# Poutine (Effect handlers)¶

Beneath the built-in inference algorithms, Pyro has a library of composable effect handlers for creating new inference algorithms and working with probabilistic programs. Pyro’s inference algorithms are all built by applying these handlers to stochastic functions. In order to get a general understanding what effect handlers are and what problem they solve, read An Introduction to Algebraic Effects and Handlers by Matija Pretnar.

## Handlers¶

Poutine is a library of composable effect handlers for recording and modifying the behavior of Pyro programs. These lower-level ingredients simplify the implementation of new inference algorithms and behavior.

Handlers can be used as higher-order functions, decorators, or context managers to modify the behavior of functions or blocks of code:

For example, consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

We can mark sample sites as observed using `condition`, which returns a callable with the same input and output signatures as `model`:

```>>> conditioned_model = poutine.condition(model, data={"z": 1.0})
```

We can also use handlers as decorators:

```>>> @pyro.condition(data={"z": 1.0})
... def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

Or as context managers:

```>>> with pyro.condition(data={"z": 1.0}):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(0., s))
...     y = z ** 2
```

Handlers compose freely:

```>>> conditioned_model = poutine.condition(model, data={"z": 1.0})
>>> traced_model = poutine.trace(conditioned_model)
```

Many inference algorithms or algorithmic components can be implemented in just a few lines of code:

```guide_tr = poutine.trace(guide).get_trace(...)
model_tr = poutine.trace(poutine.replay(conditioned_model, trace=guide_tr)).get_trace(...)
monte_carlo_elbo = model_tr.log_prob_sum() - guide_tr.log_prob_sum()
```
block(fn: None = None, hide_fn: Optional[Callable[[Message], Optional[bool]]] = None, expose_fn: Optional[Callable[[Message], Optional[bool]]] = None, hide_all: bool = True, expose_all: bool = False, hide: Optional[List[str]] = None, expose: Optional[List[str]] = None, hide_types: Optional[List[str]] = None, expose_types: Optional[List[str]] = None) [source]
block(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], hide_fn: Optional[Callable[[Message], Optional[bool]]] = None, expose_fn: Optional[Callable[[Message], Optional[bool]]] = None, hide_all: bool = True, expose_all: bool = False, hide: Optional[List[str]] = None, expose: Optional[List[str]] = None, hide_types: Optional[List[str]] = None, expose_types: Optional[List[str]] = None) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `BlockMessenger`

This handler selectively hides Pyro primitive sites from the outside world. Default behavior: block everything.

A site is hidden if at least one of the following holds:

1. `hide_fn(msg) is True` or `(not expose_fn(msg)) is True`

2. `msg["name"] in hide`

3. `msg["type"] in hide_types`

4. `msg["name"] not in expose and msg["type"] not in expose_types`

5. `hide`, `hide_types`, and `expose_types` are all `None`

For example, suppose the stochastic function fn has two sample sites “a” and “b”. Then any effect outside of `BlockMessenger(fn, hide=["a"])` will not be applied to site “a” and will only see site “b”:

```>>> def fn():
...     a = pyro.sample("a", dist.Normal(0., 1.))
...     return pyro.sample("b", dist.Normal(a, 1.))
>>> fn_inner = pyro.poutine.trace(fn)
>>> fn_outer = pyro.poutine.trace(pyro.poutine.block(fn_inner, hide=["a"]))
>>> trace_inner = fn_inner.get_trace()
>>> trace_outer  = fn_outer.get_trace()
>>> "a" in trace_inner
True
>>> "a" in trace_outer
False
>>> "b" in trace_inner
True
>>> "b" in trace_outer
True
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• hide_fn – function that takes a site and returns True to hide the site or False/None to expose it. If specified, all other parameters are ignored. Only specify one of hide_fn or expose_fn, not both.

• expose_fn – function that takes a site and returns True to expose the site or False/None to hide it. If specified, all other parameters are ignored. Only specify one of hide_fn or expose_fn, not both.

• hide_all (bool) – hide all sites

• expose_all (bool) – expose all sites normally

• hide (list) – list of site names to hide

• expose (list) – list of site names to be exposed while all others hidden

• hide_types (list) – list of site types to be hidden

• expose_types (list) – list of site types to be exposed while all others hidden

Returns

stochastic function decorated with a `BlockMessenger`

Convenient wrapper of `BroadcastMessenger`

Automatically broadcasts the batch shape of the stochastic function at a sample site when inside a single or nested plate context. The existing batch_shape must be broadcastable with the size of the `plate` contexts installed in the cond_indep_stack.

Notice how model_automatic_broadcast below automates expanding of distribution batch shapes. This makes it easy to modularize a Pyro model as the sub-components are agnostic of the wrapping `plate` contexts.

```>>> def model_broadcast_by_hand():
...     with IndepMessenger("batch", 100, dim=-2):
...         with IndepMessenger("components", 3, dim=-1):
...             sample = pyro.sample("sample", dist.Bernoulli(torch.ones(3) * 0.5)
...                                                .expand_by(100))
...             assert sample.shape == torch.Size((100, 3))
...     return sample
```
```>>> @poutine.broadcast
...     with IndepMessenger("batch", 100, dim=-2):
...         with IndepMessenger("components", 3, dim=-1):
...             sample = pyro.sample("sample", dist.Bernoulli(torch.tensor(0.5)))
...             assert sample.shape == torch.Size((100, 3))
...     return sample
```
collapse(fn: None = None, *args: Any, **kwargs: Any) [source]
collapse(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], *args: Any, **kwargs: Any) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `CollapseMessenger`

EXPERIMENTAL Collapses all sites in the context by lazily sampling and attempting to use conjugacy relations. If no conjugacy is known this will fail. Code using the results of sample sites must be written to accept Funsors rather than Tensors. This requires `funsor` to be installed.

Warning

This is not compatible with automatic guessing of `max_plate_nesting`. If any plates appear within the collapsed context, you should manually declare `max_plate_nesting` to your inference algorithm (e.g. `Trace_ELBO(max_plate_nesting=1)`).

condition(data: Union[Dict[str, torch.Tensor], Trace]) [source]
condition(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], data: Union[Dict[str, torch.Tensor], Trace]) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `ConditionMessenger`

Given a stochastic function with some sample statements and a dictionary of observations at names, change the sample statements at those names into observes with those values.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

To observe a value for site z, we can write

```>>> conditioned_model = pyro.poutine.condition(model, data={"z": torch.tensor(1.)})
```

This is equivalent to adding obs=value as a keyword argument to pyro.sample(“z”, …) in model.

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• data – a dict or a `Trace`

Returns

stochastic function decorated with a `ConditionMessenger`

do(data: Dict[str, Union[torch.Tensor, numbers.Number]]) [source]
do(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], data: Dict[str, Union[torch.Tensor, numbers.Number]]) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `DoMessenger`

Given a stochastic function with some sample statements and a dictionary of values at names, set the return values of those sites equal to the values as if they were hard-coded to those values and introduce fresh sample sites with the same names whose values do not propagate.

Composes freely with `condition()` to represent counterfactual distributions over potential outcomes. See Single World Intervention Graphs [1] for additional details and theory.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

To intervene with a value for site z, we can write

```>>> intervened_model = pyro.poutine.do(model, data={"z": torch.tensor(1.)})
```

This is equivalent to replacing z = pyro.sample(“z”, …) with z = torch.tensor(1.) and introducing a fresh sample site pyro.sample(“z”, …) whose value is not used elsewhere.

References

[1] Single World Intervention Graphs: A Primer,

Thomas Richardson, James Robins

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• data – a `dict` mapping sample site names to interventions

Returns

stochastic function decorated with a `DoMessenger`

enum(fn: None = None, first_available_dim: Optional[int] = None) [source]
enum(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], first_available_dim: Optional[int] = None) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `EnumMessenger`

Enumerates in parallel over discrete sample sites marked `infer={"enumerate": "parallel"}`.

Parameters

first_available_dim (int) – The first tensor dimension (counting from the right) that is available for parallel enumeration. This dimension and all dimensions left may be used internally by Pyro. This should be a negative integer or None.

escape(escape_fn: Callable[[Message], bool]) [source]
escape(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], escape_fn: Callable[[Message], bool]) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `EscapeMessenger`

Messenger that does a nonlocal exit by raising a util.NonlocalExit exception

infer_config(config_fn: Callable[[Message], InferDict]) [source]
infer_config(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], config_fn: Callable[[Message], InferDict]) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `InferConfigMessenger`

Given a callable fn that contains Pyro primitive calls and a callable config_fn taking a trace site and returning a dictionary, updates the value of the infer kwarg at a sample site to config_fn(site).

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• config_fn – a callable taking a site and returning an infer dict

Returns

stochastic function decorated with `InferConfigMessenger`

lift(prior: Union[Callable, Distribution, Dict[str, Union[Distribution, Callable]]]) [source]
lift(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], prior: Union[Callable, Distribution, Dict[str, Union[Distribution, Callable]]]) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `LiftMessenger`

Given a stochastic function with param calls and a prior distribution, create a stochastic function where all param calls are replaced by sampling from prior. Prior should be a callable or a dict of names to callables.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
>>> lifted_model = pyro.poutine.lift(model, prior={"s": dist.Exponential(0.3)})
```

`lift` makes `param` statements behave like `sample` statements using the distributions in `prior`. In this example, site s will now behave as if it was replaced with `s = pyro.sample("s", dist.Exponential(0.3))`:

```>>> tr = pyro.poutine.trace(lifted_model).get_trace(0.0)
>>> tr.nodes["s"]["type"] == "sample"
True
>>> tr2 = pyro.poutine.trace(lifted_model).get_trace(0.0)
>>> bool((tr2.nodes["s"]["value"] == tr.nodes["s"]["value"]).all())
False
```
Parameters
• fn – function whose parameters will be lifted to random values

• prior – prior function in the form of a Distribution or a dict of stochastic fns

Returns

`fn` decorated with a `LiftMessenger`

markov(fn: None = None, history: int = 1, keep: bool = False, dim: Optional[int] = None, name: Optional[str] = None) [source]
markov(fn: Iterable[int] = None, history: int = 1, keep: bool = False, dim: Optional[int] = None, name: Optional[str] = None)
markov(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T] = None, history: int = 1, keep: bool = False, dim: Optional[int] = None, name: Optional[str] = None) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Markov dependency declaration.

This can be used in a variety of ways:

• as a context manager

• as a decorator for recursive functions

• as an iterator for markov chains

Parameters
• history (int) – The number of previous contexts visible from the current context. Defaults to 1. If zero, this is similar to `pyro.plate`.

• keep (bool) – If true, frames are replayable. This is important when branching: if `keep=True`, neighboring branches at the same level can depend on each other; if `keep=False`, neighboring branches are independent (conditioned on their share”

• dim (int) – An optional dimension to use for this independence index. Interface stub, behavior not yet implemented.

• name (str) – An optional unique name to help inference algorithms match `pyro.markov()` sites between models and guides. Interface stub, behavior not yet implemented.

Convenient wrapper of `MaskMessenger`

Given a stochastic function with some batched sample statements and masking tensor, mask out some of the sample statements elementwise.

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• mask (torch.BoolTensor) – a `{0,1}`-valued masking tensor (1 includes a site, 0 excludes a site)

Returns

stochastic function decorated with a `MaskMessenger`

queue(fn=None, queue=None, max_tries=None, extend_fn=None, escape_fn=None, num_samples=None)[source]

Used in sequential enumeration over discrete variables.

Given a stochastic function and a queue, return a return value from a complete trace in the queue.

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• queue – a queue data structure like multiprocessing.Queue to hold partial traces

• max_tries – maximum number of attempts to compute a single complete trace

• extend_fn – function (possibly stochastic) that takes a partial trace and a site, and returns a list of extended traces

• escape_fn – function (possibly stochastic) that takes a partial trace and a site, and returns a boolean value to decide whether to exit

• num_samples – optional number of extended traces for extend_fn to return

Returns

stochastic function decorated with poutine logic

reparam(config: Union[Dict[str, Reparam], Callable[[Message], Optional[Reparam]]]) [source]
reparam(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], config: Union[Dict[str, Reparam], Callable[[Message], Optional[Reparam]]]) pyro.poutine.reparam_messenger.ReparamHandler[pyro.poutine.handlers._P, pyro.poutine.handlers._T]

Convenient wrapper of `ReparamMessenger`

Reparametrizes each affected sample site into one or more auxiliary sample sites followed by a deterministic transformation [1].

To specify reparameterizers, pass a `config` dict or callable to the constructor. See the `pyro.infer.reparam` module for available reparameterizers.

Note some reparameterizers can examine the `*args,**kwargs` inputs of functions they affect; these reparameterizers require using `poutine.reparam` as a decorator rather than as a context manager.

[1] Maria I. Gorinova, Dave Moore, Matthew D. Hoffman (2019)

“Automatic Reparameterisation of Probabilistic Programs” https://arxiv.org/pdf/1906.03028.pdf

Parameters

config (dict or callable) – Configuration, either a dict mapping site name to `Reparameterizer` , or a function mapping site to `Reparam` or None. See `pyro.infer.reparam.strategies` for built-in configuration strategies.

replay(fn: None = None, trace: Optional[Trace] = None, params: Optional[Dict[str, torch.Tensor]] = None) [source]
replay(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], trace: Optional[Trace] = None, params: Optional[Dict[str, torch.Tensor]] = None) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `ReplayMessenger`

Given a callable that contains Pyro primitive calls, return a callable that runs the original, reusing the values at sites in trace at those sites in the new trace

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

`replay` makes `sample` statements behave as if they had sampled the values at the corresponding sites in the trace:

```>>> old_trace = pyro.poutine.trace(model).get_trace(1.0)
>>> replayed_model = pyro.poutine.replay(model, trace=old_trace)
>>> bool(replayed_model(0.0) == old_trace.nodes["_RETURN"]["value"])
True
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• trace – a `Trace` data structure to replay against

• params – dict of names of param sites and constrained values in fn to replay against

Returns

a stochastic function decorated with a `ReplayMessenger`

scale(scale: Union[float, torch.Tensor]) [source]
scale(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], scale: Union[float, torch.Tensor]) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `ScaleMessenger`

Given a stochastic function with some sample statements and a positive scale factor, scale the score of all sample and observe sites in the function.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     pyro.sample("z", dist.Normal(x, s), obs=torch.tensor(1.0))
```

`scale` multiplicatively scales the log-probabilities of sample sites:

```>>> scaled_model = pyro.poutine.scale(model, scale=0.5)
>>> scaled_tr = pyro.poutine.trace(scaled_model).get_trace(0.0)
>>> unscaled_tr = pyro.poutine.trace(model).get_trace(0.0)
>>> bool((scaled_tr.log_prob_sum() == 0.5 * unscaled_tr.log_prob_sum()).all())
True
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• scale – a positive scaling factor

Returns

stochastic function decorated with a `ScaleMessenger`

seed(rng_seed: int) [source]
seed(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], rng_seed: int) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `SeedMessenger`

Handler to set the random number generator to a pre-defined state by setting its seed. This is the same as calling `pyro.set_rng_seed()` before the call to fn. This handler has no additional effect on primitive statements on the standard Pyro backend, but it might intercept `pyro.sample` calls in other backends. e.g. the NumPy backend.

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls).

• rng_seed (int) – rng seed.

substitute(data: Dict[str, torch.Tensor]) [source]
substitute(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], data: Dict[str, torch.Tensor]) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `SubstituteMessenger`

Given a stochastic function with param calls and a set of parameter values, create a stochastic function where all param calls are substituted with the fixed values. data should be a dict of names to values. Consider the following Pyro program:

```>>> def model(x):
...     a = pyro.param("a", torch.tensor(0.5))
...     x = pyro.sample("x", dist.Bernoulli(probs=a))
...     return x
>>> substituted_model = pyro.poutine.substitute(model, data={"a": torch.tensor(0.3)})
```

In this example, site a will now have value torch.tensor(0.3). :param data: dictionary of values keyed by site names. :returns: `fn` decorated with a `SubstituteMessenger`

trace(fn: None = None, graph_type: Optional[Literal['flat', 'dense']] = None, param_only: Optional[bool] = None) [source]
trace(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T], graph_type: Optional[Literal['flat', 'dense']] = None, param_only: Optional[bool] = None) pyro.poutine.trace_messenger.TraceHandler[pyro.poutine.handlers._P, pyro.poutine.handlers._T]

Convenient wrapper of `TraceMessenger`

Return a handler that records the inputs and outputs of primitive calls and their dependencies.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

We can record its execution using `trace` and use the resulting data structure to compute the log-joint probability of all of the sample sites in the execution or extract all parameters.

```>>> trace = pyro.poutine.trace(model).get_trace(0.0)
>>> logp = trace.log_prob_sum()
>>> params = [trace.nodes[name]["value"].unconstrained() for name in trace.param_nodes]
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• graph_type – string that specifies the kind of graph to construct

• param_only – if true, only records params and not samples

Returns

stochastic function decorated with a `TraceMessenger`

uncondition(fn: None = None) [source]
uncondition(fn: Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T] = None) Callable[[pyro.poutine.handlers._P], pyro.poutine.handlers._T]

Convenient wrapper of `UnconditionMessenger`

Messenger to force the value of observed nodes to be sampled from their distribution, ignoring observations.

config_enumerate(guide=None, default='parallel', expand=False, num_samples=None, tmc='diagonal')[source]

Configures enumeration for all relevant sites in a guide. This is mainly used in conjunction with `TraceEnum_ELBO`.

When configuring for exhaustive enumeration of discrete variables, this configures all sample sites whose distribution satisfies `.has_enumerate_support == True`. When configuring for local parallel Monte Carlo sampling via `default="parallel", num_samples=n`, this configures all sample sites. This does not overwrite existing annotations `infer={"enumerate": ...}`.

This can be used as either a function:

```guide = config_enumerate(guide)
```

or as a decorator:

```@config_enumerate
def guide1(*args, **kwargs):
...

@config_enumerate(default="sequential", expand=True)
def guide2(*args, **kwargs):
...
```
Parameters
• guide (callable) – a pyro model that will be used as a guide in `SVI`.

• default (str) – Which enumerate strategy to use, one of “sequential”, “parallel”, or None. Defaults to “parallel”.

• expand (bool) – Whether to expand enumerated sample values. See `enumerate_support()` for details. This only applies to exhaustive enumeration, where `num_samples=None`. If `num_samples` is not `None`, then this samples will always be expanded.

• num_samples (int or None) – if not `None`, use local Monte Carlo sampling rather than exhaustive enumeration. This makes sense for both continuous and discrete distributions.

• tmc (string or None) – “mixture” or “diagonal” strategies to use in Tensor Monte Carlo

Returns

an annotated guide

Return type

callable

## Trace¶

class Trace(graph_type: Literal['flat', 'dense'] = 'flat')[source]

Bases: `object`

Graph data structure denoting the relationships amongst different pyro primitives in the execution trace.

An execution trace of a Pyro program is a record of every call to `pyro.sample()` and `pyro.param()` in a single execution of that program. Traces are directed graphs whose nodes represent primitive calls or input/output, and whose edges represent conditional dependence relationships between those primitive calls. They are created and populated by `poutine.trace`.

Each node (or site) in a trace contains the name, input and output value of the site, as well as additional metadata added by inference algorithms or user annotation. In the case of `pyro.sample`, the trace also includes the stochastic function at the site, and any observed data added by users.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

We can record its execution using `pyro.poutine.trace` and use the resulting data structure to compute the log-joint probability of all of the sample sites in the execution or extract all parameters.

```>>> trace = pyro.poutine.trace(model).get_trace(0.0)
>>> logp = trace.log_prob_sum()
>>> params = [trace.nodes[name]["value"].unconstrained() for name in trace.param_nodes]
```

We can also inspect or manipulate individual nodes in the trace. `trace.nodes` contains a `collections.OrderedDict` of site names and metadata corresponding to `x`, `s`, `z`, and the return value:

```>>> list(name for name in trace.nodes.keys())
["_INPUT", "s", "z", "_RETURN"]
```

Values of `trace.nodes` are dictionaries of node metadata:

```>>> trace.nodes["z"]
{'type': 'sample', 'name': 'z', 'is_observed': False,
'fn': Normal(), 'value': tensor(0.6480), 'args': (), 'kwargs': {},
'infer': {}, 'scale': 1.0, 'cond_indep_stack': (),
'done': True, 'stop': False, 'continuation': None}
```

`'infer'` is a dictionary of user- or algorithm-specified metadata. `'args'` and `'kwargs'` are the arguments passed via `pyro.sample` to `fn.__call__` or `fn.log_prob`. `'scale'` is used to scale the log-probability of the site when computing the log-joint. `'cond_indep_stack'` contains data structures corresponding to `pyro.plate` contexts appearing in the execution. `'done'`, `'stop'`, and `'continuation'` are only used by Pyro’s internals.

Parameters

graph_type (string) – string specifying the kind of trace graph to construct

Parameters

site_name (string) – the name of the site to be added

Adds a site to the trace.

Raises an error when attempting to add a duplicate node instead of silently overwriting.

compute_log_prob(site_filter: Callable[[str, Message], bool] = <function allow_all_sites>) None[source]

Compute the site-wise log probabilities of the trace. Each `log_prob` has shape equal to the corresponding `batch_shape`. Each `log_prob_sum` is a scalar. Both computations are memoized.

compute_score_parts() None[source]

Compute the batched local score parts at each site of the trace. Each `log_prob` has shape equal to the corresponding `batch_shape`. Each `log_prob_sum` is a scalar. All computations are memoized.

copy() [source]

Makes a shallow copy of self with nodes and edges preserved.

detach_() None[source]

Detach values (in-place) at each sample site of the trace.

property edges: Iterable[Tuple[str, str]]
format_shapes(title: str = 'Trace Shapes:', last_site: Optional[str] = None) str[source]

Returns a string showing a table of the shapes of all sites in the trace.

iter_stochastic_nodes() Iterator[Tuple[str, Message]][source]
Returns

an iterator over stochastic nodes in the trace.

log_prob_sum(site_filter: Callable[[str, Message], bool] = <function allow_all_sites>) Union[torch.Tensor, float][source]

Compute the site-wise log probabilities of the trace. Each `log_prob` has shape equal to the corresponding `batch_shape`. Each `log_prob_sum` is a scalar. The computation of `log_prob_sum` is memoized.

Returns

total log probability.

Return type

torch.Tensor

property nonreparam_stochastic_nodes: List[str]

a list of names of sample sites whose stochastic functions are not reparameterizable primitive distributions

Type

return

property observation_nodes: List[str]

a list of names of observe sites

Type

return

pack_tensors(plate_to_symbol: Optional[Dict[str, str]] = None) None[source]

Computes packed representations of tensors in the trace. This should be called after `compute_log_prob()` or `compute_score_parts()`.

property param_nodes: List[str]

a list of names of param sites

Type

return

predecessors(site_name: str) Set[str][source]
remove_node(site_name: str) None[source]
property reparameterized_nodes: List[str]

a list of names of sample sites whose stochastic functions are reparameterizable primitive distributions

Type

return

property stochastic_nodes: List[str]

a list of names of sample sites

Type

return

successors(site_name: str) Set[str][source]
symbolize_dims(plate_to_symbol: Optional[Dict[str, str]] = None) None[source]

Assign unique symbols to all tensor dimensions.

topological_sort(reverse: bool = False) List[str][source]

Return a list of nodes (site names) in topologically sorted order.

Parameters

reverse (bool) – Return the list in reverse order.

Returns

list of topologically sorted nodes (site names).

## Runtime¶

class InferDict[source]

Bases: `typing_extensions.TypedDict`

A dictionary that contains information about inference.

This can be used to configure per-site inference strategies, e.g.:

```pyro.sample(
"x",
dist.Bernoulli(0.5),
infer={"enumerate": "parallel"},
)
```
Keys:
enumerate (str):

If one of the strings “sequential” or “parallel”, enables enumeration. Parallel enumeration is generally faster but requires broadcasting-safe operations and static structure.

expand (bool):

Whether to expand the distribution during enumeration. Defaults to False if missing.

is_auxiliary (bool):

Whether the sample site is auxiliary, e.g. for use in guides that deterministically transform auxiliary variables. Defaults to False if missing.

is_observed (bool):

Whether the sample site is observed (i.e. not latent). Defaults to False if missing.

num_samples (int):

The number of samples to draw. Defaults to 1 if missing.

obs (optional torch.Tensor):

The observed value, or None for latent variables. Defaults to None if missing.

prior (optional torch.distributions.Distribution):

(internal) For use in GuideMessenger to store the model’s prior distribution (conditioned on upstream sites).

tmc (str):

Whether to use the diagonal or mixture approximation for Tensor Monte Carlo in TraceTMC_ELBO.

was_observed (bool):

(internal) Whether the sample site was originally observed, in the context of inference via Reweighted Wake Sleep or Compiled Sequential Importance Sampling.

enumerate: typing_extensions.Literal[sequential, parallel]
expand: bool
is_auxiliary: bool
is_observed: bool
num_samples: int
obs: Optional[torch.Tensor]
prior: TorchDistributionMixin
tmc: typing_extensions.Literal[diagonal, mixture]
was_observed: bool
class Message[source]

Bases: `typing_extensions.TypedDict`, `Generic`[`pyro.poutine.runtime._P`, `pyro.poutine.runtime._T`]

Pyro’s internal message type for effect handling.

Messages are stored in trace objects, e.g.:

```trace.nodes["my_site_name"]  # This is a Message.
```
Keys:
type (str):

The message type, typically one of the strings “sample”, “param”, “plate”, or “markov”, but possibly custom.

name (str):

The site name, typically naming a sample or parameter.

fn (callable):

The distribution or function used to generate the sample.

is_observed (bool):

A flag to indicate whether the value is observed.

args (tuple):

Positional arguments to the distribution or function.

kwargs (dict):

Keyword arguments to the distribution or function.

value (torch.Tensor):

The value of the sample (either observed or sampled).

scale (torch.Tensor):

A scaling factor for the log probability.

A bool or tensor to mask the log probability.

cond_indep_stack (tuple):

The site’s local stack of conditional independence metadata. Immutable.

done (bool):

A flag to indicate whether the message has been handled.

stop (bool):

A flag to stop further processing of the message.

continuation (callable):

A function to call after processing the message.

infer (optional InferDict):

A dictionary of inference parameters.

obs (torch.Tensor):

The observed value.

log_prob (torch.Tensor):

The log probability of the sample.

log_prob_sum (torch.Tensor):

The sum of the log probability.

unscaled_log_prob (torch.Tensor):

The unscaled log probability.

score_parts (pyro.distributions.ScoreParts):

A collection of score parts.

packed (Message):

A packed message, used during enumeration.

args: Tuple
cond_indep_stack: Tuple[CondIndepStackFrame, ...]
continuation: Optional[Callable[[Message], None]]
done: bool
fn: Callable[[pyro.poutine.runtime._P], pyro.poutine.runtime._T]
infer: Optional[pyro.poutine.runtime.InferDict]
is_observed: bool
kwargs: Dict
log_prob: torch.Tensor
log_prob_sum: torch.Tensor
name: Optional[str]
obs: Optional[torch.Tensor]
packed: Message
scale: Union[torch.Tensor, float]
score_parts: ScoreParts
stop: bool
type: str
unscaled_log_prob: torch.Tensor
value: Optional[pyro.poutine.runtime._T]
exception NonlocalExit(site: pyro.poutine.runtime.Message, *args, **kwargs)[source]

Bases: `Exception`

Exception for exiting nonlocally from poutine execution.

Used by poutine.EscapeMessenger to return site information.

reset_stack() None[source]

Reset the state of the frames remaining in the stack. Necessary for multiple re-executions in poutine.queue.

am_i_wrapped() bool[source]

Checks whether the current computation is wrapped in a poutine. :returns: bool

apply_stack(initial_msg: pyro.poutine.runtime.Message) None[source]

Execute the effect stack at a single site according to the following scheme:

1. For each `Messenger` in the stack from bottom to top, execute `Messenger._process_message` with the message; if the message field “stop” is True, stop; otherwise, continue

2. Apply default behavior (`default_process_message`) to finish remaining site execution

3. For each `Messenger` in the stack from top to bottom, execute `_postprocess_message` to update the message and internal messenger state with the site results

4. If the message field “continuation” is not `None`, call it with the message

Parameters

initial_msg (dict) – the starting version of the trace site

Returns

`None`

default_process_message(msg: pyro.poutine.runtime.Message) None[source]

Default method for processing messages in inference.

Parameters

msg – a message to be processed

Returns

None

effectful(fn: None = None, type: Optional[str] = None) Callable[[Callable[[pyro.poutine.runtime._P], pyro.poutine.runtime._T]], Callable[[...], pyro.poutine.runtime._T]][source]
effectful(fn: Callable[[pyro.poutine.runtime._P], pyro.poutine.runtime._T] = None, type: Optional[str] = None) Callable[[...], pyro.poutine.runtime._T]
Parameters
• fn – function or callable that performs an effectful computation

• type (str) – the type label of the operation, e.g. “sample”

Wrapper for calling `apply_stack()` to apply any active effects.

Records the effects of enclosing `poutine.mask` handlers.

This is useful for avoiding expensive `pyro.factor()` computations during prediction, when the log density need not be computed, e.g.:

```def model():
# ...
log_density = my_expensive_computation()
pyro.factor("foo", log_density)
# ...
```
Returns

Return type

None, bool, or torch.Tensor

get_plates() Tuple[CondIndepStackFrame, ...][source]

Records the effects of enclosing `pyro.plate` contexts.

Returns

A tuple of `pyro.poutine.indep_messenger.CondIndepStackFrame` objects.

Return type

tuple

## Utilities¶

all_escape(trace: Trace, msg: Message) bool[source]
Parameters
• trace – a partial trace

• msg – the message at a Pyro primitive site

Returns

boolean decision value

Utility function that checks if a site is not already in a trace.

Used by EscapeMessenger to decide whether to do a nonlocal exit at a site. Subroutine for approximately integrating out variables for variance reduction.

discrete_escape(trace: Trace, msg: Message) bool[source]
Parameters
• trace – a partial trace

• msg – the message at a Pyro primitive site

Returns

boolean decision value

Utility function that checks if a sample site is discrete and not already in a trace.

Used by EscapeMessenger to decide whether to do a nonlocal exit at a site. Subroutine for integrating out discrete variables for variance reduction.

enable_validation(is_validate: bool) None[source]
enum_extend(trace: Trace, msg: Message, num_samples: Optional[int] = None) List[Trace][source]
Parameters
• trace – a partial trace

• msg – the message at a Pyro primitive site

• num_samples – maximum number of extended traces to return.

Returns

a list of traces, copies of input trace with one extra site

Utility function to copy and extend a trace with sites based on the input site whose values are enumerated from the support of the input site’s distribution.

Used for exact inference and integrating out discrete variables.

is_validation_enabled() bool[source]
mc_extend(trace: Trace, msg: Message, num_samples: Optional[int] = None) List[Trace][source]
Parameters
• trace – a partial trace

• msg – the message at a Pyro primitive site

• num_samples – maximum number of extended traces to return.

Returns

a list of traces, copies of input trace with one extra site

Utility function to copy and extend a trace with sites based on the input site whose values are sampled from the input site’s function.

Used for Monte Carlo marginalization of individual sample sites.

prune_subsample_sites(trace: Trace) Trace[source]

Copies and removes all subsample sites from a trace.

site_is_factor(site: Message) bool[source]

Determines whether a trace site originated from a factor statement.

site_is_subsample(site: Message) bool[source]

Determines whether a trace site originated from a subsample statement inside an plate.

## Messengers¶

Messenger objects contain the implementations of the effects exposed by handlers. Advanced users may modify the implementations of messengers behind existing handlers or write new messengers that implement new effects and compose correctly with the rest of the library.

### Messenger¶

class Messenger[source]

Bases: `object`

Context manager class that modifies behavior and adds side effects to stochastic functions i.e. callables containing Pyro primitive statements.

This is the base Messenger class. It implements the default behavior for all Pyro primitives, so that the joint distribution induced by a stochastic function fn is identical to the joint distribution induced by `Messenger()(fn)`.

Class of transformers for messages passed during inference. Most inference operations are implemented in subclasses of this.

classmethod register(fn: Optional[Callable] = None, type: Optional[str] = None, post: Optional[bool] = None) Callable[source]
Parameters
• fn – function implementing operation

• type (str) – name of the operation (also passed to `effectful()`)

• post (bool) – if True, use this operation as postprocess

Dynamically add operations to an effect. Useful for generating wrappers for libraries.

Example:

```@SomeMessengerClass.register
def some_function(msg)
...do_something...
return msg
```
classmethod unregister(fn: Optional[Callable] = None, type: Optional[str] = None) Optional[Callable][source]
Parameters

Dynamically remove operations from an effect. Useful for removing wrappers from libraries.

Example:

```SomeMessengerClass.unregister(some_function, "name")
```
block_messengers(predicate: Callable[[pyro.poutine.messenger.Messenger], bool]) Iterator[List[pyro.poutine.messenger.Messenger]][source]

EXPERIMENTAL Context manager to temporarily remove matching messengers from the _PYRO_STACK. Note this does not call the `.__exit__()` and `.__enter__()` methods.

This is useful to selectively block enclosing handlers.

Parameters

predicate (callable) – A predicate mapping messenger instance to boolean. This mutes all messengers `m` for which `bool(predicate(m)) is True`.

Yields

A list of matched messengers that are blocked.

unwrap(fn: Callable) Callable[source]

Recursively unwraps poutines.

### BlockMessenger¶

class BlockMessenger(hide_fn: Optional[Callable[[Message], Optional[bool]]] = None, expose_fn: Optional[Callable[[Message], Optional[bool]]] = None, hide_all: bool = True, expose_all: bool = False, hide: Optional[List[str]] = None, expose: Optional[List[str]] = None, hide_types: Optional[List[str]] = None, expose_types: Optional[List[str]] = None)[source]

This handler selectively hides Pyro primitive sites from the outside world. Default behavior: block everything.

A site is hidden if at least one of the following holds:

1. `hide_fn(msg) is True` or `(not expose_fn(msg)) is True`

2. `msg["name"] in hide`

3. `msg["type"] in hide_types`

4. `msg["name"] not in expose and msg["type"] not in expose_types`

5. `hide`, `hide_types`, and `expose_types` are all `None`

For example, suppose the stochastic function fn has two sample sites “a” and “b”. Then any effect outside of `BlockMessenger(fn, hide=["a"])` will not be applied to site “a” and will only see site “b”:

```>>> def fn():
...     a = pyro.sample("a", dist.Normal(0., 1.))
...     return pyro.sample("b", dist.Normal(a, 1.))
>>> fn_inner = pyro.poutine.trace(fn)
>>> fn_outer = pyro.poutine.trace(pyro.poutine.block(fn_inner, hide=["a"]))
>>> trace_inner = fn_inner.get_trace()
>>> trace_outer  = fn_outer.get_trace()
>>> "a" in trace_inner
True
>>> "a" in trace_outer
False
>>> "b" in trace_inner
True
>>> "b" in trace_outer
True
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• hide_fn – function that takes a site and returns True to hide the site or False/None to expose it. If specified, all other parameters are ignored. Only specify one of hide_fn or expose_fn, not both.

• expose_fn – function that takes a site and returns True to expose the site or False/None to hide it. If specified, all other parameters are ignored. Only specify one of hide_fn or expose_fn, not both.

• hide_all (bool) – hide all sites

• expose_all (bool) – expose all sites normally

• hide (list) – list of site names to hide

• expose (list) – list of site names to be exposed while all others hidden

• hide_types (list) – list of site types to be hidden

• expose_types (list) – list of site types to be exposed while all others hidden

Returns

stochastic function decorated with a `BlockMessenger`

Automatically broadcasts the batch shape of the stochastic function at a sample site when inside a single or nested plate context. The existing batch_shape must be broadcastable with the size of the `plate` contexts installed in the cond_indep_stack.

Notice how model_automatic_broadcast below automates expanding of distribution batch shapes. This makes it easy to modularize a Pyro model as the sub-components are agnostic of the wrapping `plate` contexts.

```>>> def model_broadcast_by_hand():
...     with IndepMessenger("batch", 100, dim=-2):
...         with IndepMessenger("components", 3, dim=-1):
...             sample = pyro.sample("sample", dist.Bernoulli(torch.ones(3) * 0.5)
...                                                .expand_by(100))
...             assert sample.shape == torch.Size((100, 3))
...     return sample
```
```>>> @poutine.broadcast
...     with IndepMessenger("batch", 100, dim=-2):
...         with IndepMessenger("components", 3, dim=-1):
...             sample = pyro.sample("sample", dist.Bernoulli(torch.tensor(0.5)))
...             assert sample.shape == torch.Size((100, 3))
...     return sample
```

### CollapseMessenger¶

class CollapseMessenger(*args: Any, **kwargs: Any)[source]

EXPERIMENTAL Collapses all sites in the context by lazily sampling and attempting to use conjugacy relations. If no conjugacy is known this will fail. Code using the results of sample sites must be written to accept Funsors rather than Tensors. This requires `funsor` to be installed.

Warning

This is not compatible with automatic guessing of `max_plate_nesting`. If any plates appear within the collapsed context, you should manually declare `max_plate_nesting` to your inference algorithm (e.g. `Trace_ELBO(max_plate_nesting=1)`).

### ConditionMessenger¶

class ConditionMessenger(data: Union[Dict[str, torch.Tensor], pyro.poutine.trace_struct.Trace])[source]

Given a stochastic function with some sample statements and a dictionary of observations at names, change the sample statements at those names into observes with those values.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

To observe a value for site z, we can write

```>>> conditioned_model = pyro.poutine.condition(model, data={"z": torch.tensor(1.)})
```

This is equivalent to adding obs=value as a keyword argument to pyro.sample(“z”, …) in model.

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• data – a dict or a `Trace`

Returns

stochastic function decorated with a `ConditionMessenger`

### DoMessenger¶

class DoMessenger(data: Dict[str, Union[torch.Tensor, numbers.Number]])[source]

Given a stochastic function with some sample statements and a dictionary of values at names, set the return values of those sites equal to the values as if they were hard-coded to those values and introduce fresh sample sites with the same names whose values do not propagate.

Composes freely with `condition()` to represent counterfactual distributions over potential outcomes. See Single World Intervention Graphs [1] for additional details and theory.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

To intervene with a value for site z, we can write

```>>> intervened_model = pyro.poutine.do(model, data={"z": torch.tensor(1.)})
```

This is equivalent to replacing z = pyro.sample(“z”, …) with z = torch.tensor(1.) and introducing a fresh sample site pyro.sample(“z”, …) whose value is not used elsewhere.

References

[1] Single World Intervention Graphs: A Primer,

Thomas Richardson, James Robins

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• data – a `dict` mapping sample site names to interventions

Returns

stochastic function decorated with a `DoMessenger`

### EnumMessenger¶

class EnumMessenger(first_available_dim: Optional[int] = None)[source]

Enumerates in parallel over discrete sample sites marked `infer={"enumerate": "parallel"}`.

Parameters

first_available_dim (int) – The first tensor dimension (counting from the right) that is available for parallel enumeration. This dimension and all dimensions left may be used internally by Pyro. This should be a negative integer or None.

enumerate_site(msg: pyro.poutine.runtime.Message) [source]

### EscapeMessenger¶

class EscapeMessenger(escape_fn: Callable[[pyro.poutine.runtime.Message], bool])[source]

Messenger that does a nonlocal exit by raising a util.NonlocalExit exception

### IndepMessenger¶

class CondIndepStackFrame(name, dim, size, counter, full_size)[source]

Bases: `tuple`

counter: int

Alias for field number 3

dim: Optional[int]

Alias for field number 1

full_size: Optional[int]

Alias for field number 4

name: str

Alias for field number 0

size: int

Alias for field number 2

property vectorized: bool
class IndepMessenger(name: str, size: int, dim: Optional[int] = None, device: Optional[str] = None)[source]

This messenger keeps track of stack of independence information declared by nested `plate` contexts. This information is stored in a `cond_indep_stack` at each sample/observe site for consumption by `TraceMessenger`.

Example:

```x_axis = IndepMessenger('outer', 320, dim=-1)
y_axis = IndepMessenger('inner', 200, dim=-2)
with x_axis:
x_noise = sample("x_noise", dist.Normal(loc, scale).expand_by([320]))
with y_axis:
y_noise = sample("y_noise", dist.Normal(loc, scale).expand_by([200, 1]))
with x_axis, y_axis:
xy_noise = sample("xy_noise", dist.Normal(loc, scale).expand_by([200, 320]))
```
property indices: torch.Tensor
next_context() None[source]

Increments the counter.

### InferConfigMessenger¶

class InferConfigMessenger(config_fn: Callable[[Message], InferDict])[source]

Given a callable fn that contains Pyro primitive calls and a callable config_fn taking a trace site and returning a dictionary, updates the value of the infer kwarg at a sample site to config_fn(site).

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• config_fn – a callable taking a site and returning an infer dict

Returns

stochastic function decorated with `InferConfigMessenger`

### LiftMessenger¶

class LiftMessenger(prior: Union[Callable, pyro.distributions.distribution.Distribution, Dict[str, Union[pyro.distributions.distribution.Distribution, Callable]]])[source]

Given a stochastic function with param calls and a prior distribution, create a stochastic function where all param calls are replaced by sampling from prior. Prior should be a callable or a dict of names to callables.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
>>> lifted_model = pyro.poutine.lift(model, prior={"s": dist.Exponential(0.3)})
```

`lift` makes `param` statements behave like `sample` statements using the distributions in `prior`. In this example, site s will now behave as if it was replaced with `s = pyro.sample("s", dist.Exponential(0.3))`:

```>>> tr = pyro.poutine.trace(lifted_model).get_trace(0.0)
>>> tr.nodes["s"]["type"] == "sample"
True
>>> tr2 = pyro.poutine.trace(lifted_model).get_trace(0.0)
>>> bool((tr2.nodes["s"]["value"] == tr.nodes["s"]["value"]).all())
False
```
Parameters
• fn – function whose parameters will be lifted to random values

• prior – prior function in the form of a Distribution or a dict of stochastic fns

Returns

`fn` decorated with a `LiftMessenger`

### MarkovMessenger¶

class MarkovMessenger(history: int = 1, keep: bool = False, dim: Optional[int] = None, name: Optional[str] = None)[source]

Markov dependency declaration.

This is a statistical equivalent of a memory management arena.

Parameters
• history (int) – The number of previous contexts visible from the current context. Defaults to 1. If zero, this is similar to `pyro.plate`.

• keep (bool) – If true, frames are replayable. This is important when branching: if `keep=True`, neighboring branches at the same level can depend on each other; if `keep=False`, neighboring branches are independent (conditioned on their shared ancestors).

• dim (int) – An optional dimension to use for this independence index. Interface stub, behavior not yet implemented.

• name (str) – An optional unique name to help inference algorithms match `pyro.markov()` sites between models and guides. Interface stub, behavior not yet implemented.

generator(iterable: Iterable[int]) typing_extensions.Self[source]

Given a stochastic function with some batched sample statements and masking tensor, mask out some of the sample statements elementwise.

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• mask (torch.BoolTensor) – a `{0,1}`-valued masking tensor (1 includes a site, 0 excludes a site)

Returns

stochastic function decorated with a `MaskMessenger`

### PlateMessenger¶

class PlateMessenger(name: str, size: Optional[int] = None, subsample_size: Optional[int] = None, subsample: Optional[torch.Tensor] = None, dim: Optional[int] = None, use_cuda: Optional[bool] = None, device: Optional[str] = None)[source]

Swiss army knife of broadcasting amazingness: combines shape inference, independence annotation, and subsampling

block_plate(name: Optional[str] = None, dim: Optional[int] = None, *, strict: bool = True) Iterator[None][source]

EXPERIMENTAL Context manager to temporarily block a single enclosing plate.

This is useful for sampling auxiliary variables or lazily sampling global variables that are needed in a plated context. For example the following models are equivalent:

Example:

```def model_1(data):
loc = pyro.sample("loc", dist.Normal(0, 1))
with pyro.plate("data", len(data)):
with block_plate("data"):
scale = pyro.sample("scale", dist.LogNormal(0, 1))
pyro.sample("x", dist.Normal(loc, scale))

def model_2(data):
loc = pyro.sample("loc", dist.Normal(0, 1))
scale = pyro.sample("scale", dist.LogNormal(0, 1))
with pyro.plate("data", len(data)):
pyro.sample("x", dist.Normal(loc, scale))
```
Parameters
• name (str) – Optional name of plate to match.

• dim (int) – Optional dim of plate to match. Must be negative.

• strict (bool) – Whether to error if no matching plate is found. Defaults to True.

Raises

ValueError if no enclosing plate was found and `strict=True`.

### ReentrantMessenger¶

class ReentrantMessenger[source]

### ReparamMessenger¶

class ReparamHandler(msngr, fn: Callable[[pyro.poutine.reparam_messenger._P], pyro.poutine.reparam_messenger._T])[source]

Bases: `Generic`[`pyro.poutine.reparam_messenger._P`, `pyro.poutine.reparam_messenger._T`]

Reparameterization poutine.

class ReparamMessenger(config: Union[Dict[str, Reparam], Callable[[Message], Optional[Reparam]]])[source]

Reparametrizes each affected sample site into one or more auxiliary sample sites followed by a deterministic transformation [1].

To specify reparameterizers, pass a `config` dict or callable to the constructor. See the `pyro.infer.reparam` module for available reparameterizers.

Note some reparameterizers can examine the `*args,**kwargs` inputs of functions they affect; these reparameterizers require using `poutine.reparam` as a decorator rather than as a context manager.

[1] Maria I. Gorinova, Dave Moore, Matthew D. Hoffman (2019)

“Automatic Reparameterisation of Probabilistic Programs” https://arxiv.org/pdf/1906.03028.pdf

Parameters

config (dict or callable) – Configuration, either a dict mapping site name to `Reparameterizer` , or a function mapping site to `Reparam` or None. See `pyro.infer.reparam.strategies` for built-in configuration strategies.

### ReplayMessenger¶

class ReplayMessenger(trace: Optional[Trace] = None, params: Optional[Dict[str, torch.Tensor]] = None)[source]

Given a callable that contains Pyro primitive calls, return a callable that runs the original, reusing the values at sites in trace at those sites in the new trace

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

`replay` makes `sample` statements behave as if they had sampled the values at the corresponding sites in the trace:

```>>> old_trace = pyro.poutine.trace(model).get_trace(1.0)
>>> replayed_model = pyro.poutine.replay(model, trace=old_trace)
>>> bool(replayed_model(0.0) == old_trace.nodes["_RETURN"]["value"])
True
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• trace – a `Trace` data structure to replay against

• params – dict of names of param sites and constrained values in fn to replay against

Returns

a stochastic function decorated with a `ReplayMessenger`

### ScaleMessenger¶

class ScaleMessenger(scale: Union[float, torch.Tensor])[source]

Given a stochastic function with some sample statements and a positive scale factor, scale the score of all sample and observe sites in the function.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     pyro.sample("z", dist.Normal(x, s), obs=torch.tensor(1.0))
```

`scale` multiplicatively scales the log-probabilities of sample sites:

```>>> scaled_model = pyro.poutine.scale(model, scale=0.5)
>>> scaled_tr = pyro.poutine.trace(scaled_model).get_trace(0.0)
>>> unscaled_tr = pyro.poutine.trace(model).get_trace(0.0)
>>> bool((scaled_tr.log_prob_sum() == 0.5 * unscaled_tr.log_prob_sum()).all())
True
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• scale – a positive scaling factor

Returns

stochastic function decorated with a `ScaleMessenger`

### SeedMessenger¶

class SeedMessenger(rng_seed: int)[source]

Handler to set the random number generator to a pre-defined state by setting its seed. This is the same as calling `pyro.set_rng_seed()` before the call to fn. This handler has no additional effect on primitive statements on the standard Pyro backend, but it might intercept `pyro.sample` calls in other backends. e.g. the NumPy backend.

Parameters
• fn – a stochastic function (callable containing Pyro primitive calls).

• rng_seed (int) – rng seed.

### SubsampleMessenger¶

class SubsampleMessenger(name: str, size: Optional[int] = None, subsample_size: Optional[int] = None, subsample: Optional[torch.Tensor] = None, dim: Optional[int] = None, use_cuda: Optional[bool] = None, device: Optional[str] = None)[source]

Extension of IndepMessenger that includes subsampling.

### SubstituteMessenger¶

class SubstituteMessenger(data: Dict[str, torch.Tensor])[source]

Given a stochastic function with param calls and a set of parameter values, create a stochastic function where all param calls are substituted with the fixed values. data should be a dict of names to values. Consider the following Pyro program:

```>>> def model(x):
...     a = pyro.param("a", torch.tensor(0.5))
...     x = pyro.sample("x", dist.Bernoulli(probs=a))
...     return x
>>> substituted_model = pyro.poutine.substitute(model, data={"a": torch.tensor(0.3)})
```

In this example, site a will now have value torch.tensor(0.3). :param data: dictionary of values keyed by site names. :returns: `fn` decorated with a `SubstituteMessenger`

### TraceMessenger¶

class TraceHandler(msngr: pyro.poutine.trace_messenger.TraceMessenger, fn: Callable[[pyro.poutine.trace_messenger._P], pyro.poutine.trace_messenger._T])[source]

Bases: `Generic`[`pyro.poutine.trace_messenger._P`, `pyro.poutine.trace_messenger._T`]

Execution trace poutine.

A TraceHandler records the input and output to every Pyro primitive and stores them as a site in a Trace(). This should, in theory, be sufficient information for every inference algorithm (along with the implicit computational graph in the Variables?)

We can also use this for visualization.

get_trace(*args, **kwargs) [source]
Returns

data structure

Return type

pyro.poutine.Trace

Helper method for a very common use case. Calls this poutine and returns its trace instead of the function’s return value.

property trace: pyro.poutine.trace_struct.Trace
class TraceMessenger(graph_type: Optional[Literal['flat', 'dense']] = None, param_only: Optional[bool] = None)[source]

Return a handler that records the inputs and outputs of primitive calls and their dependencies.

Consider the following Pyro program:

```>>> def model(x):
...     s = pyro.param("s", torch.tensor(0.5))
...     z = pyro.sample("z", dist.Normal(x, s))
...     return z ** 2
```

We can record its execution using `trace` and use the resulting data structure to compute the log-joint probability of all of the sample sites in the execution or extract all parameters.

```>>> trace = pyro.poutine.trace(model).get_trace(0.0)
>>> logp = trace.log_prob_sum()
>>> params = [trace.nodes[name]["value"].unconstrained() for name in trace.param_nodes]
```
Parameters
• fn – a stochastic function (callable containing Pyro primitive calls)

• graph_type – string that specifies the kind of graph to construct

• param_only – if true, only records params and not samples

Returns

stochastic function decorated with a `TraceMessenger`

get_trace() [source]
Returns

data structure

Return type

pyro.poutine.Trace

Helper method for a very common use case. Returns a shallow copy of `self.trace`.

identify_dense_edges(trace: pyro.poutine.trace_struct.Trace) None[source]

Modifies a trace in-place by adding all edges based on the cond_indep_stack information stored at each site.

### UnconditionMessenger¶

class UnconditionMessenger[source]

Messenger to force the value of observed nodes to be sampled from their distribution, ignoring observations.

### GuideMessenger¶

class GuideMessenger(model: Callable)[source]

Abstract base class for effect-based guides.

Derived classes must implement the `get_posterior()` method.

property model: Callable
__call__(*args, **kwargs) Dict[str, torch.Tensor][source]

Draws posterior samples from the guide and replays the model against those samples.

Returns

A dict mapping sample site name to sample value. This includes latent, deterministic, and observed values.

Return type

dict

abstract get_posterior(name: str, prior: TorchDistributionMixin) Union[TorchDistributionMixin, torch.Tensor][source]

Abstract method to compute a posterior distribution or sample a posterior value given a prior distribution conditioned on upstream posterior samples.

Implementations may use `pyro.param` and `pyro.sample` inside this function, but `pyro.sample` statements should set `infer={"is_auxiliary": True"}` .

Implementations may access further information for computations:

• `value = self.upstream_value(name)` is the value of an upstream

sample or deterministic site.

• `self.trace` is a trace of upstream sites, and may be useful for other information such as `self.trace.nodes["my_site"]["fn"]` or `self.trace.nodes["my_site"]["cond_indep_stack"]` .

• `args, kwargs = self.args_kwargs` are the inputs to the model, and

may be useful for amortization.

Parameters
• name (str) – The name of the sample site to sample.

• prior (Distribution) – The prior distribution of this sample site (conditioned on upstream samples from the posterior).

Returns

A posterior distribution or sample from the posterior distribution.

Return type
upstream_value(name: str) Optional[torch.Tensor][source]

For use in `get_posterior()` .

Returns

The value of an upstream sample or deterministic site

Return type

torch.Tensor

get_traces() [source]

This can be called after running `__call__()` to extract a pair of traces.

In contrast to the trace-replay pattern of generating a pair of traces, `GuideMessenger` interleaves model and guide computations, so only a single `guide(*args, **kwargs)` call is needed to create both traces. This function merely extract the relevant information from this guide’s `.trace` attribute.

Returns

a pair `(model_trace, guide_trace)`

Return type

tuple