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Also makes a new release. PiperOrigin-RevId: 606642479
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| Original file line number | Diff line number | Diff line change |
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| # Copyright 2024 The bayeux Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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||
| # Copyright 2024 The bayeux Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| """NumPyro specific code.""" | ||
|
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| import arviz as az | ||
| from bayeux._src import shared | ||
| import jax | ||
| import numpy as np | ||
| import tensorflow_probability.substrates.jax as tfp | ||
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| class SnaperHMC(shared.Base): | ||
| """Implements SNAPER HMC [1] with step size adaptation. | ||
| [1]: Sountsov, P. & Hoffman, M. (2021). Focusing on Difficult Directions for | ||
| Learning HMC Trajectory Lengths. <https://arxiv.org/abs/2110.11576> | ||
| """ | ||
| name = "tfp_snaper_hmc" | ||
|
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| def get_kwargs(self, **kwargs): | ||
| kwargs_with_defaults = { | ||
| "num_results": 1_000, | ||
| "num_chains": 8, | ||
| } | kwargs | ||
| snaper = tfp.experimental.mcmc.sample_snaper_hmc | ||
| snaper_kwargs, snaper_required = shared.get_default_signature(snaper) | ||
| snaper_kwargs.update({k: kwargs_with_defaults[k] for k in snaper_required | ||
| if k in kwargs_with_defaults}) | ||
| snaper_required.remove("model") | ||
| # Initial state is handled internally | ||
| snaper_kwargs.pop("init_state") | ||
| # Seed set later | ||
| snaper_kwargs.pop("seed") | ||
|
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| snaper_required = snaper_required - snaper_kwargs.keys() | ||
|
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| if snaper_required: | ||
| raise ValueError(f"Unexpected required arguments: " | ||
| f"{','.join(snaper_required)}. Probably file a bug, but " | ||
| "you can try to manually supply them as keywords.") | ||
| snaper_kwargs.update({k: kwargs_with_defaults[k] for k in snaper_kwargs | ||
| if k in kwargs_with_defaults}) | ||
| return { | ||
| snaper: snaper_kwargs, | ||
| "extra_parameters": { | ||
| "return_pytree": kwargs.get("return_pytree", False) | ||
| }, | ||
| } | ||
|
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| def __call__(self, seed, **kwargs): | ||
| snaper = tfp.experimental.mcmc.sample_snaper_hmc | ||
| init_key, sample_key = jax.random.split(seed) | ||
| kwargs = self.get_kwargs(**kwargs) | ||
| initial_state = self.get_initial_state( | ||
| init_key, num_chains=kwargs[snaper]["num_chains"]) | ||
|
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| vmapped_constrained_log_prob = jax.vmap(self.constrained_log_density()) | ||
|
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| def tlp(*args, **kwargs): | ||
| if args: | ||
| return vmapped_constrained_log_prob(args) | ||
| else: | ||
| return vmapped_constrained_log_prob(kwargs) | ||
|
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| (draws, trace), *_ = snaper( | ||
| model=tlp, init_state=initial_state, seed=sample_key, **kwargs[snaper] | ||
| ) | ||
| draws = self.transform_fn(draws) | ||
| if kwargs["extra_parameters"]["return_pytree"]: | ||
| return draws | ||
|
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||
| if hasattr(draws, "_asdict"): | ||
| draws = draws._asdict() | ||
| elif not isinstance(draws, dict): | ||
| draws = {"var0": draws} | ||
|
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| draws = {x: np.swapaxes(v, 0, 1) for x, v in draws.items()} | ||
| return az.from_dict(posterior=draws, sample_stats=_tfp_stats_to_dict(trace)) | ||
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| def _tfp_stats_to_dict(stats): | ||
| new_stats = {} | ||
| for k, v in stats.items(): | ||
| if k == "variance_scaling": | ||
| continue | ||
| if np.ndim(v) > 1: | ||
| new_stats[k] = np.swapaxes(v, 0, 1) | ||
| else: | ||
| new_stats[k] = v | ||
| return new_stats |
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