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Copyright 2018 The TensorFlow Authors.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Standards for tfp.distributions

This document represents the TFP standards for all members of the tfp.distributions module. These standards only apply to the tfp.distributions module and is intentionally a "high bar" meant to protect users of the module. The "TensorFlow Distributions" whitepaper is an exposition on the standards described here (see Section 3). These standards have been in effect since 16-aug-2016.

You are encouraged to subclass tfp.distributions.Distribution and invited to disregard any/all of these standards. We especially recommend you ignore point #4.

All TFP code (including tfp.distributions) follows the TFP Style Guide.

Requirements (Comprehensive)

  1. A Distribution subclass must implement a sample function. The sample function shall generate random outcomes which in expectation correspond to other properties of the Distribution. Other properties must assume the semantics implied by the output of sample.

  2. A Distribution must implement a log_prob function. This function is the log probability mass function or probability density function, depending on the underlying measure.

  3. All member functions must be efficiently computable. To us, efficiently computable means: computable in (at most) expected polynomial time.

  4. All member functions (except sample) must be deterministic, reproducible, and platform invariant. The means no stochastic approximations (even seeded). For example, it is acceptable to implement an efficiently computable analytical upper bound on entropy but it is not acceptable to implement a Monte Carlo estimate of entropy, even if that Monte Carlo estimate uses a seeded RNG for reproducibility. It also acceptable to implement an approximation of statistic (e.g., approximation of LogitNormal mean), as long as this can be computed non-stochastically.

  5. Implementing other distribution properties is highly encouraged iff they are mathematically well-defined and efficiently computable. For example mean_log_det is a meaningful property for a Wishart but not for a (scalar) Normal.

  6. A Distribution's inputs/outputs are Tensors with "Distribution shape semantics." For example, functions like prob and cdf accept a Tensor with "Distribution shape semantics" whereas sample returns a Tensor with "Distribution shape semantics."

    • Exception: tfd.JointDistribution takes/returns a list-like of Tensors.

  7. A Distribution's event_ndims must be known statically. For example, Wishart has event_ndims=2, MultivariateNormalDiag has event_ndims=1, Normal has event_ndims=0, Categorical has event_ndims=0 and OneHotCategorical has event_ndims=1. The event_shape need not be known statically, i.e., this might only be known at runtime (in Eager mode) or at graph execution time (in graph mode). Often a Distribution's event_ndims will be self-evident from the class name itself.

    • Redacted 07-nov-2019.

  8. All Distributions are implicitly or explicitly conditioned on global or local (per-sample) parameters. A Distribution infers dtype and batch/event shape from its global parameters. For example, a scalar Distribution's event_shape is implicitly inferrable (event_shape=[]) thus always known statically; the same is not necessarily true of a MultivariateNormalDiag.

  9. When possible, a Distribution must support Numpy-like broadcasting for all arguments. When broadcasting is not possible, arguments must be validated.

  10. All possible effort is made to validate arguments prior to graph execution. Any validation requiring graph execution must be gated by a Boolean, global parameter.

  11. Distribution parameters are descriptive English (i.e., not Greek letters) and draw upon a relatively small, shared lexicon. Examples include: loc, scale, concentration, rate, probs, logits, df. When forced to choose between mathematical purity and conveying intuitive meaning, prefer the latter but provide extensive documentation.

  12. TFP Distributions guarantee that input arguments are not manipulated by __init__ except to convert non-TF-derived inputs to tf.Tensors.

    Among other things, this contract implies:

    1. tf.Variable-derived __init__ arguments are not read ("concretized") until some computation is requested, e.g. a member function is called. We call this "maximally deferred read" idea: "tf.Variable safety."

    2. No additional computation result is stored in lieu of __init__ arguments except to convert them to tf.Tensors (if they aren't already). For reasons made clear below, we call this "non manipulation" idea: "tf.GradientTape safety".

    The above contract ensures several desirable features of Distributions:

    1. Evaluations of mutable arguments (including assertions) are re-run any time the underlying values could possibly change. Example:

      loc = tf.constant(0.)
scale = tf.Variable(1.)
d = tfp.distributions.Normal(loc, scale, validate_args=True)
d.log_prob(0.)
# ==> -0.918938
d.scale.assign(-1.)
d.log_prob(0.)
# ==> InvalidArgumentError: Argument `scale` must be positive.

    2. Gradients of public Distribution methods with respect to __init__ arguments are valid regardless of the Distribution being created inside or outside the tf.GradientTape. Example:

      loc = tf.constant(0.)
scale = tf.Variable(1.)
d = tfp.distributions.Normal(loc, scale, validate_args=True)
with tf.GradientTape() as tape:
  tape.watch(loc)
  # `tape.watch(scale)` is not required since `tf.GradientTape`
  # automatically watches `tf.Variable` dependencies (by default).
  x = -d.log_prob(1.)
grad = tape.gradient(x, [loc, scale, d.loc, d.scale])
assert all([g is not None for g in grad])

    Note that both of these properties would be lost if __init__ memoized any derived computation in lieu of the original Tensor-convertible arguments.

  13. Distribution and subclasses' @property methods shall never execute TF ops. For example, in graph execution regime this implies calling @property will never mutate the graph.

Non-Requirements (Noncomprehensive)

In this section we list items which have historically been presumed true of tfp.distributions but are not official requirements.

  1. Mutable state is not explicitly disallowed. However, it is highly discouraged as it makes reasoning about the object more challenging (for both API owner and user). As of 11-nov-2019, no tfp.distributions member has its own mutable state although all distributions do mutate the global random number generate state on access to sample.

  2. Subclasses are free to override public base class members. I.e., you don't have to follow the "public calls private" pattern. (However, as of 11-nov-2019, there has not yet been a reason to deviate from this pattern.)