Get CI working with new black, torch, pytest

examples/capture_recapture/cjs.py

@@ -324,9 +324,11 @@ def expose_fn(msg):
         elbo = TraceTMC_ELBO(max_plate_nesting=1)
         tmc_model = poutine.infer_config(
             model,
-            lambda msg: {"num_samples": args.tmc_num_samples, "expand": False}
-            if msg["infer"].get("enumerate", None) == "parallel"
-            else {},
+            lambda msg: (
+                {"num_samples": args.tmc_num_samples, "expand": False}
+                if msg["infer"].get("enumerate", None) == "parallel"
+                else {}
+            ),
         )  # noqa: E501
         svi = SVI(tmc_model, guide, optim, elbo)
     else:

examples/contrib/funsor/hmm.py

@@ -737,9 +737,11 @@ def main(args):
         elbo = Elbo(max_plate_nesting=1 if model is model_0 else 2)
         tmc_model = handlers.infer_config(
             model,
-            lambda msg: {"num_samples": args.tmc_num_samples, "expand": False}
-            if msg["infer"].get("enumerate", None) == "parallel"
-            else {},
+            lambda msg: (
+                {"num_samples": args.tmc_num_samples, "expand": False}
+                if msg["infer"].get("enumerate", None) == "parallel"
+                else {}
+            ),
         )  # noqa: E501
         svi = infer.SVI(tmc_model, guide, optimizer, elbo)
     else:

examples/hmm.py

@@ -677,9 +677,11 @@ def main(args):
         elbo = TraceTMC_ELBO(max_plate_nesting=1 if model is model_0 else 2)
         tmc_model = poutine.infer_config(
             model,
-            lambda msg: {"num_samples": args.tmc_num_samples, "expand": False}
-            if msg["infer"].get("enumerate", None) == "parallel"
-            else {},
+            lambda msg: (
+                {"num_samples": args.tmc_num_samples, "expand": False}
+                if msg["infer"].get("enumerate", None) == "parallel"
+                else {}
+            ),
         )  # noqa: E501
         svi = SVI(tmc_model, guide, optim, elbo)
     else:

examples/mixed_hmm/experiment.py

@@ -143,16 +143,12 @@ def closure():
         re_str = "g" + (
             "n"
             if args["group"] is None
-            else "d"
-            if args["group"] == "discrete"
-            else "c"
+            else "d" if args["group"] == "discrete" else "c"
         )
         re_str += "i" + (
             "n"
             if args["individual"] is None
-            else "d"
-            if args["individual"] == "discrete"
-            else "c"
+            else "d" if args["individual"] == "discrete" else "c"
         )
         results_filename = "expt_{}_{}_{}.json".format(
             dataset, re_str, str(uuid.uuid4().hex)[0:5]

pyro/contrib/autoname/autoname.py

@@ -154,8 +154,7 @@ def _pyro_genname(msg):
 
 
 @_make_handler(AutonameMessenger, __name__)
-def autoname(fn=None, name=None):
-    ...
+def autoname(fn=None, name=None): ...
 
 
 @singledispatch

pyro/contrib/bnn/hidden_layer.py

@@ -55,6 +55,7 @@ class HiddenLayer(TorchDistribution):
     "Variational dropout and the local reparameterization trick."
     Advances in Neural Information Processing Systems. 2015.
     """
+
     has_rsample = True
 
     def __init__(

pyro/contrib/cevae/__init__.py

@@ -271,7 +271,7 @@ def __init__(self, data):
         super().__init__()
         with torch.no_grad():
             loc = data.mean(0)
-            scale = data.std(0)
+            scale = data.std(0, unbiased=False)
             scale[~(scale > 0)] = 1.0
             self.register_buffer("loc", loc)
             self.register_buffer("inv_scale", scale.reciprocal())

pyro/contrib/epidemiology/models.py

@@ -680,9 +680,11 @@ def transition(self, params, state, t):
             coal_rate = R * (1.0 + 1.0 / k) / (tau_i * state["I"] + 1e-8)
             pyro.factor(
                 "coalescent_{}".format(t),
-                self.coal_likelihood(coal_rate, t)
-                if t_is_observed
-                else torch.tensor(0.0),
+                (
+                    self.coal_likelihood(coal_rate, t)
+                    if t_is_observed
+                    else torch.tensor(0.0)
+                ),
             )
 
         # Update compartements with flows.

pyro/contrib/funsor/handlers/__init__.py

@@ -23,18 +23,15 @@
 
 
 @_make_handler(EnumMessenger, __name__)
-def enum(fn=None, first_available_dim=None):
-    ...
+def enum(fn=None, first_available_dim=None): ...
 
 
 @_make_handler(MarkovMessenger, __name__)
-def markov(fn=None, history=1, keep=False):
-    ...
+def markov(fn=None, history=1, keep=False): ...
 
 
 @_make_handler(NamedMessenger, __name__)
-def named(fn=None, first_available_dim=None):
-    ...
+def named(fn=None, first_available_dim=None): ...
 
 
 @_make_handler(PlateMessenger, __name__)
@@ -47,20 +44,16 @@ def plate(
     dim=None,
     use_cuda=None,
     device=None,
-):
-    ...
+): ...
 
 
 @_make_handler(ReplayMessenger, __name__)
-def replay(fn=None, trace=None, params=None):
-    ...
+def replay(fn=None, trace=None, params=None): ...
 
 
 @_make_handler(TraceMessenger, __name__)
-def trace(fn=None, graph_type=None, param_only=None, pack_online=True):
-    ...
+def trace(fn=None, graph_type=None, param_only=None, pack_online=True): ...
 
 
 @_make_handler(VectorizedMarkovMessenger, __name__)
-def vectorized_markov(fn=None, name=None, size=None, dim=None, history=1):
-    ...
+def vectorized_markov(fn=None, name=None, size=None, dim=None, history=1): ...

pyro/contrib/funsor/handlers/replay_messenger.py

@@ -14,9 +14,9 @@ class ReplayMessenger(OrigReplayMessenger):
 
     def _pyro_sample(self, msg):
         name = msg["name"]
-        msg[
-            "replay_active"
-        ] = True  # indicate replaying so importance weights can be scaled
+        msg["replay_active"] = (
+            True  # indicate replaying so importance weights can be scaled
+        )
         if self.trace is None:
             return
 

pyro/contrib/funsor/infer/trace_elbo.py

@@ -18,9 +18,11 @@
 @copy_docs_from(_OrigTrace_ELBO)
 class Trace_ELBO(ELBO):
     def differentiable_loss(self, model, guide, *args, **kwargs):
-        with enum(), plate(
-            size=self.num_particles
-        ) if self.num_particles > 1 else contextlib.ExitStack():
+        with enum(), (
+            plate(size=self.num_particles)
+            if self.num_particles > 1
+            else contextlib.ExitStack()
+        ):
             guide_tr = trace(config_enumerate(default="flat")(guide)).get_trace(
                 *args, **kwargs
             )

pyro/contrib/funsor/infer/traceenum_elbo.py

@@ -93,12 +93,14 @@ def terms_from_trace(tr):
 class TraceMarkovEnum_ELBO(ELBO):
     def differentiable_loss(self, model, guide, *args, **kwargs):
         # get batched, enumerated, to_funsor-ed traces from the guide and model
-        with plate(
-            size=self.num_particles
-        ) if self.num_particles > 1 else contextlib.ExitStack(), enum(
-            first_available_dim=(-self.max_plate_nesting - 1)
-            if self.max_plate_nesting
-            else None
+        with (
+            plate(size=self.num_particles)
+            if self.num_particles > 1
+            else contextlib.ExitStack()
+        ), enum(
+            first_available_dim=(
+                (-self.max_plate_nesting - 1) if self.max_plate_nesting else None
+            )
         ):
             guide_tr = trace(guide).get_trace(*args, **kwargs)
             model_tr = trace(replay(model, trace=guide_tr)).get_trace(*args, **kwargs)
@@ -170,12 +172,14 @@ def differentiable_loss(self, model, guide, *args, **kwargs):
 class TraceEnum_ELBO(ELBO):
     def differentiable_loss(self, model, guide, *args, **kwargs):
         # get batched, enumerated, to_funsor-ed traces from the guide and model
-        with plate(
-            size=self.num_particles
-        ) if self.num_particles > 1 else contextlib.ExitStack(), enum(
-            first_available_dim=(-self.max_plate_nesting - 1)
-            if self.max_plate_nesting
-            else None
+        with (
+            plate(size=self.num_particles)
+            if self.num_particles > 1
+            else contextlib.ExitStack()
+        ), enum(
+            first_available_dim=(
+                (-self.max_plate_nesting - 1) if self.max_plate_nesting else None
+            )
         ):
             guide_tr = trace(guide).get_trace(*args, **kwargs)
             model_tr = trace(replay(model, trace=guide_tr)).get_trace(*args, **kwargs)

pyro/contrib/funsor/infer/tracetmc_elbo.py

@@ -16,12 +16,14 @@
 @copy_docs_from(_OrigTraceTMC_ELBO)
 class TraceTMC_ELBO(ELBO):
     def differentiable_loss(self, model, guide, *args, **kwargs):
-        with plate(
-            size=self.num_particles
-        ) if self.num_particles > 1 else contextlib.ExitStack(), enum(
-            first_available_dim=(-self.max_plate_nesting - 1)
-            if self.max_plate_nesting
-            else None
+        with (
+            plate(size=self.num_particles)
+            if self.num_particles > 1
+            else contextlib.ExitStack()
+        ), enum(
+            first_available_dim=(
+                (-self.max_plate_nesting - 1) if self.max_plate_nesting else None
+            )
         ):
             guide_tr = trace(guide).get_trace(*args, **kwargs)
             model_tr = trace(replay(model, trace=guide_tr)).get_trace(*args, **kwargs)

pyro/contrib/timeseries/lgssmgp.py

@@ -109,9 +109,9 @@ def _get_init_dist(self):
         covar[: self.full_gp_state_dim, : self.full_gp_state_dim] = block_diag_embed(
             self.kernel.stationary_covariance()
         )
-        covar[
-            self.full_gp_state_dim :, self.full_gp_state_dim :
-        ] = self.init_noise_scale_sq.diag_embed()
+        covar[self.full_gp_state_dim :, self.full_gp_state_dim :] = (
+            self.init_noise_scale_sq.diag_embed()
+        )
         return MultivariateNormal(loc, covar)
 
     def _get_obs_dist(self):
@@ -134,23 +134,23 @@ def get_dist(self, duration=None):
         trans_covar = self.z_trans_matrix.new_zeros(
             self.full_state_dim, self.full_state_dim
         )
-        trans_covar[
-            : self.full_gp_state_dim, : self.full_gp_state_dim
-        ] = block_diag_embed(gp_process_covar)
-        trans_covar[
-            self.full_gp_state_dim :, self.full_gp_state_dim :
-        ] = self.trans_noise_scale_sq.diag_embed()
+        trans_covar[: self.full_gp_state_dim, : self.full_gp_state_dim] = (
+            block_diag_embed(gp_process_covar)
+        )
+        trans_covar[self.full_gp_state_dim :, self.full_gp_state_dim :] = (
+            self.trans_noise_scale_sq.diag_embed()
+        )
         trans_dist = MultivariateNormal(
             trans_covar.new_zeros(self.full_state_dim), trans_covar
         )
 
         full_trans_mat = trans_covar.new_zeros(self.full_state_dim, self.full_state_dim)
-        full_trans_mat[
-            : self.full_gp_state_dim, : self.full_gp_state_dim
-        ] = block_diag_embed(gp_trans_matrix)
-        full_trans_mat[
-            self.full_gp_state_dim :, self.full_gp_state_dim :
-        ] = self.z_trans_matrix
+        full_trans_mat[: self.full_gp_state_dim, : self.full_gp_state_dim] = (
+            block_diag_embed(gp_trans_matrix)
+        )
+        full_trans_mat[self.full_gp_state_dim :, self.full_gp_state_dim :] = (
+            self.z_trans_matrix
+        )
 
         return dist.GaussianHMM(
             self._get_init_dist(),

pyro/contrib/tracking/distributions.py

@@ -27,6 +27,7 @@ class EKFDistribution(TorchDistribution):
     :param dt: time step
     :type dt: torch.Tensor
     """
+
     arg_constraints = {
         "measurement_cov": constraints.positive_definite,
         "P0": constraints.positive_definite,

pyro/distributions/__init__.py

@@ -48,7 +48,9 @@
     LinearHMM,
 )
 from pyro.distributions.improper_uniform import ImproperUniform
-from pyro.distributions.inverse_gamma import InverseGamma
+
+if "InverseGamma" not in locals():  # Use PyTorch version if available.
+    from pyro.distributions.inverse_gamma import InverseGamma
 from pyro.distributions.lkj import LKJ, LKJCorrCholesky
 from pyro.distributions.log_normal_negative_binomial import LogNormalNegativeBinomial
 from pyro.distributions.logistic import Logistic, SkewLogistic

pyro/distributions/asymmetriclaplace.py

@@ -199,9 +199,7 @@ def variance(self):
         total = left + right
         p = left / total
         q = right / total
-        return (
-            p * left**2 + q * right**2 + p * q * total**2 + self.soft_scale**2
-        )
+        return p * left**2 + q * right**2 + p * q * total**2 + self.soft_scale**2
 
 
 def _logerfc(x):

pyro/distributions/conditional.py

@@ -82,9 +82,11 @@ class ConditionalComposeTransformModule(
 
     def __init__(self, transforms, cache_size: int = 0):
         self.transforms = [
-            ConstantConditionalTransform(t)
-            if not isinstance(t, ConditionalTransform)
-            else t
+            (
+                ConstantConditionalTransform(t)
+                if not isinstance(t, ConditionalTransform)
+                else t
+            )
             for t in transforms
         ]
         super().__init__()
@@ -131,9 +133,11 @@ def __init__(self, base_dist, transforms):
             else ConstantConditionalDistribution(base_dist)
         )
         self.transforms = [
-            t
-            if isinstance(t, ConditionalTransform)
-            else ConstantConditionalTransform(t)
+            (
+                t
+                if isinstance(t, ConditionalTransform)
+                else ConstantConditionalTransform(t)
+            )
             for t in transforms
         ]
 

pyro/distributions/conjugate.py

@@ -47,6 +47,7 @@ class BetaBinomial(TorchDistribution):
     :param total_count: Number of Bernoulli trials.
     :type total_count: float or torch.Tensor
     """
+
     arg_constraints = {
         "concentration1": constraints.positive,
         "concentration0": constraints.positive,
@@ -150,6 +151,7 @@ class DirichletMultinomial(TorchDistribution):
     :param bool is_sparse: Whether to assume value is mostly zero when computing
         :meth:`log_prob`, which can speed up computation when data is sparse.
     """
+
     arg_constraints = {
         "concentration": constraints.independent(constraints.positive, 1),
         "total_count": constraints.nonnegative_integer,

pyro/distributions/constraints.py

@@ -158,12 +158,14 @@ def check(self, value):
     {}
     """.format(
             _name,
-            "alias of :class:`torch.distributions.constraints.{}`".format(_name)
-            if globals()[_name].__module__.startswith("torch")
-            else ".. autoclass:: {}".format(
-                _name
-                if type(globals()[_name]) is type
-                else type(globals()[_name]).__name__
+            (
+                "alias of :class:`torch.distributions.constraints.{}`".format(_name)
+                if globals()[_name].__module__.startswith("torch")
+                else ".. autoclass:: {}".format(
+                    _name
+                    if type(globals()[_name]) is type
+                    else type(globals()[_name]).__name__
+                )
             ),
         )
         for _name in sorted(__all__)

pyro/distributions/grouped_normal_normal.py

@@ -48,6 +48,7 @@ class GroupedNormalNormal(TorchDistribution):
     :param torch.LongTensor group_idx: Tensor of indices of shape `(num_data,)` linking each observation to one
         of the `num_groups` groups that are specified in `prior_loc` and `prior_scale`.
     """
+
     arg_constraints = {
         "prior_loc": constraints.real,
         "prior_scale": constraints.positive,