Source code for numpyro.contrib.tfp.mcmc

# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0

from abc import ABCMeta
from collections import namedtuple
import inspect

from jax import random, vmap
from jax.flatten_util import ravel_pytree
import jax.numpy as jnp
from jax.tree_util import tree_map
import tensorflow_probability.substrates.jax as tfp

from numpyro.infer import init_to_uniform
from numpyro.infer.mcmc import MCMCKernel
from numpyro.infer.util import initialize_model
from numpyro.util import identity, is_prng_key

TFPKernelState = namedtuple("TFPKernelState", ["z", "kernel_results", "rng_key"])

def _extract_kernel_functions(kernel):
    def init_fn(z, rng_key):
        z_flat, _ = ravel_pytree(z)
        results = kernel.bootstrap_results(z_flat)
        return TFPKernelState(z, results, rng_key)

    def sample_fn(state, model_args=(), model_kwargs=None):
        rng_key, rng_key_transition = random.split(state.rng_key)
        z_flat, unravel_fn = ravel_pytree(state.z)
        z_new_flat, results = kernel.one_step(
            z_flat, state.kernel_results, seed=rng_key_transition
        return TFPKernelState(unravel_fn(z_new_flat), results, rng_key)

    return init_fn, sample_fn

def _make_log_prob_fn(potential_fn, unravel_fn):
    def log_prob_fn(x):
        # we deal with batched x in case the kernel is ReplicaExchangeMC
        batch_shape = jnp.shape(x)[:-1]
        if batch_shape:
            flatten_result = vmap(lambda a: -potential_fn(unravel_fn(a)))(
                jnp.reshape(x, (-1,) + jnp.shape(x)[-1:])
            return tree_map(
                lambda a: jnp.reshape(a, batch_shape + jnp.shape(a)[1:]), flatten_result
            return -potential_fn(unravel_fn(x))

    return log_prob_fn

class _TFPKernelMeta(ABCMeta):
    def __getitem__(cls, kernel_class):
        assert issubclass(kernel_class, tfp.mcmc.TransitionKernel)
        assert (
            "target_log_prob_fn" in inspect.getfullargspec(kernel_class).args
        ), f"the first argument of {kernel_class} must be `target_log_prob_fn`"

        _PyroKernel = type(kernel_class.__name__, (TFPKernel,), {})
        _PyroKernel.kernel_class = kernel_class
        return _PyroKernel

[docs] class TFPKernel(MCMCKernel, metaclass=_TFPKernelMeta): """ A thin wrapper for TensorFlow Probability (TFP) MCMC transition kernels. The argument `target_log_prob_fn` in TFP is replaced by either `model` or `potential_fn` (which is the negative of `target_log_prob_fn`). This class can be used to convert a TFP kernel to a NumPyro-compatible one as follows:: from numpyro.contrib.tfp.mcmc import TFPKernel kernel = TFPKernel[tfp.mcmc.NoUTurnSampler](model, step_size=1.) .. note:: By default, uncalibrated kernels will be inner kernels of the :class:`~tensorflow_probability.substrates.jax.mcmc.MetropolisHastings` kernel. .. note:: For :class:`~numpyro.contrib.tfp.mcmc.ReplicaExchangeMC`, TFP requires that the shape of `step_size` of the inner kernel must be `[len(inverse_temperatures), 1]` or `[len(inverse_temperatures), latent_size]`. :param model: Python callable containing Pyro :mod:`~numpyro.primitives`. If model is provided, `potential_fn` will be inferred using the model. :param potential_fn: Python callable that computes the target potential energy given input parameters. The input parameters to `potential_fn` can be any python collection type, provided that `init_params` argument to :meth:`init` has the same type. :param callable init_strategy: a per-site initialization function. See :ref:`init_strategy` section for available functions. :param kernel_kwargs: other arguments to be passed to TFP kernel constructor. """ kernel_class = None def __init__( self, model=None, potential_fn=None, init_strategy=init_to_uniform, **kernel_kwargs, ): if not (model is None) ^ (potential_fn is None): raise ValueError("Only one of `model` or `potential_fn` must be specified.") self._model = model self._potential_fn = potential_fn self._kernel_kwargs = kernel_kwargs self._init_strategy = init_strategy # Set on first call to init self._init_fn = None self._postprocess_fn = None self._sample_fn = None def _init_state(self, rng_key, model_args, model_kwargs, init_params): if self._model is not None: init_params, potential_fn, postprocess_fn, model_trace = initialize_model( rng_key, self._model, init_strategy=self._init_strategy, dynamic_args=True, model_args=model_args, model_kwargs=model_kwargs, ) init_params = init_params.z if self._init_fn is None: _, unravel_fn = ravel_pytree(init_params) kernel = self.kernel_class( _make_log_prob_fn( potential_fn(*model_args, **model_kwargs), unravel_fn ), **self._kernel_kwargs, ) # Uncalibrated... kernels have to used inside MetropolisHastings, see # if self.kernel_class.__name__.startswith("Uncalibrated"): kernel = tfp.mcmc.MetropolisHastings(kernel) self._init_fn, self._sample_fn = _extract_kernel_functions(kernel) self._postprocess_fn = postprocess_fn elif self._init_fn is None: _, unravel_fn = ravel_pytree(init_params) kernel = self.kernel_class( _make_log_prob_fn(self._potential_fn, unravel_fn), **self._kernel_kwargs ) if self.kernel_class.__name__.startswith("Uncalibrated"): kernel = tfp.mcmc.MetropolisHastings(kernel) self._init_fn, self._sample_fn = _extract_kernel_functions(kernel) return init_params @property def model(self): return self._model @property def sample_field(self): return "z" @property def default_fields(self): return ("z",) def get_diagnostics_str(self, state): """ Given the current `state`, returns the diagnostics string to be added to progress bar for diagnostics purpose. """ return "" def init( self, rng_key, num_warmup, init_params=None, model_args=(), model_kwargs={} ): # non-vectorized if is_prng_key(rng_key): rng_key, rng_key_init_model = random.split(rng_key) # vectorized else: rng_key, rng_key_init_model = jnp.swapaxes( vmap(random.split)(rng_key), 0, 1 ) init_params = self._init_state( rng_key_init_model, model_args, model_kwargs, init_params ) if self._potential_fn and init_params is None: raise ValueError( "Valid value of `init_params` must be provided with" " `target_log_prob_fn`." ) if is_prng_key(rng_key): init_state = self._init_fn(init_params, rng_key) else: # XXX it is safe to run hmc_init_fn under vmap despite that hmc_init_fn changes some # nonlocal variables: momentum_generator, wa_update, trajectory_len, max_treedepth, # wa_steps because those variables do not depend on traced args: init_params, rng_key. init_state = vmap(self._init_fn)(init_params, rng_key) sample_fn = vmap(self._sample_fn, in_axes=(0, None, None)) self._sample_fn = sample_fn return init_state def postprocess_fn(self, args, kwargs): if self._postprocess_fn is None: return identity return self._postprocess_fn(*args, **kwargs) def sample(self, state, model_args, model_kwargs): """ Run the kernel from the given :data:`~numpyro.contrib.tfp.mcmc.TFPKernelState` and return the resulting :data:`~numpyro.contrib.tfp.mcmc.TFPKernelState`. :param TFPKernelState state: Represents the current state. :param model_args: Arguments provided to the model. :param model_kwargs: Keyword arguments provided to the model. :return: Next `state` after running the kernel. """ return self._sample_fn(state, model_args, model_kwargs)
__all__ = ["TFPKernel"] for _name, _Kernel in tfp.mcmc.__dict__.items(): if not isinstance(_Kernel, type): continue if not issubclass(_Kernel, tfp.mcmc.TransitionKernel): continue if "target_log_prob_fn" not in inspect.getfullargspec(_Kernel).args: continue _PyroKernel = TFPKernel[_Kernel] _PyroKernel.__module__ = __name__ locals()[_name] = _PyroKernel _PyroKernel.__doc__ = """ Wraps `{}.{} <{}>`_ with :class:`~numpyro.contrib.tfp.mcmc.TFPKernel`. The first argument `target_log_prob_fn` in TFP kernel construction is replaced by either `model` or `potential_fn`. """.format( _Kernel.__module__, _Kernel.__name__, _Kernel.__name__ ) __all__.append(_name) # Create sphinx documentation. __doc__ = "\n\n".join( [ """ {0} ---------------------------------------------------------------- .. autoclass:: numpyro.contrib.tfp.mcmc.{0} """.format( _name ) for _name in __all__[:1] + sorted(__all__[1:]) ] )