Merge branch 'ciguaran_waste_free_smc' of github.com:ciguaran/blackja…

…x into ciguaran_waste_free_smc

README.md

@@ -75,9 +75,10 @@ state = nuts.init(initial_position)
 
 # Iterate
 rng_key = jax.random.key(0)
-for step in range(100):
-    nuts_key = jax.random.fold_in(rng_key, step)
-    state, _ = nuts.step(nuts_key, state)
+step = jax.jit(nuts.step)
+for i in range(100):
+    nuts_key = jax.random.fold_in(rng_key, i)
+    state, _ = step(nuts_key, state)
 ```
 
 See [the documentation](https://blackjax-devs.github.io/blackjax/index.html) for more examples of how to use the library: how to write inference loops for one or several chains, how to use the Stan warmup, etc.

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/adaptation/window_adaptation.py

@@ -28,7 +28,7 @@
     dual_averaging_adaptation,
 )
 from blackjax.base import AdaptationAlgorithm
-from blackjax.progress_bar import progress_bar_scan
+from blackjax.progress_bar import gen_scan_fn
 from blackjax.types import Array, ArrayLikeTree, PRNGKey
 from blackjax.util import pytree_size
 
@@ -333,17 +333,16 @@ def run(rng_key: PRNGKey, position: ArrayLikeTree, num_steps: int = 1000):
 
         if progress_bar:
             print("Running window adaptation")
-            one_step_ = jax.jit(progress_bar_scan(num_steps)(one_step))
-        else:
-            one_step_ = jax.jit(one_step)
-
+        scan_fn = gen_scan_fn(num_steps, progress_bar=progress_bar)
+        start_state = (init_state, init_adaptation_state)
         keys = jax.random.split(rng_key, num_steps)
         schedule = build_schedule(num_steps)
-        last_state, info = jax.lax.scan(
-            one_step_,
-            (init_state, init_adaptation_state),
+        last_state, info = scan_fn(
+            one_step,
+            start_state,
             (jnp.arange(num_steps), keys, schedule),
         )
+
         last_chain_state, last_warmup_state, *_ = last_state
 
         step_size, inverse_mass_matrix = adapt_final(last_warmup_state)

blackjax/progress_bar.py

@@ -14,56 +14,64 @@
 """Progress bar decorators for use with step functions.
 Adapted from Jeremie Coullon's blog post :cite:p:`progress_bar`.
 """
+from threading import Lock
+
 from fastprogress.fastprogress import progress_bar
 from jax import lax
 from jax.experimental import io_callback
+from jax.numpy import array
 
 
 def progress_bar_scan(num_samples, print_rate=None):
     "Progress bar for a JAX scan"
     progress_bars = {}
+    idx_counter = 0
+    lock = Lock()
 
     if print_rate is None:
         if num_samples > 20:
             print_rate = int(num_samples / 20)
         else:
             print_rate = 1  # if you run the sampler for less than 20 iterations
 
-    def _define_bar(arg):
-        del arg
-        progress_bars[0] = progress_bar(range(num_samples))
-        progress_bars[0].update(0)
+    def _calc_chain_idx(iter_num):
+        nonlocal idx_counter
+        with lock:
+            idx = idx_counter
+            idx_counter += 1
+        return idx
+
+    def _update_bar(arg, chain_id):
+        chain_id = int(chain_id)
+        if arg == 0:
+            chain_id = _calc_chain_idx(arg)
+            progress_bars[chain_id] = progress_bar(range(num_samples))
+            progress_bars[chain_id].update(0)
 
-    def _update_bar(arg):
-        progress_bars[0].update_bar(arg + 1)
+        progress_bars[chain_id].update_bar(arg + 1)
+        return chain_id
 
-    def _close_bar(arg):
-        del arg
-        progress_bars[0].on_iter_end()
+    def _close_bar(arg, chain_id):
+        progress_bars[int(chain_id)].on_iter_end()
 
-    def _update_progress_bar(iter_num):
+    def _update_progress_bar(iter_num, chain_id):
         "Updates progress bar of a JAX scan or loop"
-        _ = lax.cond(
-            iter_num == 0,
-            lambda _: io_callback(_define_bar, None, iter_num),
-            lambda _: None,
-            operand=None,
-        )
 
-        _ = lax.cond(
+        chain_id = lax.cond(
             # update every multiple of `print_rate` except at the end
             (iter_num % print_rate == 0) | (iter_num == (num_samples - 1)),
-            lambda _: io_callback(_update_bar, None, iter_num),
-            lambda _: None,
+            lambda _: io_callback(_update_bar, array(0), iter_num, chain_id),
+            lambda _: chain_id,
             operand=None,
         )
 
         _ = lax.cond(
             iter_num == num_samples - 1,
-            lambda _: io_callback(_close_bar, None, None),
+            lambda _: io_callback(_close_bar, None, iter_num + 1, chain_id),
             lambda _: None,
             operand=None,
         )
+        return chain_id
 
     def _progress_bar_scan(func):
         """Decorator that adds a progress bar to `body_fun` used in `lax.scan`.
@@ -77,9 +85,26 @@ def wrapper_progress_bar(carry, x):
                 iter_num, *_ = x
             else:
                 iter_num = x
-            _update_progress_bar(iter_num)
-            return func(carry, x)
+            subcarry, chain_id = carry
+            chain_id = _update_progress_bar(iter_num, chain_id)
+            subcarry, y = func(subcarry, x)
+
+            return (subcarry, chain_id), y
 
         return wrapper_progress_bar
 
     return _progress_bar_scan
+
+
+def gen_scan_fn(num_samples, progress_bar, print_rate=None):
+    if progress_bar:
+
+        def scan_wrap(f, init, *args, **kwargs):
+            func = progress_bar_scan(num_samples, print_rate)(f)
+            carry = (init, -1)
+            (last_state, _), output = lax.scan(func, carry, *args, **kwargs)
+            return last_state, output
+
+        return scan_wrap
+    else:
+        return lax.scan