Fix interaction between PyroParam and torch.func.grad

pyro/nn/module.py

@@ -21,6 +21,7 @@
 from torch.distributions import constraints, transform_to
 
 import pyro
+import pyro.params.param_store
 from pyro.ops.provenance import detach_provenance
 from pyro.poutine.runtime import _PYRO_PARAM_STORE
 
@@ -474,7 +475,7 @@ def __getattr__(self, name):
             if name in _pyro_params:
                 constraint, event_dim = _pyro_params[name]
                 unconstrained_value = getattr(self, name + "_unconstrained")
-                if self._pyro_context.active:
+                if self._pyro_context.active and not _is_module_local_param_enabled():
                     fullname = self._pyro_get_fullname(name)
                     if fullname in _PYRO_PARAM_STORE:
                         if (
@@ -503,6 +504,15 @@ def __getattr__(self, name):
                         _PYRO_PARAM_STORE._params[fullname] = unconstrained_value
                         _PYRO_PARAM_STORE._param_to_name[unconstrained_value] = fullname
                     return pyro.param(fullname, event_dim=event_dim)
+                elif self._pyro_context.active and _is_module_local_param_enabled():
+                    # fake param statement to ensure any handlers of pyro.param are applied,
+                    # even though we don't use the contents of the local parameter store
+                    fullname = self._pyro_get_fullname(name)
+                    constrained_value = transform_to(constraint)(unconstrained_value)
+                    constrained_value.unconstrained = weakref.ref(unconstrained_value)
+                    return pyro.poutine.runtime.effectful(type="param")(
+                        lambda *_, **__: constrained_value
+                    )(fullname, event_dim=event_dim, name=fullname)
                 else:  # Cannot determine supermodule and hence cannot compute fullname.
                     constrained_value = transform_to(constraint)(unconstrained_value)
                     constrained_value.unconstrained = weakref.ref(unconstrained_value)
@@ -534,8 +544,15 @@ def __getattr__(self, name):
         if isinstance(result, torch.nn.Parameter) and not name.endswith(
             "_unconstrained"
         ):
-            if self._pyro_context.active:
+            if self._pyro_context.active and not _is_module_local_param_enabled():
                 pyro.param(self._pyro_get_fullname(name), result)
+            elif self._pyro_context.active and _is_module_local_param_enabled():
+                # fake param statement to ensure any handlers of pyro.param are applied,
+                # even though we don't use the contents of the local parameter store
+                fullname = self._pyro_get_fullname(name)
+                pyro.poutine.runtime.effectful(type="param")(lambda *_, **__: result)(
+                    fullname, result, name=fullname
+                )
 
         if isinstance(result, torch.nn.Module):
             if isinstance(result, PyroModule):
@@ -546,8 +563,19 @@ def __getattr__(self, name):
                     )
             else:
                 # Regular nn.Modules trigger pyro.module statements.
-                if self._pyro_context.active:
+                if self._pyro_context.active and not _is_module_local_param_enabled():
                     pyro.module(self._pyro_get_fullname(name), result)
+                elif self._pyro_context.active and _is_module_local_param_enabled():
+                    # fake module statement to ensure any handlers of pyro.module are applied,
+                    # even though we don't use the contents of the local parameter store
+                    fullname_module = self._pyro_get_fullname(name)
+                    for param_name, param_value in result.named_parameters():
+                        fullname_param = pyro.params.param_store.param_with_module_name(
+                            fullname_module, param_name
+                        )
+                        pyro.poutine.runtime.effectful(type="param")(
+                            lambda *_, **__: param_value
+                        )(fullname_param, param_value, name=fullname_param)
 
         return result
 
@@ -569,7 +597,7 @@ def __setattr__(self, name, value):
                 pass
             constrained_value, constraint, event_dim = value
             self._pyro_params[name] = constraint, event_dim
-            if self._pyro_context.active:
+            if self._pyro_context.active and not _is_module_local_param_enabled():
                 fullname = self._pyro_get_fullname(name)
                 pyro.param(
                     fullname,
@@ -584,6 +612,21 @@ def __setattr__(self, name, value):
                     unconstrained_value = torch.nn.Parameter(unconstrained_value)
                     _PYRO_PARAM_STORE._params[fullname] = unconstrained_value
                     _PYRO_PARAM_STORE._param_to_name[unconstrained_value] = fullname
+            elif self._pyro_context.active and _is_module_local_param_enabled():
+                # fake param statement to ensure any handlers of pyro.param are applied,
+                # even though we don't use the contents of the local parameter store
+                fullname = self._pyro_get_fullname(name)
+                constrained_value = detach_provenance(
+                    pyro.poutine.runtime.effectful(type="param")(
+                        lambda *_, **__: constrained_value
+                    )(
+                        fullname,
+                        constraint=constraint,
+                        event_dim=event_dim,
+                        name=fullname,
+                    )
+                )
+                unconstrained_value = _unconstrain(constrained_value, constraint)
             else:  # Cannot determine supermodule and hence cannot compute fullname.
                 unconstrained_value = _unconstrain(constrained_value, constraint)
             super().__setattr__(name + "_unconstrained", unconstrained_value)
@@ -595,14 +638,23 @@ def __setattr__(self, name, value):
                 delattr(self, name)
             except AttributeError:
                 pass
-            if self._pyro_context.active:
+            if self._pyro_context.active and not _is_module_local_param_enabled():
                 fullname = self._pyro_get_fullname(name)
                 value = pyro.param(fullname, value)
                 if not isinstance(value, torch.nn.Parameter):
                     # Update PyroModule ---> ParamStore (type only; data is preserved).
                     value = torch.nn.Parameter(detach_provenance(value))
                     _PYRO_PARAM_STORE._params[fullname] = value
                     _PYRO_PARAM_STORE._param_to_name[value] = fullname
+            elif self._pyro_context.active and _is_module_local_param_enabled():
+                # fake param statement to ensure any handlers of pyro.param are applied,
+                # even though we don't use the contents of the local parameter store
+                fullname = self._pyro_get_fullname(name)
+                value = detach_provenance(
+                    pyro.poutine.runtime.effectful(type="param")(
+                        lambda *_, **__: value
+                    )(fullname, value, constraint=constraints.real, name=fullname)
+                )
             super().__setattr__(name, value)
             return
 

tests/nn/test_module.py

@@ -15,7 +15,7 @@
 from pyro.infer import SVI, Trace_ELBO
 from pyro.nn.module import PyroModule, PyroParam, PyroSample, clear, to_pyro_module_
 from pyro.optim import Adam
-from tests.common import assert_equal
+from tests.common import assert_equal, xfail_param
 
 
 def test_svi_smoke():
@@ -765,3 +765,82 @@ def test_bayesian_gru():
     assert output.shape == (seq_len, batch_size, hidden_size)
     output2, _ = gru(input_)
     assert not torch.allclose(output2, output)
+
+
+@pytest.mark.parametrize(
+    "use_local_params",
+    [
+        True,
+        xfail_param(
+            False, reason="torch.func not compatible with global parameter store"
+        ),
+    ],
+)
+def test_functorch_pyroparam(use_local_params):
+    class ParamModule(PyroModule):
+        def __init__(self):
+            super().__init__()
+            self.a2 = PyroParam(torch.tensor(0.678), constraints.positive)
+
+        @PyroParam(constraint=constraints.real)
+        def a1(self):
+            return torch.tensor(0.456)
+
+    class Model(PyroModule):
+        def __init__(self):
+            super().__init__()
+            self.param_module = ParamModule()
+            self.b1 = PyroParam(torch.tensor(0.123), constraints.positive)
+            self.b3 = torch.nn.Parameter(torch.tensor(0.789))
+            self.c = torch.nn.Linear(1, 1)
+
+        @PyroParam(constraint=constraints.positive)
+        def b2(self):
+            return torch.tensor(1.234)
+
+        def forward(self, x, y):
+            return (
+                (self.param_module.a1 + self.param_module.a2) * x
+                + self.b1
+                + self.b2
+                + self.b3
+                - self.c(y.unsqueeze(-1)).squeeze(-1)
+            ) ** 2
+
+    with pyro.settings.context(module_local_params=use_local_params):
+        model = Model()
+        x, y = torch.tensor(1.3), torch.tensor(0.2)
+
+        with pyro.poutine.trace() as tr:
+            model(x, y)
+
+        params = dict(model.named_parameters())
+
+        # Check that all parameters appear in the trace for SVI compatibility
+        assert len(params) == len(
+            {
+                name: node
+                for name, node in tr.trace.nodes.items()
+                if node["type"] == "param"
+            }
+        )
+
+        grad_model = torch.func.grad(
+            lambda p, x, y: torch.func.functional_call(model, p, (x, y))
+        )
+        grad_params_func = grad_model(params, x, y)
+
+        gs = torch.autograd.grad(model(x, y), tuple(params.values()))
+        grad_params_autograd = dict(zip(params.keys(), gs))
+
+        assert len(grad_params_autograd) == len(grad_params_func) != 0
+        assert (
+            set(grad_params_autograd.keys())
+            == set(grad_params_func.keys())
+            == set(params.keys())
+        )
+        for k in grad_params_autograd.keys():
+            assert not torch.allclose(
+                grad_params_func[k], torch.zeros_like(grad_params_func[k])
+            ), k
+            assert torch.allclose(grad_params_autograd[k], grad_params_func[k]), k