Upgrade deprecated APIs in PyTorch 1.11 & clean out some legacy codes

src/beanmachine/ppl/experimental/tests/gp/inference_test.py

@@ -14,7 +14,6 @@
 from beanmachine.ppl.experimental.gp import likelihoods
 from beanmachine.ppl.experimental.gp.kernels import PeriodicKernel, ScaleKernel
 from beanmachine.ppl.experimental.gp.models import SimpleGP
-from beanmachine.ppl.legacy.inference import SingleSiteNoUTurnSampler
 from gpytorch.distributions import MultivariateNormal
 
 
@@ -78,7 +77,7 @@ def test_simple_regression(self):
             self.lengthscale_prior(),
             self.period_length_prior(),
         ]
-        samples = SingleSiteNoUTurnSampler().infer(
+        samples = bm.SingleSiteNoUTurnSampler().infer(
             queries, obs, n_samples, num_chains=1
         )
 

src/beanmachine/ppl/experimental/tests/gp/models_test.py

@@ -10,7 +10,6 @@
 import torch.distributions as dist
 from beanmachine.ppl.experimental.gp import kernels, likelihoods
 from beanmachine.ppl.experimental.gp.models import BoTorchGP, SimpleGP
-from beanmachine.ppl.legacy.inference import SingleSiteNoUTurnSampler
 from botorch.posteriors.gpytorch import GPyTorchPosterior
 
 
@@ -34,7 +33,7 @@ def mean():
 
     def test_infer(self):
         self.model.train()
-        SingleSiteNoUTurnSampler().infer([self.p()], {}, num_samples=2, num_chains=1)
+        bm.SingleSiteNoUTurnSampler().infer([self.p()], {}, num_samples=2, num_chains=1)
 
     def test_load_and_predict(self):
         self.model.eval()

src/beanmachine/ppl/experimental/tests/vi/vi_test.py

@@ -450,7 +450,7 @@ def w():
         def q_y():
             weights = w()
             data = x()
-            p = torch.sigmoid(data @ weights.T)
+            p = torch.sigmoid(data @ weights)
             return dist.Bernoulli(p)
 
         opt_params = VariationalInference().infer(

src/beanmachine/ppl/inference/proposer/nmc/single_site_real_space_nmc_proposer.py

@@ -137,11 +137,11 @@ def get_proposal_distribution(self, world: World) -> dist.Distribution:
             # flip(flip(H^-1)) = flip((L^-1)') @ flip(L^-1)
             # H^-1 = flip(L^-1)' @ flip(L^-1)
             # flip(L^-1)' is the lower triangular cholesky factor for H^-1.
-            L_inv = torch.triangular_solve(
-                torch.eye(L.size(-1)).to(dtype=neg_hessian.dtype, device=node_device),
+            L_inv = torch.linalg.solve_triangular(
                 L,
+                torch.eye(L.size(-1)).to(dtype=neg_hessian.dtype, device=node_device),
                 upper=False,
-            ).solution
+            )
             L_chol = L_inv.flip([0, 1]).T
             distance = torch.cholesky_solve(first_gradient.unsqueeze(1), L).t()
             proposal_args = _ProposalArgs(

src/beanmachine/ppl/legacy/inference/__init__.py

@@ -8,33 +8,18 @@
 from beanmachine.ppl.legacy.inference.single_site_ancestral_mh import (
     SingleSiteAncestralMetropolisHastings,
 )
-from beanmachine.ppl.legacy.inference.single_site_hamiltonian_monte_carlo import (
-    SingleSiteHamiltonianMonteCarlo,
-)
 from beanmachine.ppl.legacy.inference.single_site_newtonian_monte_carlo import (
     SingleSiteNewtonianMonteCarlo,
 )
-from beanmachine.ppl.legacy.inference.single_site_no_u_turn_sampler import (
-    SingleSiteNoUTurnSampler,
-)
 from beanmachine.ppl.legacy.inference.single_site_random_walk import (
     SingleSiteRandomWalk,
 )
-from beanmachine.ppl.legacy.inference.single_site_uniform_mh import (
-    SingleSiteUniformMetropolisHastings,
-)
 
 
 __all__ = [
     "CompositionalInference",
     "RejectionSampling",
     "SingleSiteAncestralMetropolisHastings",
-    "SingleSiteHamiltonianMonteCarlo",
     "SingleSiteNewtonianMonteCarlo",
-    "SingleSiteNoUTurnSampler",
     "SingleSiteRandomWalk",
-    "SingleSiteUniformMetropolisHastings",
-    "Predictive",
-    "empirical",
-    "simulate",
 ]

src/beanmachine/ppl/legacy/inference/proposer/__init__.py

@@ -9,17 +9,9 @@
 from beanmachine.ppl.legacy.inference.proposer.single_site_newtonian_monte_carlo_proposer import (
     SingleSiteNewtonianMonteCarloProposer,
 )
-from beanmachine.ppl.legacy.inference.proposer.single_site_no_u_turn_sampler_proposer import (
-    SingleSiteNoUTurnSamplerProposer,
-)
-from beanmachine.ppl.legacy.inference.proposer.single_site_uniform_proposer import (
-    SingleSiteUniformProposer,
-)
 
 
 __all__ = [
     "SingleSiteAncestralProposer",
     "SingleSiteNewtonianMonteCarloProposer",
-    "SingleSiteNoUTurnSamplerProposer",
-    "SingleSiteUniformProposer",
 ]