from collections import namedtuple, OrderedDict
from contextlib import contextmanager
import os
import random
import re
import numpy as onp
import tqdm
import jax
from jax import device_put, jit, lax, ops, vmap
from jax.interpreters.batching import BatchTracer
from jax.interpreters.partial_eval import JaxprTracer
from jax.dtypes import canonicalize_dtype
import jax.numpy as np
from jax.tree_util import tree_flatten, tree_map, tree_unflatten
_DATA_TYPES = {}
_DISABLE_CONTROL_FLOW_PRIM = False
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.
"""
random.seed(rng_seed)
onp.random.seed(rng_seed)
[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_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 <https://github.com/google/jax/issues/1408>`_.
: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
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 = np.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 = np.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
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