def testing(): for i in markov(range(5)): v1 = to_data(Tensor(jnp.ones(2), OrderedDict([(str(i), bint(2))]), 'real')) v2 = to_data(Tensor(jnp.zeros(2), OrderedDict([('a', bint(2))]), 'real')) fv1 = to_funsor(v1, reals()) fv2 = to_funsor(v2, reals()) print(i, v1.shape) # shapes should alternate if i % 2 == 0: assert v1.shape == (2,) else: assert v1.shape == (2, 1, 1) assert v2.shape == (2, 1) print(i, fv1.inputs) print('a', v2.shape) # shapes should stay the same print('a', fv2.inputs)
def testing(): for i in markov(range(12)): if i % 4 == 0: v2 = to_data(Tensor(jnp.zeros(2), OrderedDict([('a', bint(2))]), 'real')) fv2 = to_funsor(v2, reals()) assert v2.shape == (2,) print('a', v2.shape) print('a', fv2.inputs)
def testing(): for i in markov(range(5)): v1 = to_data( Tensor(jnp.ones(2), OrderedDict([(str(i), Bint[2])]), "real")) v2 = to_data( Tensor(jnp.zeros(2), OrderedDict([("a", Bint[2])]), "real")) fv1 = to_funsor(v1, Real) fv2 = to_funsor(v2, Real) print(i, v1.shape) # shapes should alternate if i % 2 == 0: assert v1.shape == (2, ) else: assert v1.shape == (2, 1, 1) assert v2.shape == (2, 1) print(i, fv1.inputs) print("a", v2.shape) # shapes should stay the same print("a", fv2.inputs)
def testing(): for i in markov(range(12)): if i % 4 == 0: v2 = to_data( Tensor(jnp.zeros(2), OrderedDict([("a", Bint[2])]), "real")) fv2 = to_funsor(v2, Real) assert v2.shape == (2, ) print("a", v2.shape) print("a", fv2.inputs)
def scan(f, init, xs, length=None, reverse=False, history=1): """ This primitive scans a function over the leading array axes of `xs` while carrying along state. See :func:`jax.lax.scan` for more information. **Usage**: .. doctest:: >>> import numpy as np >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.contrib.control_flow import scan >>> >>> def gaussian_hmm(y=None, T=10): ... def transition(x_prev, y_curr): ... x_curr = numpyro.sample('x', dist.Normal(x_prev, 1)) ... y_curr = numpyro.sample('y', dist.Normal(x_curr, 1), obs=y_curr) ... return x_curr, (x_curr, y_curr) ... ... x0 = numpyro.sample('x_0', dist.Normal(0, 1)) ... _, (x, y) = scan(transition, x0, y, length=T) ... return (x, y) >>> >>> # here we do some quick tests >>> with numpyro.handlers.seed(rng_seed=0): ... x, y = gaussian_hmm(np.arange(10.)) >>> assert x.shape == (10,) and y.shape == (10,) >>> assert np.all(y == np.arange(10)) >>> >>> with numpyro.handlers.seed(rng_seed=0): # generative ... x, y = gaussian_hmm() >>> assert x.shape == (10,) and y.shape == (10,) .. warning:: This is an experimental utility function that allows users to use JAX control flow with NumPyro's effect handlers. Currently, `sample` and `deterministic` sites within the scan body `f` are supported. If you notice that any effect handlers or distributions are unsupported, please file an issue. .. note:: It is ambiguous to align `scan` dimension inside a `plate` context. So the following pattern won't be supported .. code-block:: python with numpyro.plate('N', 10): last, ys = scan(f, init, xs) All `plate` statements should be put inside `f`. For example, the corresponding working code is .. code-block:: python def g(*args, **kwargs): with numpyro.plate('N', 10): return f(*arg, **kwargs) last, ys = scan(g, init, xs) .. note:: Nested scan is currently not supported. .. note:: We can scan over discrete latent variables in `f`. The joint density is evaluated using parallel-scan (reference [1]) over time dimension, which reduces parallel complexity to `O(log(length))`. A :class:`~numpyro.handlers.trace` of `scan` with discrete latent variables will contain the following sites: + init sites: those sites belong to the first `history` traces of `f`. Sites at the `i`-th trace will have name prefixed with `'_PREV_' * (2 * history - 1 - i)`. + scanned sites: those sites collect the values of the remaining scan loop over `f`. An addition time dimension `_time_foo` will be added to those sites, where `foo` is the name of the first site appeared in `f`. Not all transition functions `f` are supported. All of the restrictions from Pyro's enumeration tutorial [2] still apply here. In addition, there should not have any site outside of `scan` depend on the first output of `scan` (the last carry value). ** References ** 1. *Temporal Parallelization of Bayesian Smoothers*, Simo Sarkka, Angel F. Garcia-Fernandez (https://arxiv.org/abs/1905.13002) 2. *Inference with Discrete Latent Variables* (http://pyro.ai/examples/enumeration.html#Dependencies-among-plates) :param callable f: a function to be scanned. :param init: the initial carrying state :param xs: the values over which we scan along the leading axis. This can be any JAX pytree (e.g. list/dict of arrays). :param length: optional value specifying the length of `xs` but can be used when `xs` is an empty pytree (e.g. None) :param bool reverse: optional boolean specifying whether to run the scan iteration forward (the default) or in reverse :param int history: The number of previous contexts visible from the current context. Defaults to 1. If zero, this is similar to :class:`numpyro.plate`. :return: output of scan, quoted from :func:`jax.lax.scan` docs: "pair of type (c, [b]) where the first element represents the final loop carry value and the second element represents the stacked outputs of the second output of f when scanned over the leading axis of the inputs". """ # if there are no active Messengers, we just run and return it as expected: if not _PYRO_STACK: (length, rng_key, carry), (pytree_trace, ys) = scan_wrapper(f, init, xs, length=length, reverse=reverse) return carry, ys else: # Otherwise, we initialize a message... initial_msg = { "type": "control_flow", "fn": scan_wrapper, "args": (f, init, xs, length, reverse), "kwargs": { "rng_key": None, "substitute_stack": [], "history": history }, "value": None, } # ...and use apply_stack to send it to the Messengers msg = apply_stack(initial_msg) (length, rng_key, carry), (pytree_trace, ys) = msg["value"] if not msg["kwargs"].get("enum", False): for msg in pytree_trace.trace.values(): apply_stack(msg) else: from numpyro.contrib.funsor import to_funsor from numpyro.contrib.funsor.enum_messenger import LocalNamedMessenger for msg in pytree_trace.trace.values(): with LocalNamedMessenger(): dim_to_name = msg["infer"].get("dim_to_name") to_funsor( msg["value"], dim_to_name=OrderedDict([(k, dim_to_name[k]) for k in sorted(dim_to_name)]), ) apply_stack(msg) return carry, ys