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sampling_jax.py
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# pylint: skip-file
import os
import re
import warnings
xla_flags = os.getenv("XLA_FLAGS", "").lstrip("--")
xla_flags = re.sub(r"xla_force_host_platform_device_count=.+\s", "", xla_flags).split()
os.environ["XLA_FLAGS"] = " ".join(["--xla_force_host_platform_device_count={}".format(100)])
import arviz as az
import jax
import numpy as np
import pandas as pd
import theano
import theano.sandbox.jax_linker
import theano.sandbox.jaxify
import pymc3 as pm
from pymc3 import modelcontext
warnings.warn("This module is experimental.")
# Disable C compilation by default
# theano.config.cxx = ""
# This will make the JAX Linker the default
# theano.config.mode = "JAX"
def sample_tfp_nuts(
draws=1000,
tune=1000,
chains=4,
target_accept=0.8,
random_seed=10,
model=None,
num_tuning_epoch=2,
num_compute_step_size=500,
):
from tensorflow_probability.substrates import jax as tfp
model = modelcontext(model)
seed = jax.random.PRNGKey(random_seed)
fgraph = theano.gof.FunctionGraph(model.free_RVs, [model.logpt])
fns = theano.sandbox.jaxify.jax_funcify(fgraph)
logp_fn_jax = fns[0]
rv_names = [rv.name for rv in model.free_RVs]
init_state = [model.test_point[rv_name] for rv_name in rv_names]
init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state)
@jax.pmap
def _sample(init_state, seed):
def gen_kernel(step_size):
hmc = tfp.mcmc.NoUTurnSampler(target_log_prob_fn=logp_fn_jax, step_size=step_size)
return tfp.mcmc.DualAveragingStepSizeAdaptation(
hmc, tune // num_tuning_epoch, target_accept_prob=target_accept
)
def trace_fn(_, pkr):
return pkr.new_step_size
def get_tuned_stepsize(samples, step_size):
return step_size[-1] * jax.numpy.std(samples[-num_compute_step_size:])
step_size = jax.tree_map(jax.numpy.ones_like, init_state)
for i in range(num_tuning_epoch - 1):
tuning_hmc = gen_kernel(step_size)
init_samples, tuning_result, kernel_results = tfp.mcmc.sample_chain(
num_results=tune // num_tuning_epoch,
current_state=init_state,
kernel=tuning_hmc,
trace_fn=trace_fn,
return_final_kernel_results=True,
seed=seed,
)
step_size = jax.tree_multimap(get_tuned_stepsize, list(init_samples), tuning_result)
init_state = [x[-1] for x in init_samples]
# Run inference
sample_kernel = gen_kernel(step_size)
mcmc_samples, leapfrog_num = tfp.mcmc.sample_chain(
num_results=draws,
num_burnin_steps=tune // num_tuning_epoch,
current_state=init_state,
kernel=sample_kernel,
trace_fn=lambda _, pkr: pkr.inner_results.leapfrogs_taken,
seed=seed,
)
return mcmc_samples, leapfrog_num
print("Compiling...")
tic2 = pd.Timestamp.now()
map_seed = jax.random.split(seed, chains)
mcmc_samples, leapfrog_num = _sample(init_state_batched, map_seed)
tic3 = pd.Timestamp.now()
print("Compilation + sampling time = ", tic3 - tic2)
# map_seed = jax.random.split(seed, chains)
# mcmc_samples = _sample(init_state_batched, map_seed)
# tic4 = pd.Timestamp.now()
# print("Sampling time = ", tic4 - tic3)
posterior = {k: v for k, v in zip(rv_names, mcmc_samples)}
az_trace = az.from_dict(posterior=posterior)
return az_trace # , leapfrog_num, tic3 - tic2
import jax
def sample_numpyro_nuts(
draws=1000,
tune=1000,
chains=4,
target_accept=0.8,
random_seed=10,
model=None,
progress_bar=True,
):
from numpyro.infer import MCMC, NUTS
from pymc3 import modelcontext
model = modelcontext(model)
seed = jax.random.PRNGKey(random_seed)
fgraph = theano.gof.FunctionGraph(model.free_RVs, [model.logpt])
fns = theano.sandbox.jaxify.jax_funcify(fgraph)
logp_fn_jax = fns[0]
rv_names = [rv.name for rv in model.free_RVs]
init_state = [model.test_point[rv_name] for rv_name in rv_names]
init_state_batched = jax.tree_map(lambda x: np.repeat(x[None, ...], chains, axis=0), init_state)
@jax.jit
def _sample(current_state, seed):
step_size = jax.tree_map(jax.numpy.ones_like, init_state)
nuts_kernel = NUTS(
potential_fn=lambda x: -logp_fn_jax(*x),
# model=model,
target_accept_prob=target_accept,
adapt_step_size=True,
adapt_mass_matrix=True,
dense_mass=False,
)
pmap_numpyro = MCMC(
nuts_kernel,
num_warmup=tune,
num_samples=draws,
num_chains=chains,
postprocess_fn=None,
chain_method="parallel",
progress_bar=progress_bar,
)
pmap_numpyro.run(seed, init_params=current_state, extra_fields=("num_steps",))
samples = pmap_numpyro.get_samples(group_by_chain=True)
leapfrogs_taken = pmap_numpyro.get_extra_fields(group_by_chain=True)["num_steps"]
return samples, leapfrogs_taken
print("Compiling...")
tic2 = pd.Timestamp.now()
map_seed = jax.random.split(seed, chains)
mcmc_samples, leapfrogs_taken = _sample(init_state_batched, map_seed)
tic3 = pd.Timestamp.now()
print("Compilation + sampling time = ", tic3 - tic2)
# map_seed = jax.random.split(seed, chains)
# mcmc_samples = _sample(init_state_batched, map_seed)
# tic4 = pd.Timestamp.now()
# print("Sampling time = ", tic4 - tic3)
posterior = {k: v for k, v in zip(rv_names, mcmc_samples)}
az_trace = az.from_dict(posterior=posterior)
return az_trace # , leapfrogs_taken, tic3 - tic2