Source code for numpyro.util

from collections import namedtuple
from contextlib import contextmanager
import os
import random
import re

import numpy as onp
import tqdm

import jax
from jax import jit, lax, ops, vmap
from jax.interpreters.batching import BatchTracer
from jax.interpreters.partial_eval import JaxprTracer
from jax.lib.xla_bridge import canonicalize_dtype
import jax.numpy as np
from jax.tree_util import tree_flatten, tree_map, tree_unflatten


def set_rng_seed(rng_seed):
    Initializes internal state for the Python and NumPy random number generators.

    :param int rng_seed: seed for Python and NumPy random states.

[docs]def set_platform(platform=None): """ Changes platform to CPU, GPU, or TPU. This utility only takes effect at the beginning of your program. :param str platform: either 'cpu', 'gpu', or 'tpu'. """ if platform is None: platform = os.getenv('JAX_PLATFORM_NAME', 'cpu') jax.config.update('jax_platform_name', platform)
[docs]def set_host_device_count(n): """ By default, XLA considers all CPU cores as one device. This utility tells XLA that there are `n` host (CPU) devices available to use. As a consequence, this allows parallel mapping in JAX :func:`jax.pmap` to work in CPU platform. .. note:: This utility only takes effect at the beginning of your program. Under the hood, this sets the environment variable `XLA_FLAGS=--xla_force_host_platform_device_count=[num_devices]`, where `[num_device]` is the desired number of CPU devices `n`. .. warning:: Our understanding of the side effects of using the `xla_force_host_platform_device_count` flag in XLA is incomplete. If you observe some strange phenomenon when using this utility, please let us know through our issue or forum page. More information is available in this `JAX issue <>`_. :param int n: number of CPU devices to use. """ 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(n)] + xla_flags)
@contextmanager def optional(condition, context_manager): """ Optionally wrap inside `context_manager` if condition is `True`. """ if condition: with context_manager: yield else: yield @contextmanager def control_flow_prims_disabled(): global _DISABLE_CONTROL_FLOW_PRIM stored_flag = _DISABLE_CONTROL_FLOW_PRIM try: _DISABLE_CONTROL_FLOW_PRIM = True yield finally: _DISABLE_CONTROL_FLOW_PRIM = stored_flag def cond(pred, true_operand, true_fun, false_operand, false_fun): if _DISABLE_CONTROL_FLOW_PRIM: if pred: return true_fun(true_operand) else: return false_fun(false_operand) else: return lax.cond(pred, true_operand, true_fun, false_operand, false_fun) def while_loop(cond_fun, body_fun, init_val): if _DISABLE_CONTROL_FLOW_PRIM: val = init_val while cond_fun(val): val = body_fun(val) return val else: return lax.while_loop(cond_fun, body_fun, init_val) def fori_loop(lower, upper, body_fun, init_val): if _DISABLE_CONTROL_FLOW_PRIM: val = init_val for i in range(int(lower), int(upper)): val = body_fun(i, val) return val else: return lax.fori_loop(lower, upper, body_fun, init_val) def not_jax_tracer(x): """ Checks if `x` is not an array generated inside `jit`, `pmap`, `vmap`, or `lax_control_flow`. """ return not isinstance(x, (JaxprTracer, BatchTracer)) def identity(x): return x
[docs]def fori_collect(lower, upper, body_fun, init_val, transform=identity, progbar=True, **progbar_opts): """ This looping construct works like :func:`~jax.lax.fori_loop` but with the additional effect of collecting values from the loop body. In addition, this allows for post-processing of these samples via `transform`, and progress bar updates. Note that, `progbar=False` will be faster, especially when collecting a lot of samples. Refer to example usage in :func:`~numpyro.infer.mcmc.hmc`. :param int lower: the index to start the collective work. In other words, we will skip collecting the first `lower` values. :param int upper: number of times to run the loop body. :param body_fun: a callable that takes a collection of `np.ndarray` and returns a collection with the same shape and `dtype`. :param init_val: initial value to pass as argument to `body_fun`. Can be any Python collection type containing `np.ndarray` objects. :param transform: a callable to post-process the values returned by `body_fn`. :param progbar: whether to post progress bar updates. :param `**progbar_opts`: optional additional progress bar arguments. A `diagnostics_fn` can be supplied which when passed the current value from `body_fun` returns a string that is used to update the progress bar postfix. Also a `progbar_desc` keyword argument can be supplied which is used to label the progress bar. :return: collection with the same type as `init_val` with values collected along the leading axis of `np.ndarray` objects. """ assert lower < upper init_val_flat, unravel_fn = ravel_pytree(transform(init_val)) ravel_fn = lambda x: ravel_pytree(transform(x))[0] # noqa: E731 if not progbar: collection = np.zeros((upper - lower,) + init_val_flat.shape) def _body_fn(i, vals): val, collection = vals val = body_fun(val) i = np.where(i >= lower, i - lower, 0) collection = ops.index_update(collection, i, ravel_fn(val)) return val, collection _, collection = fori_loop(0, upper, _body_fn, (init_val, collection)) else: diagnostics_fn = progbar_opts.pop('diagnostics_fn', None) progbar_desc = progbar_opts.pop('progbar_desc', lambda x: '') collection = [] val = init_val with tqdm.trange(upper) as t: for i in t: val = jit(body_fun)(val) if i >= lower: collection.append(jit(ravel_fn)(val)) t.set_description(progbar_desc(i), refresh=False) if diagnostics_fn: t.set_postfix_str(diagnostics_fn(val), refresh=False) collection = np.stack(collection) return vmap(unravel_fn)(collection)
def copy_docs_from(source_class, full_text=False): """ Decorator to copy class and method docs from source to destin class. """ def decorator(destin_class): # This works only in python 3.3+: # if not destin_class.__doc__: # destin_class.__doc__ = source_class.__doc__ for name in dir(destin_class): if name.startswith('_'): continue destin_attr = getattr(destin_class, name) destin_attr = getattr(destin_attr, '__func__', destin_attr) source_attr = getattr(source_class, name, None) source_doc = getattr(source_attr, '__doc__', None) if source_doc and not getattr(destin_attr, '__doc__', None): if full_text or source_doc.startswith('See '): destin_doc = source_doc else: destin_doc = 'See :meth:`{}.{}.{}`'.format( source_class.__module__, source_class.__name__, name) if isinstance(destin_attr, property): # Set docs for object properties. # Since __doc__ is read-only, we need to reset the property # with the updated doc. updated_property = property(destin_attr.fget, destin_attr.fset, destin_attr.fdel, destin_doc) setattr(destin_class, name, updated_property) else: destin_attr.__doc__ = destin_doc return destin_class return decorator pytree_metadata = namedtuple('pytree_metadata', ['flat', 'shape', 'size', 'dtype']) def _ravel_list(*leaves): leaves_metadata = tree_map(lambda l: pytree_metadata( np.ravel(l), np.shape(l), np.size(l), canonicalize_dtype(lax.dtype(l))), leaves) leaves_idx = np.cumsum(np.array((0,) + tuple(d.size for d in leaves_metadata))) def unravel_list(arr): return [np.reshape(lax.dynamic_slice_in_dim(arr, leaves_idx[i], m.size), m.shape).astype(m.dtype) for i, m in enumerate(leaves_metadata)] flat = np.concatenate([m.flat for m in leaves_metadata]) if leaves_metadata else np.array([]) return flat, unravel_list def ravel_pytree(pytree): leaves, treedef = tree_flatten(pytree) flat, unravel_list = _ravel_list(*leaves) def unravel_pytree(arr): return tree_unflatten(treedef, unravel_list(arr)) return flat, unravel_pytree