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Fixing issue Samples are outside the support for DiscreteUniform dist… #1835

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59de90f
Fixing issue Samples are outside the support for DiscreteUniform dist…
Deathn0t Jul 24, 2024
0cd448f
updated with enumerate support as padded zeros arrays
Deathn0t Jul 26, 2024
e14eea7
updating the logical using ravel to maintain a consistant behaviour
Deathn0t Jul 29, 2024
10548fe
iterating of support_sizes
Deathn0t Jul 29, 2024
f4b9d99
fixed lint issues
Deathn0t Jul 30, 2024
fe46ba1
adding test for mixed hmc sampling of distribution discrete uniform
Deathn0t Jul 30, 2024
17147bb
hmc_gibbs updated to work with different support sizes and batching, …
Deathn0t Sep 5, 2024
b1f36dc
applying format and changes similar to PR 1859
Deathn0t Sep 9, 2024
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82 changes: 72 additions & 10 deletions numpyro/infer/hmc_gibbs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,12 +192,22 @@ def __getstate__(self):


def _discrete_gibbs_proposal_body_fn(
z_init_flat, unravel_fn, pe_init, potential_fn, idx, i, val
z_init_flat,
unravel_fn,
pe_init,
potential_fn,
idx,
i,
val,
support_size,
support_enumerate,
):
rng_key, z, pe, log_weight_sum = val
rng_key, rng_transition = random.split(rng_key)
proposal = jnp.where(i >= z_init_flat[idx], i + 1, i)
z_new_flat = z_init_flat.at[idx].set(proposal)
proposal_index = jnp.where(
support_enumerate[i] == z_init_flat[idx], support_size - 1, i
)
z_new_flat = z_init_flat.at[idx].set(support_enumerate[proposal_index])
z_new = unravel_fn(z_new_flat)
pe_new = potential_fn(z_new)
log_weight_new = pe_init - pe_new
Expand All @@ -216,7 +226,9 @@ def _discrete_gibbs_proposal_body_fn(
return rng_key, z, pe, log_weight_sum


def _discrete_gibbs_proposal(rng_key, z_discrete, pe, potential_fn, idx, support_size):
def _discrete_gibbs_proposal(
rng_key, z_discrete, pe, potential_fn, idx, support_size, support_enumerate
):
# idx: current index of `z_discrete_flat` to update
# support_size: support size of z_discrete at the index idx

Expand All @@ -234,6 +246,8 @@ def _discrete_gibbs_proposal(rng_key, z_discrete, pe, potential_fn, idx, support
pe,
potential_fn,
idx,
support_size=support_size,
support_enumerate=support_enumerate,
)
init_val = (rng_key, z_discrete, pe, jnp.array(0.0))
rng_key, z_new, pe_new, _ = fori_loop(0, support_size - 1, body_fn, init_val)
Expand All @@ -242,7 +256,14 @@ def _discrete_gibbs_proposal(rng_key, z_discrete, pe, potential_fn, idx, support


def _discrete_modified_gibbs_proposal(
rng_key, z_discrete, pe, potential_fn, idx, support_size, stay_prob=0.0
rng_key,
z_discrete,
pe,
potential_fn,
idx,
support_size,
support_enumerate,
stay_prob=0.0,
):
assert isinstance(stay_prob, float) and stay_prob >= 0.0 and stay_prob < 1
z_discrete_flat, unravel_fn = ravel_pytree(z_discrete)
Expand All @@ -253,6 +274,8 @@ def _discrete_modified_gibbs_proposal(
pe,
potential_fn,
idx,
support_size=support_size,
support_enumerate=support_enumerate,
)
# like gibbs_step but here, weight of the current value is 0
init_val = (rng_key, z_discrete, pe, jnp.array(-jnp.inf))
Expand All @@ -276,28 +299,41 @@ def _discrete_modified_gibbs_proposal(
return rng_key, z_new, pe_new, log_accept_ratio


def _discrete_rw_proposal(rng_key, z_discrete, pe, potential_fn, idx, support_size):
def _discrete_rw_proposal(
rng_key, z_discrete, pe, potential_fn, idx, support_size, support_enumerate
):
rng_key, rng_proposal = random.split(rng_key, 2)
z_discrete_flat, unravel_fn = ravel_pytree(z_discrete)

proposal = random.randint(rng_proposal, (), minval=0, maxval=support_size)
z_new_flat = z_discrete_flat.at[idx].set(proposal)
z_new_flat = z_discrete_flat.at[idx].set(support_enumerate[proposal])
z_new = unravel_fn(z_new_flat)
pe_new = potential_fn(z_new)
log_accept_ratio = pe - pe_new
return rng_key, z_new, pe_new, log_accept_ratio


def _discrete_modified_rw_proposal(
rng_key, z_discrete, pe, potential_fn, idx, support_size, stay_prob=0.0
rng_key,
z_discrete,
pe,
potential_fn,
idx,
support_size,
support_enumerate,
stay_prob=0.0,
):
assert isinstance(stay_prob, float) and stay_prob >= 0.0 and stay_prob < 1
rng_key, rng_proposal, rng_stay = random.split(rng_key, 3)
z_discrete_flat, unravel_fn = ravel_pytree(z_discrete)

i = random.randint(rng_proposal, (), minval=0, maxval=support_size - 1)
proposal = jnp.where(i >= z_discrete_flat[idx], i + 1, i)
proposal = jnp.where(random.bernoulli(rng_stay, stay_prob), idx, proposal)
proposal_index = jnp.where(
support_enumerate[i] == z_discrete_flat[idx], support_size - 1, i
)
proposal = jnp.where(
random.bernoulli(rng_stay, stay_prob), idx, support_enumerate[proposal_index]
)
z_new_flat = z_discrete_flat.at[idx].set(proposal)
z_new = unravel_fn(z_new_flat)
pe_new = potential_fn(z_new)
Expand Down Expand Up @@ -434,6 +470,32 @@ def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):
and site["fn"].has_enumerate_support
and not site["is_observed"]
}
max_length_support_enumerates = max(
(
site["fn"].enumerate_support(False).shape[0]
for site in self._prototype_trace.values()
if site["type"] == "sample"
and site["fn"].has_enumerate_support
and not site["is_observed"]
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@fehiepsi fehiepsi Jul 29, 2024

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nit: it is better to loop over support_sizes: for name, site in self._prototype_trace.items() if name in support_sizes

)
)
# All support_enumerates should have the same length to be used in the loop
# Each support is padded with zeros to have the same length
self._support_enumerates = np.zeros(
(len(self._support_sizes), max_length_support_enumerates), dtype=int
)
for i, (name, site) in enumerate(self._prototype_trace.items()):
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@fehiepsi fehiepsi Jul 27, 2024 •
edited
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great solution! I just have a couple of comments:

  • it might be better to loop over names in support_sizes and get site via site = self._prototype_trace[name]
  • we use ravel_pytree to flatten support_sizes. so we might want to keep the same behavior here. I don't have a great solution for this, maybe
support_enumerates = {}
for name, support_size in self._support_sizes.items():
    site = self._prototype_trace[name]
    enumerate_support = site["fn"].enumerate_support(False)
    padded_enumerate_support = np.pad(enumerate_support, (0, max_length_support_enumerates - enumerate_support.shape[0]))
    padded_enumerate_support = np.broadcast_to(padded_enumerate_support, support_size.shape + (max_length_support_enumerates,))
    support_enumerates[name] = padded_enumerate_support

self._support_enumerates = jax.vmap(lambda x: ravel_pytree(x)[0], in_axes=1, out_axes=1)(support_enumerates)

if (
site["type"] == "sample"
and site["fn"].has_enumerate_support
and not site["is_observed"]
):
self._support_enumerates[
i, : site["fn"].enumerate_support(False).shape[0]
] = site["fn"].enumerate_support(False)
self._support_enumerates = jnp.asarray(
self._support_enumerates, dtype=jnp.int32
)
self._gibbs_sites = [
name
for name, site in self._prototype_trace.items()
Expand Down
2 changes: 2 additions & 0 deletions numpyro/infer/mixed_hmc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@

from jax import grad, jacfwd, lax, random
from jax.flatten_util import ravel_pytree
import jax
import jax.numpy as jnp

from numpyro.infer.hmc import momentum_generator
Expand Down Expand Up @@ -138,6 +139,7 @@ def update_discrete(
partial(potential_fn, z_hmc=hmc_state.z),
idx,
self._support_sizes_flat[idx],
self._support_enumerates[idx],
)
# Algo 1, line 20: depending on reject or refract, we will update
# the discrete variable and its corresponding kinetic energy. In case of
Expand Down