Support torch.jit in ELBO implementations

docs/source/inference_algos.rst

@@ -13,21 +13,25 @@ ELBO
     :members:
     :undoc-members:
     :show-inheritance:
+    :member-order: bysource
 
 .. automodule:: pyro.infer.trace_elbo
     :members:
     :undoc-members:
     :show-inheritance:
+    :member-order: bysource
 
 .. automodule:: pyro.infer.tracegraph_elbo
     :members:
     :undoc-members:
     :show-inheritance:
+    :member-order: bysource
 
 .. automodule:: pyro.infer.traceenum_elbo
     :members:
     :undoc-members:
     :show-inheritance:
+    :member-order: bysource
 
 Importance
 ----------

pyro/infer/__init__.py

@@ -5,22 +5,25 @@
 from pyro.infer.enum import config_enumerate
 from pyro.infer.importance import Importance
 from pyro.infer.svi import SVI
-from pyro.infer.trace_elbo import Trace_ELBO
-from pyro.infer.traceenum_elbo import TraceEnum_ELBO
-from pyro.infer.tracegraph_elbo import TraceGraph_ELBO
+from pyro.infer.trace_elbo import JitTrace_ELBO, Trace_ELBO
+from pyro.infer.traceenum_elbo import JitTraceEnum_ELBO, TraceEnum_ELBO
+from pyro.infer.tracegraph_elbo import JitTraceGraph_ELBO, TraceGraph_ELBO
 from pyro.infer.util import enable_validation, is_validation_enabled
 
 __all__ = [
     "config_enumerate",
     "enable_validation",
     "is_validation_enabled",
     "ELBO",
-    "Importance",
     "EmpiricalMarginal",
-    "TracePredictive",
+    "Importance",
+    "JitTraceEnum_ELBO",
+    "JitTraceGraph_ELBO",
+    "JitTrace_ELBO",
     "SVI",
-    "TracePosterior",
-    "Trace_ELBO",
     "TraceEnum_ELBO",
     "TraceGraph_ELBO",
+    "TracePosterior",
+    "TracePredictive",
+    "Trace_ELBO",
 ]

pyro/infer/trace_elbo.py

@@ -1,7 +1,11 @@
 from __future__ import absolute_import, division, print_function
 
 import warnings
+import weakref
 
+import torch
+
+import pyro
 import pyro.poutine as poutine
 from pyro.distributions.util import is_identically_zero
 from pyro.infer.elbo import ELBO
@@ -142,3 +146,81 @@ def loss_and_grads(self, model, guide, *args, **kwargs):
         if torch_isnan(loss):
             warnings.warn('Encountered NAN loss')
         return loss
+
+
+class JitTrace_ELBO(Trace_ELBO):
+    """
+    Like :class:`Trace_ELBO` but uses :func:`torch.jit.compile` to compile
+    :meth:`loss_and_grads`.
+
+    This works only for a limited set of models:
+
+    -   Models must have static structure.
+    -   Models must not depend on any global data (except the param store).
+    -   All model inputs that are tensors must be passed in via ``*args``.
+    -   All model inputs that are *not* tensors must be passed in via
+        ``*kwargs``, and these will be fixed to their values on the first
+        call to :meth:`jit_loss_and_grads`.
+
+    .. warning:: Experimental. Interface subject to change.
+    """
+    def loss_and_grads(self, model, guide, *args, **kwargs):
+        if getattr(self, '_loss_and_surrogate_loss', None) is None:
+            # populate param store
+            with poutine.block():
+                with poutine.trace(param_only=True) as param_capture:
+                    for _ in self._get_traces(model, guide, *args, **kwargs):
+                        pass
+            self._param_names = list(param_capture.trace.nodes.keys())
+
+            # build a closure for loss_and_surrogate_loss
+            weakself = weakref.ref(self)
+
+            @torch.jit.compile(nderivs=1)
+            def loss_and_surrogate_loss(args_list, param_list):
+                self = weakself()
+                loss = 0.0
+                surrogate_loss = 0.0
+                for model_trace, guide_trace in self._get_traces(model, guide, *args_list, **kwargs):
+                    elbo_particle = 0
+                    surrogate_elbo_particle = 0
+                    log_r = None
+
+                    # compute elbo and surrogate elbo
+                    for name, site in model_trace.nodes.items():
+                        if site["type"] == "sample":
+                            elbo_particle = elbo_particle + site["log_prob_sum"]
+                            surrogate_elbo_particle = surrogate_elbo_particle + site["log_prob_sum"]
+
+                    for name, site in guide_trace.nodes.items():
+                        if site["type"] == "sample":
+                            log_prob, score_function_term, entropy_term = site["score_parts"]
+
+                            elbo_particle = elbo_particle - site["log_prob_sum"]
+
+                            if not is_identically_zero(entropy_term):
+                                surrogate_elbo_particle = surrogate_elbo_particle - entropy_term.sum()
+
+                            if not is_identically_zero(score_function_term):
+                                if log_r is None:
+                                    log_r = _compute_log_r(model_trace, guide_trace)
+                                site = log_r.sum_to(site["cond_indep_stack"])
+                                surrogate_elbo_particle = surrogate_elbo_particle + (site * score_function_term).sum()
+
+                    loss = loss - elbo_particle / self.num_particles
+                    surrogate_loss = surrogate_loss - surrogate_elbo_particle / self.num_particles
+
+                return loss, surrogate_loss
+
+            self._loss_and_surrogate_loss = loss_and_surrogate_loss
+
+        # invoke _loss_and_surrogate_loss
+        args_list = list(args)
+        param_list = [pyro.param(name).unconstrained() for name in self._param_names]
+        loss, surrogate_loss = self._loss_and_surrogate_loss(args_list, param_list)
+        surrogate_loss.backward()  # this line triggers jit compilation
+        loss = loss.item()
+
+        if torch_isnan(loss):
+            warnings.warn('Encountered NAN loss')
+        return loss

pyro/infer/traceenum_elbo.py

@@ -1,7 +1,11 @@
 from __future__ import absolute_import, division, print_function
 
 import warnings
+import weakref
 
+import torch
+
+import pyro
 import pyro.poutine as poutine
 from pyro.distributions.util import is_identically_zero
 from pyro.infer.elbo import ELBO
@@ -147,3 +151,53 @@ def loss_and_grads(self, model, guide, *args, **kwargs):
         if torch_isnan(loss):
             warnings.warn('Encountered NAN loss')
         return loss
+
+
+class JitTraceEnum_ELBO(TraceEnum_ELBO):
+    """
+    Like :class:`TraceEnum_ELBO` but uses :func:`torch.jit.compile` to
+    compile :meth:`loss_and_grads`.
+
+    This works only for a limited set of models:
+
+    -   Models must have static structure.
+    -   Models must not depend on any global data (except the param store).
+    -   All model inputs that are tensors must be passed in via ``*args``.
+    -   All model inputs that are *not* tensors must be passed in via
+        ``*kwargs``, and these will be fixed to their values on the first
+        call to :meth:`jit_loss_and_grads`.
+
+    .. warning:: Experimental. Interface subject to change.
+    """
+    def loss_and_grads(self, model, guide, *args, **kwargs):
+        if getattr(self, '_differentiable_loss', None) is None:
+            # populate param store
+            with poutine.block():
+                with poutine.trace(param_only=True) as param_capture:
+                    for _ in self._get_traces(model, guide, *args, **kwargs):
+                        pass
+            self._param_names = list(param_capture.trace.nodes.keys())
+
+            # build a closure for differentiable_loss
+            weakself = weakref.ref(self)
+
+            @torch.jit.compile(nderivs=1)
+            def differentiable_loss(args_list, param_list):
+                self = weakself()
+                elbo = 0.0
+                for model_trace, guide_trace in self._get_traces(model, guide, *args_list, **kwargs):
+                    elbo += _compute_dice_elbo(model_trace, guide_trace)
+                return elbo * (-1.0 / self.num_particles)
+
+            self._differentiable_loss = differentiable_loss
+
+        # invoke _differentiable_loss
+        args_list = list(args)
+        param_list = [pyro.param(name).unconstrained() for name in self._param_names]
+        differentiable_loss = self._differentiable_loss(args_list, param_list)
+        differentiable_loss.backward()  # this line triggers jit compilation
+        loss = differentiable_loss.item()
+
+        if torch_isnan(loss):
+            warnings.warn('Encountered NAN loss')
+        return loss

pyro/infer/tracegraph_elbo.py

@@ -1,6 +1,7 @@
 from __future__ import absolute_import, division, print_function
 
 import warnings
+import weakref
 from operator import itemgetter
 
 import networkx
@@ -99,13 +100,13 @@ def _compute_elbo_reparam(model_trace, guide_trace, non_reparam_nodes):
     # deal with log p(z|...) terms
     for name, site in model_trace.nodes.items():
         if site["type"] == "sample":
-            elbo += torch_item(site["log_prob_sum"])
+            elbo += site["log_prob_sum"]
             surrogate_elbo += site["log_prob_sum"]
 
     # deal with log q(z|...) terms
     for name, site in guide_trace.nodes.items():
         if site["type"] == "sample":
-            elbo -= torch_item(site["log_prob_sum"])
+            elbo -= site["log_prob_sum"]
             entropy_term = site["score_parts"].entropy_term
             if not is_identically_zero(entropy_term):
                 surrogate_elbo -= entropy_term.sum()
@@ -277,7 +278,84 @@ def _loss_and_grads_particle(self, weight, model_trace, guide_trace):
             surrogate_loss = -surrogate_elbo
             torch_backward(weight * (surrogate_loss + baseline_loss))
 
-        loss = -elbo
+        loss = -torch_item(elbo)
         if torch_isnan(loss):
             warnings.warn('Encountered NAN loss')
         return weight * loss
+
+
+class JitTraceGraph_ELBO(TraceGraph_ELBO):
+    """
+    Like :class:`TraceGraph_ELBO` but uses :func:`torch.jit.compile` to
+    compile :meth:`loss_and_grads`.
+
+    This works only for a limited set of models:
+
+    -   Models must have static structure.
+    -   Models must not depend on any global data (except the param store).
+    -   All model inputs that are tensors must be passed in via ``*args``.
+    -   All model inputs that are *not* tensors must be passed in via
+        ``*kwargs``, and these will be fixed to their values on the first
+        call to :meth:`loss_and_grads`.
+
+    .. warning:: Experimental. Interface subject to change.
+    """
+
+    def loss_and_grads(self, model, guide, *args, **kwargs):
+        if getattr(self, '_loss_and_surrogate_loss', None) is None:
+            # populate param store
+            with poutine.block():
+                with poutine.trace(param_only=True) as param_capture:
+                    for _ in self._get_traces(model, guide, *args, **kwargs):
+                        pass
+            self._param_names = list(param_capture.trace.nodes.keys())
+
+            # build a closure for loss_and_surrogate_loss
+            weakself = weakref.ref(self)
+
+            @torch.jit.compile(nderivs=1)
+            def loss_and_surrogate_loss(args_list, param_list):
+                self = weakself()
+                loss = 0.0
+                surrogate_loss = 0.0
+                for weight, model_trace, guide_trace in self._get_traces(model, guide, *args_list, **kwargs):
+                    model_trace.compute_log_prob()
+                    guide_trace.compute_score_parts()
+                    if is_validation_enabled():
+                        for site in model_trace.nodes.values():
+                            if site["type"] == "sample":
+                                check_site_shape(site, self.max_iarange_nesting)
+                        for site in guide_trace.nodes.values():
+                            if site["type"] == "sample":
+                                check_site_shape(site, self.max_iarange_nesting)
+
+                    # compute elbo for reparameterized nodes
+                    non_reparam_nodes = set(guide_trace.nonreparam_stochastic_nodes)
+                    elbo, surrogate_elbo = _compute_elbo_reparam(model_trace, guide_trace, non_reparam_nodes)
+
+                    # the following computations are only necessary if we have non-reparameterizable nodes
+                    baseline_loss = 0.0
+                    if non_reparam_nodes:
+                        downstream_costs, _ = _compute_downstream_costs(model_trace, guide_trace, non_reparam_nodes)
+                        surrogate_elbo_term, baseline_loss = _compute_elbo_non_reparam(guide_trace,
+                                                                                       non_reparam_nodes,
+                                                                                       downstream_costs)
+                        surrogate_elbo += surrogate_elbo_term
+
+                    loss = loss - weight * elbo
+                    surrogate_loss = surrogate_loss - weight * surrogate_elbo
+
+                return loss, surrogate_loss
+
+            self._loss_and_surrogate_loss = loss_and_surrogate_loss
+
+        # invoke _loss_and_surrogate_loss
+        args_list = list(args)
+        param_list = [pyro.param(name).unconstrained() for name in self._param_names]
+        loss, surrogate_loss = self._loss_and_surrogate_loss(args_list, param_list)
+        surrogate_loss.backward()  # this line triggers jit compilation
+        loss = loss.item()
+
+        if torch_isnan(loss):
+            warnings.warn('Encountered NAN loss')
+        return loss

tests/infer/test_gradient.py

@@ -10,9 +10,10 @@
 import pyro
 import pyro.distributions as dist
 from pyro.distributions.testing import fakes
-from pyro.infer import SVI, Trace_ELBO, TraceEnum_ELBO, TraceGraph_ELBO
+from pyro.infer import (SVI, JitTrace_ELBO, JitTraceEnum_ELBO, JitTraceGraph_ELBO, Trace_ELBO, TraceEnum_ELBO,
+                        TraceGraph_ELBO)
 from pyro.optim import Adam
-from tests.common import assert_equal
+from tests.common import assert_equal, xfail_param
 
 logger = logging.getLogger(__name__)
 
@@ -112,7 +113,17 @@ def guide():
 
 @pytest.mark.parametrize("reparameterized", [True, False], ids=["reparam", "nonreparam"])
 @pytest.mark.parametrize("subsample", [False, True], ids=["full", "subsample"])
-@pytest.mark.parametrize("Elbo", [Trace_ELBO, TraceGraph_ELBO, TraceEnum_ELBO])
+@pytest.mark.parametrize("Elbo", [
+    Trace_ELBO,
+    TraceGraph_ELBO,
+    TraceEnum_ELBO,
+    xfail_param(JitTrace_ELBO,
+                reason="jit RuntimeError: Unsupported op descriptor: index-2"),
+    xfail_param(JitTraceGraph_ELBO,
+                reason="jit RuntimeError: Unsupported op descriptor: index-2"),
+    xfail_param(JitTraceEnum_ELBO,
+                reason="jit RuntimeError: Unsupported op descriptor: index-2"),
+])
 def test_subsample_gradient_sequential(Elbo, reparameterized, subsample):
     pyro.clear_param_store()
     data = torch.tensor([-0.5, 2.0])
@@ -134,11 +145,15 @@ def guide():
             pyro.sample("z", Normal(loc[ind], scale))
 
     optim = Adam({"lr": 0.1})
-    elbo = Elbo(num_particles=num_particles, strict_enumeration_warning=False)
-    inference = SVI(model, guide, optim, loss=elbo)
-    inference.loss_and_grads(model, guide)
+    elbo = Elbo(num_particles=10, strict_enumeration_warning=False)
+    inference = SVI(model, guide, optim, elbo)
+    iters = num_particles // 10
+    for _ in range(iters):
+        inference.loss_and_grads(model, guide)
+
     params = dict(pyro.get_param_store().named_parameters())
-    actual_grads = {name: param.grad.detach().cpu().numpy() for name, param in params.items()}
+    actual_grads = {name: param.grad.detach().cpu().numpy() / iters
+                    for name, param in params.items()}
 
     expected_grads = {'loc': np.array([0.5, -2.0]), 'scale': np.array([2.0])}
     for name in sorted(params):