Add experimental JAX samplers

RELEASE-NOTES.md

@@ -26,6 +26,7 @@ pip install theano-pymc
 This new version of `Theano-PyMC` comes with an experimental JAX backend which, when combined with the new and experimental JAX samplers in PyMC3, can greatly speed up sampling in your model. As this is still very new, please do not use it in production yet but do test it out and let us know if anything breaks and what results you are seeing, especially speed-wise.
 
 ### New features
+- New experimental JAX samplers in `pymc3.sample_jax` (see [notebook](https://docs.pymc.io/notebooks/GLM-hierarchical-jax.html) and [#4247](https://github.com/pymc-devs/pymc3/pull/4247)). Requires JAX and either TFP or numpyro.
 - Add MLDA, a new stepper for multilevel sampling. MLDA can be used when a hierarchy of approximate posteriors of varying accuracy is available, offering improved sampling efficiency especially in high-dimensional problems and/or where gradients are not available (see [#3926](https://github.com/pymc-devs/pymc3/pull/3926))
 - Add Bayesian Additive Regression Trees (BARTs) [#4183](https://github.com/pymc-devs/pymc3/pull/4183))
 - Added `pymc3.gp.cov.Circular` kernel for Gaussian Processes on circular domains, e.g. the unit circle (see [#4082](https://github.com/pymc-devs/pymc3/pull/4082)).

docs/source/notebooks/table_of_contents_examples.js

@@ -64,5 +64,6 @@ Gallery.contents = {
     "MLDA_introduction": "MCMC",
     "MLDA_simple_linear_regression": "MCMC",
     "MLDA_gravity_surveying": "MCMC",
-    "MLDA_variance_reduction_linear_regression": "MCMC"
+    "MLDA_variance_reduction_linear_regression": "MCMC",
+    "GLM-hierarchical-jax": "MCMC"
 }

pymc3/sampling_jax.py

@@ -0,0 +1,183 @@
+# 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