Type annotate poutines

pyro/infer/autoguide/effect.py

@@ -68,7 +68,7 @@ def _adjust_plates(self, value: torch.Tensor, event_dim: int) -> torch.Tensor:
         Adjusts plates for generating initial values of parameters.
         """
         for f in get_plates():
-            full_size = getattr(f, "full_size", f.size)
+            full_size = f.full_size or f.size
             dim = f.dim - event_dim
             if f in self._outer_plates or f.name in self.amortized_plates:
                 if -value.dim() <= dim:

pyro/infer/autoguide/guides.py

@@ -138,7 +138,7 @@ def _create_plates(self, *args, **kwargs):
                 self.plates = {p.name: p for p in plates}
             for name, frame in sorted(self._prototype_frames.items()):
                 if name not in self.plates:
-                    full_size = getattr(frame, "full_size", frame.size)
+                    full_size = frame.full_size or frame.size
                     self.plates[name] = pyro.plate(
                         name, full_size, dim=frame.dim, subsample_size=frame.size
                     )
@@ -363,7 +363,7 @@ def _setup_prototype(self, *args, **kwargs):
 
             # If subsampling, repeat init_value to full size.
             for frame in site["cond_indep_stack"]:
-                full_size = getattr(frame, "full_size", frame.size)
+                full_size = frame.full_size or frame.size
                 if full_size != frame.size:
                     dim = frame.dim - event_dim
                     value = periodic_repeat(value, full_size, dim).contiguous()
@@ -475,7 +475,7 @@ def _setup_prototype(self, *args, **kwargs):
 
             # If subsampling, repeat init_value to full size.
             for frame in site["cond_indep_stack"]:
-                full_size = getattr(frame, "full_size", frame.size)
+                full_size = frame.full_size or frame.size
                 if full_size != frame.size:
                     dim = frame.dim - event_dim
                     init_loc = periodic_repeat(init_loc, full_size, dim).contiguous()

pyro/poutine/enum_messenger.py

@@ -23,6 +23,7 @@ def _tmc_mixture_sample(msg: Message) -> torch.Tensor:
     batch_shape = [1] * len(dist.batch_shape)
     for f in msg["cond_indep_stack"]:
         if f.vectorized:
+            assert f.dim is not None
             batch_shape[f.dim] = f.size if f.size > 0 else dist.batch_shape[f.dim]
     batch_shape_tuple = tuple(batch_shape)
 
@@ -72,6 +73,7 @@ def _tmc_diagonal_sample(msg: Message) -> torch.Tensor:
     batch_shape = [1] * len(dist.batch_shape)
     for f in msg["cond_indep_stack"]:
         if f.vectorized:
+            assert f.dim is not None
             batch_shape[f.dim] = f.size if f.size > 0 else dist.batch_shape[f.dim]
     batch_shape_tuple = tuple(batch_shape)
 

pyro/poutine/escape_messenger.py

@@ -1,16 +1,18 @@
 # Copyright (c) 2017-2019 Uber Technologies, Inc.
 # SPDX-License-Identifier: Apache-2.0
 
-from .messenger import Messenger
-from .runtime import NonlocalExit
+from typing import Callable
+
+from pyro.poutine.messenger import Messenger
+from pyro.poutine.runtime import Message, NonlocalExit
 
 
 class EscapeMessenger(Messenger):
     """
     Messenger that does a nonlocal exit by raising a util.NonlocalExit exception
     """
 
-    def __init__(self, escape_fn):
+    def __init__(self, escape_fn: Callable[[Message], bool]) -> None:
         """
         :param escape_fn: function that takes a msg as input and returns True
             if the poutine should perform a nonlocal exit at that site.
@@ -20,7 +22,7 @@ def __init__(self, escape_fn):
         super().__init__()
         self.escape_fn = escape_fn
 
-    def _pyro_sample(self, msg):
+    def _pyro_sample(self, msg: Message) -> None:
         """
         :param msg: current message at a trace site
         :returns: a sample from the stochastic function at the site.
@@ -38,4 +40,3 @@ def cont(m):
                 raise NonlocalExit(m)
 
             msg["continuation"] = cont
-        return None

pyro/poutine/indep_messenger.py

@@ -2,40 +2,46 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import numbers
-from collections import namedtuple
+from typing import Iterator, NamedTuple, Optional, Tuple
 
 import torch
+from typing_extensions import Self
 
+from pyro.poutine.messenger import Messenger
+from pyro.poutine.runtime import _DIM_ALLOCATOR, Message
 from pyro.util import ignore_jit_warnings
 
-from .messenger import Messenger
-from .runtime import _DIM_ALLOCATOR
 
+class CondIndepStackFrame(NamedTuple):
+    name: str
+    dim: Optional[int]
+    size: int
+    counter: int
+    full_size: Optional[int] = None
 
-class CondIndepStackFrame(
-    namedtuple("CondIndepStackFrame", ["name", "dim", "size", "counter"])
-):
     @property
-    def vectorized(self):
+    def vectorized(self) -> bool:
         return self.dim is not None
 
-    def _key(self):
+    def _key(self) -> Tuple[str, Optional[int], int, int]:
         with ignore_jit_warnings(["Converting a tensor to a Python number"]):
             size = (
-                self.size.item() if isinstance(self.size, torch.Tensor) else self.size
+                self.size.item() if isinstance(self.size, torch.Tensor) else self.size  # type: ignore[attr-defined]
             )
             return self.name, self.dim, size, self.counter
 
-    def __eq__(self, other):
-        return type(self) == type(other) and self._key() == other._key()
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, CondIndepStackFrame):
+            return False
+        return self._key() == other._key()
 
-    def __ne__(self, other):
+    def __ne__(self, other: object) -> bool:
         return not self.__eq__(other)
 
-    def __hash__(self):
+    def __hash__(self) -> int:
         return hash(self._key())
 
-    def __str__(self):
+    def __str__(self) -> str:
         return self.name
 
 
@@ -59,7 +65,13 @@ class IndepMessenger(Messenger):
 
     """
 
-    def __init__(self, name=None, size=None, dim=None, device=None):
+    def __init__(
+        self,
+        name: str,
+        size: int,
+        dim: Optional[int] = None,
+        device: Optional[str] = None,
+    ):
         if not torch._C._get_tracing_state() and size == 0:
             raise ZeroDivisionError("size cannot be zero")
 
@@ -68,20 +80,20 @@ def __init__(self, name=None, size=None, dim=None, device=None):
         if dim is not None:
             self._vectorized = True
 
-        self._indices = None
+        self._indices: Optional[torch.Tensor] = None
         self.name = name
         self.dim = dim
         self.size = size
         self.device = device
         self.counter = 0
 
-    def next_context(self):
+    def next_context(self) -> None:
         """
         Increments the counter.
         """
         self.counter += 1
 
-    def __enter__(self):
+    def __enter__(self) -> Self:
         if self._vectorized is not False:
             self._vectorized = True
 
@@ -90,12 +102,13 @@ def __enter__(self):
 
         return super().__enter__()
 
-    def __exit__(self, *args):
+    def __exit__(self, *args) -> None:
         if self._vectorized is True:
+            assert self.dim is not None
             _DIM_ALLOCATOR.free(self.name, self.dim)
         return super().__exit__(*args)
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator[int]:
         if self._vectorized is True or self.dim is not None:
             raise ValueError(
                 "cannot use plate {} as both vectorized and non-vectorized"
@@ -110,18 +123,19 @@ def __iter__(self):
                 with self:
                     yield i if isinstance(i, numbers.Number) else i.item()
 
-    def _reset(self):
+    def _reset(self) -> None:
         if self._vectorized:
+            assert self.dim is not None
             _DIM_ALLOCATOR.free(self.name, self.dim)
         self._vectorized = None
         self.counter = 0
 
     @property
-    def indices(self):
+    def indices(self) -> torch.Tensor:
         if self._indices is None:
             self._indices = torch.arange(self.size, dtype=torch.long).to(self.device)
         return self._indices
 
-    def _process_message(self, msg):
+    def _process_message(self, msg: Message) -> None:
         frame = CondIndepStackFrame(self.name, self.dim, self.size, self.counter)
         msg["cond_indep_stack"] = (frame,) + msg["cond_indep_stack"]

pyro/poutine/infer_config_messenger.py

@@ -1,7 +1,10 @@
 # Copyright (c) 2017-2019 Uber Technologies, Inc.
 # SPDX-License-Identifier: Apache-2.0
 
-from .messenger import Messenger
+from typing import Callable
+
+from pyro.poutine.messenger import Messenger
+from pyro.poutine.runtime import InferDict, Message
 
 
 class InferConfigMessenger(Messenger):
@@ -15,7 +18,7 @@ class InferConfigMessenger(Messenger):
     :returns: stochastic function decorated with :class:`~pyro.poutine.infer_config_messenger.InferConfigMessenger`
     """
 
-    def __init__(self, config_fn):
+    def __init__(self, config_fn: Callable[[Message], InferDict]):
         """
         :param config_fn: a callable taking a site and returning an infer dict
 
@@ -25,7 +28,7 @@ def __init__(self, config_fn):
         super().__init__()
         self.config_fn = config_fn
 
-    def _pyro_sample(self, msg):
+    def _pyro_sample(self, msg: Message) -> None:
         """
         :param msg: current message at a trace site.
 
@@ -35,10 +38,10 @@ def _pyro_sample(self, msg):
         Otherwise, implements default sampling behavior
         with no additional effects.
         """
+        assert msg["infer"] is not None
         msg["infer"].update(self.config_fn(msg))
-        return None
 
-    def _pyro_param(self, msg):
+    def _pyro_param(self, msg: Message) -> None:
         """
         :param msg: current message at a trace site.
 
@@ -48,5 +51,5 @@ def _pyro_param(self, msg):
         Otherwise, implements default param behavior
         with no additional effects.
         """
+        assert msg["infer"] is not None
         msg["infer"].update(self.config_fn(msg))
-        return None

pyro/poutine/lift_messenger.py

@@ -2,13 +2,16 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import warnings
+from typing import Callable, Dict, Set, Union
+
+from typing_extensions import Self
 
 from pyro import params
 from pyro.distributions.distribution import Distribution
+from pyro.poutine.messenger import Messenger
+from pyro.poutine.runtime import Message
 from pyro.poutine.util import is_validation_enabled
 
-from .messenger import Messenger
-
 
 class LiftMessenger(Messenger):
     """
@@ -40,7 +43,10 @@ class LiftMessenger(Messenger):
     :returns: ``fn`` decorated with a :class:`~pyro.poutine.lift_messenger.LiftMessenger`
     """
 
-    def __init__(self, prior):
+    def __init__(
+        self,
+        prior: Union[Callable, Distribution, Dict[str, Union[Distribution, Callable]]],
+    ) -> None:
         """
         :param prior: prior used to lift parameters. Prior can be of type
                       dict, pyro.distributions, or a python stochastic fn
@@ -49,16 +55,16 @@ def __init__(self, prior):
         """
         super().__init__()
         self.prior = prior
-        self._samples_cache = {}
+        self._samples_cache: Dict[str, Message] = {}
 
-    def __enter__(self):
+    def __enter__(self) -> Self:
         self._samples_cache = {}
         if is_validation_enabled() and isinstance(self.prior, dict):
-            self._param_hits = set()
-            self._param_misses = set()
+            self._param_hits: Set[str] = set()
+            self._param_misses: Set[str] = set()
         return super().__enter__()
 
-    def __exit__(self, *args, **kwargs):
+    def __exit__(self, *args, **kwargs) -> None:
         self._samples_cache = {}
         if is_validation_enabled() and isinstance(self.prior, dict):
             extra = set(self.prior) - self._param_hits
@@ -71,17 +77,18 @@ def __exit__(self, *args, **kwargs):
                 )
         return super().__exit__(*args, **kwargs)
 
-    def _pyro_sample(self, msg):
+    def _pyro_sample(self, msg: Message) -> None:
         return None
 
-    def _pyro_param(self, msg):
+    def _pyro_param(self, msg: Message) -> None:
         """
         Overrides the `pyro.param` call with samples sampled from the
         distribution specified in the prior. The prior can be a
         pyro.distributions object or a dict of distributions keyed
         on the param names. If the param name does not match the
         name the keys in the prior, that param name is unchanged.
         """
+        assert msg["name"] is not None
         name = msg["name"]
         param_name = params.user_param_name(name)
         if isinstance(self.prior, dict):

pyro/poutine/mask_messenger.py

@@ -1,9 +1,12 @@
 # Copyright (c) 2017-2019 Uber Technologies, Inc.
 # SPDX-License-Identifier: Apache-2.0
 
+from typing import Union
+
 import torch
 
-from .messenger import Messenger
+from pyro.poutine.messenger import Messenger
+from pyro.poutine.runtime import Message
 
 
 class MaskMessenger(Messenger):
@@ -17,7 +20,7 @@ class MaskMessenger(Messenger):
     :returns: stochastic function decorated with a :class:`~pyro.poutine.scale_messenger.MaskMessenger`
     """
 
-    def __init__(self, mask):
+    def __init__(self, mask: Union[bool, torch.BoolTensor]) -> None:
         if isinstance(mask, torch.Tensor):
             if mask.dtype != torch.bool:
                 raise ValueError(
@@ -31,6 +34,5 @@ def __init__(self, mask):
         super().__init__()
         self.mask = mask
 
-    def _process_message(self, msg):
+    def _process_message(self, msg: Message) -> None:
         msg["mask"] = self.mask if msg["mask"] is None else msg["mask"] & self.mask
-        return None

pyro/poutine/runtime.py

@@ -50,8 +50,10 @@ class InferDict(TypedDict, total=False):
     is_auxiliary: bool
     is_observed: bool
     num_samples: int
+    obs: Optional[torch.Tensor]
     prior: TorchDistributionMixin
     tmc: Literal["diagonal", "mixture"]
+    was_observed: bool
     _deterministic: bool
     _dim_to_symbol: Dict[int, str]
     _do_not_trace: bool