Simplify run_inference_algorithm

blackjax/adaptation/mclmc_adaptation.py

@@ -20,7 +20,7 @@
 from jax.flatten_util import ravel_pytree
 
 from blackjax.diagnostics import effective_sample_size
-from blackjax.util import pytree_size, streaming_average_update
+from blackjax.util import incremental_value_update, pytree_size
 
 
 class MCLMCAdaptationState(NamedTuple):
@@ -199,9 +199,9 @@ def step(iteration_state, weight_and_key):
 
         x = ravel_pytree(state.position)[0]
         # update the running average of x, x^2
-        streaming_avg = streaming_average_update(
-            current_value=jnp.array([x, jnp.square(x)]),
-            previous_weight_and_average=streaming_avg,
+        streaming_avg = incremental_value_update(
+            expectation=jnp.array([x, jnp.square(x)]),
+            incremental_val=streaming_avg,
             weight=(1 - mask) * success * params.step_size,
             zero_prevention=mask,
         )

blackjax/util.py

@@ -3,7 +3,6 @@
 from functools import partial
 from typing import Callable, Union
 
-import jax
 import jax.numpy as jnp
 from jax import jit, lax
 from jax.flatten_util import ravel_pytree
@@ -149,9 +148,7 @@
     initial_state: ArrayLikeTree = None,
     initial_position: ArrayLikeTree = None,
     progress_bar: bool = False,
-    transform: Callable = lambda x: x,
-    return_state_history=True,
-    expectation: Callable = lambda x: x,
+    transform: Callable = lambda state, info: (state, info),
 ) -> tuple:
     """Wrapper to run an inference algorithm.
 
@@ -166,104 +163,148 @@
     initial_state
         The initial state of the inference algorithm.
     initial_position
-        The initial position of the inference algorithm. This is used when the initial
-        state is not provided.
+        The initial position of the inference algorithm. This is used when the initial state is not provided.
     inference_algorithm
         One of blackjax's sampling algorithms or variational inference algorithms.
     num_steps
         Number of MCMC steps.
     progress_bar
         Whether to display a progress bar.
     transform
-        A transformation of the trace of states to be returned. This is useful for
+        A transformation of the trace of states (and info) to be returned. This is useful for
         computing determinstic variables, or returning a subset of the states.
         By default, the states are returned as is.
-    expectation
-        A function that computes the expectation of the state. This is done
-        incrementally, so doesn't require storing all the states.
-    return_state_history
-        if False, `run_inference_algorithm` will only return an expectation of the value
-        of transform, and return that average instead of the full set of samples. This
-        is useful when memory is a bottleneck.
 
     Returns
     -------
-    If return_state_history is True:
         1. The final state.
-        2. The trace of the state.
+        2. The trace of the transform(state)
         3. The trace of the info of the inference algorithm for diagnostics.
-    If return_state_history is False:
-        1. This is the expectation of state over the chain. Otherwise the final state.
-        2. The final state of the inference algorithm.
     """
 
     if initial_state is None and initial_position is None:
-        raise ValueError(
-            "Either `initial_state` or `initial_position` must be provided."
-        )
+        raise ValueError("Either initial_state or initial_position must be provided.")
     if initial_state is not None and initial_position is not None:
         raise ValueError(
-            "Only one of `initial_state` or `initial_position` must be provided."
+            "Only one of initial_state or initial_position must be provided."
         )
 
-    if initial_state is None:
-        rng_key, init_key = split(rng_key, 2)
+    rng_key, init_key = split(rng_key, 2)
+    if initial_position is not None:
         initial_state = inference_algorithm.init(initial_position, init_key)
 
     keys = split(rng_key, num_steps)
 
-    def one_step(average_and_state, xs, return_state):
+    def one_step(state, xs):
         _, rng_key = xs
-        average, state = average_and_state
         state, info = inference_algorithm.step(rng_key, state)
-        average = streaming_average_update(expectation(transform(state)), average)
-        if return_state:
-            return (average, state), (transform(state), info)
-        else:
-            return (average, state), None
+        return state, transform(state, info)
 
-    one_step = jax.jit(partial(one_step, return_state=return_state_history))
-
-    xs = (jnp.arange(num_steps), keys)
     scan_fn = gen_scan_fn(num_steps, progress_bar)
-    ((_, average), final_state), history = scan_fn(
-        one_step,
-        ((0, expectation(transform(initial_state))), initial_state),
-        xs,
-    )
 
-    if not return_state_history:
-        return average, transform(final_state)
-    else:
-        state_history, info_history = history
-        return transform(final_state), state_history, info_history
+    xs = jnp.arange(num_steps), keys
+    final_state, history = scan_fn(one_step, initial_state, xs)
+
+    return final_state, history
 
 
-def streaming_average_update(
-    current_value, previous_weight_and_average, weight=1.0, zero_prevention=0.0
+def store_only_expectation_values(
+    sampling_algorithm,
+    state_transform=lambda x: x,
+    incremental_value_transform=lambda x: x,
+    burn_in=0,
+):
+    """Takes a sampling algorithm and constructs from it a new sampling algorithm object. The new sampling algorithm has the same
+     kernel but only stores the streaming expectation values of some observables, not the full states; to save memory.
+
+    It saves incremental_value_transform(E[state_transform(x)]) at each step i, where expectation is computed with samples up to i-th sample.
+
+    Example:
+
+    .. code::
+
+         init_key, state_key, run_key = jax.random.split(jax.random.PRNGKey(0),3)
+         model = StandardNormal(2)
+         initial_position = model.sample_init(init_key)
+         initial_state = blackjax.mcmc.mclmc.init(
+             position=initial_position, logdensity_fn=model.logdensity_fn, rng_key=state_key
+         )
+         integrator_type = "mclachlan"
+         L = 1.0
+         step_size = 0.1
+         num_steps = 4
+
+         integrator = map_integrator_type_to_integrator['mclmc'][integrator_type]
+         state_transform = lambda state: state.position
+         memory_efficient_sampling_alg, transform = store_only_expectation_values(
+             sampling_algorithm=sampling_alg,
+             state_transform=state_transform)
+
+         initial_state = memory_efficient_sampling_alg.init(initial_state)
+
+         final_state, trace_at_every_step = run_inference_algorithm(
+
+             rng_key=run_key,
+             initial_state=initial_state,
+             inference_algorithm=memory_efficient_sampling_alg,
+             num_steps=num_steps,
+             transform=transform,
+             progress_bar=True,
+         )
+    """
+
+    def init_fn(state):
+        averaging_state = (0.0, state_transform(state))
+        return (state, averaging_state)
+
+    def update_fn(rng_key, state_and_incremental_val):
+        state, averaging_state = state_and_incremental_val
+        state, info = sampling_algorithm.step(
+            rng_key, state
+        )  # update the state with the sampling algorithm
+        averaging_state = incremental_value_update(
+            state_transform(state),
+            averaging_state,
+            weight=(
+                averaging_state[0] >= burn_in
+            ),  # If we want to eliminate some number of steps as a burn-in
+            zero_prevention=1e-10 * (burn_in > 0),
+        )
+        # update the expectation value with the running average
+        return (state, averaging_state), info
+
+    def transform(state_and_incremental_val, info):
+        (state, (_, incremental_value)) = state_and_incremental_val
+        return incremental_value_transform(incremental_value), info
+
+    return SamplingAlgorithm(init_fn, update_fn), transform
+
+
+def incremental_value_update(
+    expectation, incremental_val, weight=1.0, zero_prevention=0.0
 ):
     """Compute the streaming average of a function O(x) using a weight.
     Parameters:
     ----------
-        current_value
-            the current value of the function that we want to take average of
-        previous_weight_and_average
-            tuple of (previous_weight, previous_average) where previous_weight is the
-            sum of weights and average is the current estimated average
+        expectation
+            the value of the expectation at the current timestep
+        incremental_val
+            tuple of (total, average) where total is the sum of weights and average is the current average
         weight
             weight of the current state
         zero_prevention
             small value to prevent division by zero
     Returns:
     ----------
-        new total weight and streaming average
+        new streaming average
     """
-    previous_weight, previous_average = previous_weight_and_average
-    current_weight = previous_weight + weight
-    current_average = jax.tree.map(
-        lambda x, avg: (previous_weight * avg + weight * x)
-        / (current_weight + zero_prevention),
-        current_value,
-        previous_average,
+
+    flat_expectation, unravel_fn = ravel_pytree(expectation)
+    total, average = incremental_val
+    flat_average, _ = ravel_pytree(average)
+    average = (total * flat_average + weight * flat_expectation) / (
+        total + weight + zero_prevention
     )
-    return current_weight, current_average
+    total += weight
+    incremental_val = (total, unravel_fn(average))
+    return incremental_val

tests/adaptation/test_adaptation.py

@@ -90,7 +90,7 @@ def test_chees_adaptation(adaptation_filters):
     algorithm = blackjax.dynamic_hmc(logprob_fn, **parameters)
 
     chain_keys = jax.random.split(inference_key, num_chains)
-    _, _, infos = jax.vmap(
+    _, (_, infos) = jax.vmap(
         lambda key, state: run_inference_algorithm(
             rng_key=key,
             initial_state=state,

tests/mcmc/test_sampling.py

@@ -135,12 +135,12 @@ def run_mclmc(
             sqrt_diag_cov=blackjax_mclmc_sampler_params.sqrt_diag_cov,
         )
 
-        _, samples, _ = run_inference_algorithm(
+        _, samples = run_inference_algorithm(
             rng_key=run_key,
             initial_state=blackjax_state_after_tuning,
             inference_algorithm=sampling_alg,
             num_steps=num_steps,
-            transform=lambda x: x.position,
+            transform=lambda state, info: state.position,
         )
 
         return samples
@@ -197,7 +197,7 @@ def check_attrs(attribute, keyset):
         for i, attribute in enumerate(["state", "info", "adaptation_state"]):
             check_attrs(attribute, keysets[i])
 
-        _, states, _ = run_inference_algorithm(
+        _, (states, _) = run_inference_algorithm(
             rng_key=inference_key,
             initial_state=state,
             inference_algorithm=inference_algorithm,
@@ -223,10 +223,11 @@ def test_mala(self):
 
         mala = blackjax.mala(logposterior_fn, 1e-5)
         state = mala.init({"coefs": 1.0, "log_scale": 1.0})
-        _, states, _ = run_inference_algorithm(
+        _, states = run_inference_algorithm(
             rng_key=inference_key,
             initial_state=state,
             inference_algorithm=mala,
+            transform=lambda state, info: state,
             num_steps=10_000,
         )
 
@@ -375,11 +376,12 @@ def test_pathfinder_adaptation(
         )
         inference_algorithm = algorithm(logposterior_fn, **parameters)
 
-        _, states, _ = run_inference_algorithm(
+        _, states = run_inference_algorithm(
             rng_key=inference_key,
             initial_state=state,
             inference_algorithm=inference_algorithm,
             num_steps=num_sampling_steps,
+            transform=lambda state, info: state,
         )
 
         coefs_samples = states.position["coefs"]
@@ -418,11 +420,12 @@ def test_meads(self):
         inference_algorithm = blackjax.ghmc(logposterior_fn, **parameters)
 
         chain_keys = jax.random.split(inference_key, num_chains)
-        _, states, _ = jax.vmap(
+        _, states = jax.vmap(
             lambda key, state: run_inference_algorithm(
                 rng_key=key,
                 initial_state=state,
                 inference_algorithm=inference_algorithm,
+                transform=lambda state, info: state,
                 num_steps=100,
             )
         )(chain_keys, last_states)
@@ -465,11 +468,12 @@ def test_chees(self, jitter_generator):
         inference_algorithm = blackjax.dynamic_hmc(logposterior_fn, **parameters)
 
         chain_keys = jax.random.split(inference_key, num_chains)
-        _, states, _ = jax.vmap(
+        _, states = jax.vmap(
             lambda key, state: run_inference_algorithm(
                 rng_key=key,
                 initial_state=state,
                 inference_algorithm=inference_algorithm,
+                transform=lambda state, info: state,
                 num_steps=100,
             )
         )(chain_keys, last_states)
@@ -494,10 +498,11 @@ def test_barker(self):
         barker = blackjax.barker_proposal(logposterior_fn, 1e-1)
         state = barker.init({"coefs": 1.0, "log_scale": 1.0})
 
-        _, states, _ = run_inference_algorithm(
+        _, states = run_inference_algorithm(
             rng_key=inference_key,
             initial_state=state,
             inference_algorithm=barker,
+            transform=lambda state, info: state,
             num_steps=10_000,
         )
 
@@ -679,10 +684,11 @@ def test_latent_gaussian(self):
 
         initial_state = inference_algorithm.init(jnp.zeros((1,)))
 
-        _, states, _ = self.variant(
+        _, states = self.variant(
             functools.partial(
                 run_inference_algorithm,
                 inference_algorithm=inference_algorithm,
+                transform=lambda state, info: state,
                 num_steps=self.sampling_steps,
             ),
         )(rng_key=self.key, initial_state=initial_state)
@@ -724,7 +730,7 @@ def univariate_normal_test_case(
         **kwargs,
     ):
         inference_key, orbit_key = jax.random.split(rng_key)
-        _, states, _ = self.variant(
+        _, (states, info) = self.variant(
             functools.partial(
                 run_inference_algorithm,
                 inference_algorithm=inference_algorithm,
@@ -855,7 +861,7 @@ def postprocess_samples(states, key):
             20_000,
             burnin,
             postprocess_samples,
-            transform=lambda x: (x.positions, x.weights),
+            transform=lambda state, info: ((state.positions, state.weights), info),
         )
 
     @chex.all_variants(with_pmap=False)
@@ -997,10 +1003,11 @@ def test_mcse(self, algorithm, parameters, is_mass_matrix_diagonal):
             functools.partial(
                 run_inference_algorithm,
                 inference_algorithm=inference_algorithm,
+                transform=lambda state, info: state,
                 num_steps=2_000,
             )
         )
-        _, states, _ = inference_loop_multiple_chains(
+        _, states = inference_loop_multiple_chains(
             rng_key=multi_chain_sample_key, initial_state=initial_states
         )