Source code for numpyro.util

# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0

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

import numpy as np
import tqdm

import jax
from jax import device_put, jit, lax, ops, vmap
from jax.core import Tracer
from jax.dtypes import canonicalize_dtype
import jax.numpy as jnp
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 enable_x64(use_x64=True): """ Changes the default array type to use 64 bit precision as in NumPy. :param bool use_x64: when `True`, JAX arrays will use 64 bits by default; else 32 bits. """ if not use_x64: use_x64 = os.getenv('JAX_ENABLE_X64', 0) jax.config.update('jax_enable_x64', use_x64)
[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, Tracer) def identity(x, *args, **kwargs): return x def cached_by(outer_fn, *keys): # Restrict cache size to prevent ref cycles. max_size = 8 outer_fn._cache = getattr(outer_fn, '_cache', OrderedDict()) def _wrapped(fn): fn_cache = outer_fn._cache if keys in fn_cache: fn = fn_cache[keys] # update position del fn_cache[keys] fn_cache[keys] = fn else: fn_cache[keys] = fn if len(fn_cache) > max_size: fn_cache.popitem(last=False) return fn return _wrapped
[docs]def fori_collect(lower, upper, body_fun, init_val, transform=identity, progbar=True, return_last_val=False, collection_size=None, **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 bool return_last_val: If `True`, the last value is also returned. This has the same type as `init_val`. :param int collection_size: Size of the returned collection. If not specified, the size will be ``upper - lower``. If the size is larger than ``upper - lower``, only the top ``upper - lower`` entries will be non-zero. :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 collection_size = upper - lower if collection_size is None else collection_size assert collection_size >= upper - lower init_val_flat, unravel_fn = ravel_pytree(transform(init_val)) @cached_by(fori_collect, body_fun, transform) def _body_fn(i, vals): val, collection, lower_idx = vals val = body_fun(val) i = jnp.where(i >= lower_idx, i - lower_idx, 0) collection = ops.index_update(collection, i, ravel_pytree(transform(val))[0]) return val, collection, lower_idx collection = jnp.zeros((collection_size,) + init_val_flat.shape) if not progbar: last_val, collection, _ = fori_loop(0, upper, _body_fn, (init_val, collection, lower)) else: diagnostics_fn = progbar_opts.pop('diagnostics_fn', None) progbar_desc = progbar_opts.pop('progbar_desc', lambda x: '') vals = (init_val, collection, device_put(lower)) if upper == 0: # special case, only compiling jit(_body_fn)(0, vals) else: with tqdm.trange(upper) as t: for i in t: vals = jit(_body_fn)(i, vals) t.set_description(progbar_desc(i), refresh=False) if diagnostics_fn: t.set_postfix_str(diagnostics_fn(vals[0]), refresh=False) last_val, collection, _ = vals unravel_collection = vmap(unravel_fn)(collection) return (unravel_collection, last_val) if return_last_val else unravel_collection
pytree_metadata = namedtuple('pytree_metadata', ['flat', 'shape', 'size', 'dtype']) def _ravel_list(*leaves): leaves_metadata = tree_map(lambda l: pytree_metadata( jnp.ravel(l), jnp.shape(l), jnp.size(l), canonicalize_dtype(lax.dtype(l))), leaves) leaves_idx = jnp.cumsum(jnp.array((0,) + tuple(d.size for d in leaves_metadata))) def unravel_list(arr): return [jnp.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 = jnp.concatenate([m.flat for m in leaves_metadata]) if leaves_metadata else jnp.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 def soft_vmap(fn, xs, batch_ndims=1, chunk_size=None): """ Vectorizing map that maps a function `fn` over `batch_ndims` leading axes of `xs`. This uses jax.vmap over smaller chunks of the batch dimensions to keep memory usage constant. :param callable fn: The function to map over. :param xs: JAX pytree (e.g. an array, a list/tuple/dict of arrays,...) :param int batch_ndims: The number of leading dimensions of `xs` to apply `fn` element-wise over them. :param int chunk_size: Size of each chunk of `xs`. Defaults to the size of batch dimensions. :returns: output of `fn(xs)`. """ flatten_xs = tree_flatten(xs)[0] batch_shape = np.shape(flatten_xs[0])[:batch_ndims] for x in flatten_xs[1:]: assert np.shape(x)[:batch_ndims] == batch_shape # we'll do map(vmap(fn), xs) and make xs.shape = (num_chunks, chunk_size, ...) num_chunks = batch_size = int( prepend_shape = (-1,) if batch_size > 1 else () xs = tree_map(lambda x: jnp.reshape(x, prepend_shape + jnp.shape(x)[batch_ndims:]), xs) # XXX: probably for the default behavior with chunk_size=None, # it is better to catch OOM error and reduce chunk_size by half until OOM disappears. chunk_size = batch_size if chunk_size is None else min(batch_size, chunk_size) if chunk_size > 1: pad = chunk_size - (batch_size % chunk_size) xs = tree_map(lambda x: jnp.pad(x, ((0, pad),) + ((0, 0),) * (np.ndim(x) - 1)), xs) num_chunks = batch_size // chunk_size + int(pad > 0) prepend_shape = (-1,) if num_chunks > 1 else () xs = tree_map(lambda x: jnp.reshape(x, prepend_shape + (chunk_size,) + jnp.shape(x)[1:]), xs) fn = vmap(fn) ys =, xs) if num_chunks > 1 else fn(xs) map_ndims = int(num_chunks > 1) + int(chunk_size > 1) ys = tree_map(lambda y: jnp.reshape(y, (-1,) + jnp.shape(y)[map_ndims:])[:batch_size], ys) return tree_map(lambda y: jnp.reshape(y, batch_shape + jnp.shape(y)[1:]), ys)