):
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return [self.size]
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def sample_dtype(self):
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return tf.int32
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def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
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pdparam = fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
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return self.pdfromflat(pdparam), pdparam
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# WRONG SECOND DERIVATIVES
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# class CategoricalPd(Pd):
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# def __init__(self, logits):
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# self.logits = logits
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# self.ps = tf.nn.softmax(logits)
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# @classmethod
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# def fromflat(cls, flat):
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# return cls(flat)
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# def flatparam(self):
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# return self.logits
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# def mode(self):
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# return U.argmax(self.logits, axis=-1)
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# def logp(self, x):
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# return -tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits, x)
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# def kl(self, other):
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# return tf.nn.softmax_cross_entropy_with_logits(other.logits, self.ps) \
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# - tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
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# def entropy(self):
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# return tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
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# def sample(self):
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# u = tf.random_uniform(tf.shape(self.logits))
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# return U.argmax(self.logits - tf.log(-tf.log(u)), axis=-1)
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class CategoricalPd(Pd):
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def __init__(self, logits):
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self.logits = logits
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def flatparam(self):
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return self.logits
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def mode(self):
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return tf.argmax(self.logits, axis=-1)
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@property
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def mean(self):
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return tf.nn.softmax(self.logits)
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def neglogp(self, x):
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# return tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=x)
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# Note: we can't use sparse_softmax_cross_entropy_with_logits because
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# the implementation does not allow second-order derivatives...
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if x.dtype in {tf.uint8, tf.int32, tf.int64}:
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# one-hot encoding
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x_shape_list = x.shape.as_list()
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logits_shape_list = self.logits.get_shape().as_list()[:-1]
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for xs, ls in zip(x_shape_list, logits_shape_list):
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if xs is not None and ls is not None:
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assert xs == ls, 'shape mismatch: {} in x vs {} in logits'.format(xs, ls)
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x = tf.one_hot(x, self.logits.get_shape().as_list()[-1])
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else:
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# already encoded
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assert x.shape.as_list() == self.logits.shape.as_list()
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return tf.nn.softmax_cross_entropy_with_logits_v2(
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logits=self.logits,
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labels=x)
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def kl(self, other):
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a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keepdims=True)
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a1 = other.logits - tf.reduce_max(other.logits, axis=-1, keepdims=True)
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ea0 = tf.exp(a0)
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ea1 = tf.exp(a1)
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z0 = tf.reduce_sum(ea0, axis=-1, keepdims=True)
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z1 = tf.reduce_sum(ea1, axis=-1, keepdims=True)
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p0 = ea0 / z0
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return tf.reduce_sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), axis=-1)
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def entropy(self):
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a0 = self.logits - tf.reduce_max(self.logits, axis=-1, keepdims=True)
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ea0 = tf.exp(a0)
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z0 = tf.reduce_sum(ea0, axis=-1, keepdims=True)
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p0 = ea0 / z0
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return tf.reduce_sum(p0 * (tf.log(z0) - a0), axis=-1)
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def sample(self):
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u = tf.random_uniform(tf.shape(self.logits), dtype=self.logits.dtype)
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return tf.argmax(self.logits - tf.log(-tf.log(u)), axis=-1)
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@classmethod
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def fromflat(cls, flat):
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return cls(flat)
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class MultiCategoricalPd(Pd):
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def __init__(self, nvec, flat):
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self.flat = flat
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self.categoricals = list(map(CategoricalPd, tf.split(flat, nvec, axis=-1)))
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def flatparam(self):
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return self.flat
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def mode(self):
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return tf.cast(tf.stack([p.mode() for p in self.categoricals], axis=-1), tf.int32)
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def neglogp(self, x):
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return tf.add_n([p.neglogp(px) for p, px in zip(self.categoricals, tf.unstack(x, axis=-1))])
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def kl(self, other):
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return tf.add_n([p.kl(q) for p, q in zip(self.categoricals, other.categoricals)])
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def entropy(self):
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return tf.add_n([p.entropy() for p in self.categoricals])
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def sample(self):
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return tf.cast(tf.stack([p.sample() for p in self.categoricals], axis=-1), tf.int32)
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@classmethod
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def fromflat(cls, flat):
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raise NotImplementedError
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class DiagGaussianPd(Pd):
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def __init__(self, flat):
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self.flat = flat
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mean, logstd = tf.split(axis=len(flat.shape)-1, num_or_size_splits=2, value=flat)
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self.mean = mean
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self.logstd = logstd
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self.std = tf.exp(logstd)
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def flatparam(self):
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return self.flat
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def mode(self):
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return self.mean
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def neglogp(self, x):
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return 0.5 * tf.reduce_sum(tf.square((x - self.mean) / self.std), axis=-1) \
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+ 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[-1]) \
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+ tf.reduce_sum(self.logstd, axis=-1)
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def kl(self, other):
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assert isinstance(other, DiagGaussianPd)
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return tf.reduce_sum(other.logstd - self.logstd + (tf.square(self.std) + tf.square(self.mean - other.mean)) / (2.0 * tf.square(other.std)) - 0.5, axis=-1)
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def entropy(self):
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return tf.reduce_sum(self.logstd + .5 * np.log(2.0 * np.pi * np.e), axis=-1)
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def sample(self):
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return self.mean + self.std * tf.random_normal(tf.shape(self.mean))
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@classmethod
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def fromflat(cls, flat):
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return cls(flat)
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class BernoulliPd(Pd):
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def __init__(self, logits):
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self.logits = logits
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self.ps = tf.sigmoid(logits)
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def flatparam(self):
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return self.logits
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@property
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def mean(self):
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return self.ps
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def mode(self):
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return tf.round(self.ps)
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def neglogp(self, x):
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return tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=tf.to_float(x)), axis=-1)
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def kl(self, other):
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return tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=other.logits, labels=self.ps), axis=-1) - tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=self.ps), axis=-1)
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def entropy(self):
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return tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=self.ps), axis=-1)
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def sample(self):
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u = tf.random_uniform(tf.shape(self.ps))
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return tf.to_float(math_ops.less(u, self.ps))
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@classmethod
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def fromflat(cls, flat):
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return cls(flat)
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def make_pdtype(ac_space):
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from gym import spaces
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if isinstance(ac_space, spaces.Box):
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assert len(ac_space.shape) == 1
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return DiagGaussianPdType(ac_space.shape[0])
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elif isinstance(ac_space, spaces.Discrete):
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return CategoricalPdType(ac_space.n)
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elif isinstance(ac_space, spaces.MultiDiscrete):
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return MultiCategoricalPdType(ac_space.nvec)
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elif isinstance(ac_space, spaces.MultiBinary):
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return BernoulliPdType(ac_space.n)
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else:
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raise NotImplementedError
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def shape_el(v, i):
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maybe = v.get_shape()[i]
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if maybe is not None:
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return maybe
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else:
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return tf.shape(v)[i]
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@U.in_session
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def test_probtypes():
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np.random.seed(0)
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pdparam_diag_gauss = np.array([-.2, .3, .4, -.5, .1, -.5, .1, 0.8])
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diag_gauss = DiagGaussianPdType(pdparam_diag_gauss.size // 2) #pylint: disable=E1101
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validate_probtype(diag_gauss, pdparam_diag_gauss)
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pdparam_categorical = np.array([-.2, .3, .5])
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categorical = CategoricalPdType(pdparam_categorical.size) #pylint: disable=E1101
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validate_probtype(categorical, pdparam_categorical)
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nvec = [1,2,3]
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pdparam_multicategorical = np.array([-.2, .3, .5, .1, 1, -.1])
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multicategorical = MultiCategoricalPdType(nvec) #pylint: disable=E1101
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validate_probtype(multicategorical, pdparam_multicategorical)
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pdparam_bernoulli = np.array([-.2, .3, .5])
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bernoulli = BernoulliPdType(pdparam_bernoulli.size) #pylint: disable=E1101
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validate_probtype(bernoulli, pdparam_bernoulli)
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def validate_probtype(probtype, pdparam):
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N = 100000
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# Check to see if mean negative log likelihood == differential entropy
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Mval = np.repeat(pdparam[None, :], N, axis=0)
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M = probtype.param_placeholder([N])
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X = probtype.sample_placeholder([N])
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pd = probtype.pdfromflat(M)
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calcloglik = U.function([X, M], pd.logp(X))
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calcent = U.function([M], pd.entropy())
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Xval = tf.get_default_session().run(pd.sample(), feed_dict={M:Mval})
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logliks = calcloglik(Xval, Mval)
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entval_ll = - logliks.mean() #pylint: disable=E1101
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entval_ll_stderr = logliks.std() / np.sqrt(N) #pylint: disable=E1101
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entval = calcent(Mval).mean() #pylint: disable=E1101
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assert np.abs(entval - entval_ll) < 3 * entval_ll_stderr # within 3 sigmas
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# Check to see if kldiv[p,q] = - ent[p] - E_p[log q]
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M2 = probtype.param_placeholder([N])
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pd2 = probtype.pdfromflat(M2)
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q = pdparam + np.random.randn(pdparam.size) * 0.1
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Mval2 = np.repeat(q[None, :], N, axis=0)
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calckl = U.function([M, M2], pd.kl(pd2))
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klval = calckl(Mval, Mval2).mean() #pylint: disable=E1101
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logliks = calcloglik(Xval, Mval2)
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klval_ll = - entval - logliks.mean() #pylint: disable=E1101
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klval_ll_stderr = logliks.std() / np.sqrt(N) #pylint: disable=E1101
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assert np.abs(klval - klval_ll) < 3 * klval_ll_stderr # within 3 sigmas
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print('ok on', probtype, pdparam)
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