Support conversion of Transforms and TransformedDistributions to and from Funsors

funsor/cnf.py

@@ -10,6 +10,7 @@
 import opt_einsum
 from multipledispatch.variadic import Variadic
 
+import funsor
 import funsor.ops as ops
 from funsor.affine import affine_inputs
 from funsor.delta import Delta
@@ -127,6 +128,14 @@ def unscaled_sample(self, sampled_vars, sample_inputs, rng_key=None):
                     terms.append(-gaussian.log_normalizer)
                     terms.append(term.unscaled_sample(greedy_vars, sample_inputs, rng_keys[0]))
                     result = Contraction(self.red_op, self.bin_op, self.reduced_vars, *terms)
+                elif any(isinstance(term, funsor.distribution.Distribution)
+                         and greedy_vars.intersection(term.value.inputs) for term in greedy_terms):
+                    sampled_terms = [
+                        term.unscaled_sample(greedy_vars.intersection(term.value.inputs), sample_inputs)
+                        for term in greedy_terms if isinstance(term, funsor.distribution.Distribution)
+                        and greedy_vars.intersection(term.value.inputs)
+                    ]
+                    result = Contraction(self.red_op, self.bin_op, self.reduced_vars, *(terms + sampled_terms))
                 else:
                     raise NotImplementedError('Unhandled case: {}'.format(
                         ', '.join(str(type(t)) for t in greedy_terms)))

funsor/distribution.py

@@ -21,7 +21,8 @@
 from funsor.interpreter import gensym
 from funsor.tensor import (Tensor, align_tensors, dummy_numeric_array, get_default_prototype,
                            ignore_jit_warnings, numeric_array, stack)
-from funsor.terms import Funsor, FunsorMeta, Independent, Number, Variable, eager, to_data, to_funsor
+from funsor.terms import Funsor, FunsorMeta, Independent, Number, Variable, \
+    eager, to_data, to_funsor
 from funsor.util import broadcast_shape, get_backend, getargspec, lazy_property
 
 
@@ -109,17 +110,16 @@ def _get_raw_dist(self):
         """
         Internal method for working with underlying distribution attributes
         """
-        if isinstance(self.value, Variable):
-            value_name = self.value.name
-        else:
-            raise NotImplementedError("cannot get raw dist for {}".format(self))
+        value_name = [name for name, domain in self.value.inputs.items()  # TODO is this right?
+                      if domain == self.value.output][0]
         # arbitrary name-dim mapping, since we're converting back to a funsor anyway
         name_to_dim = {name: -dim-1 for dim, (name, domain) in enumerate(self.inputs.items())
                        if isinstance(domain.dtype, int) and name != value_name}
         raw_dist = to_data(self, name_to_dim=name_to_dim)
         dim_to_name = {dim: name for name, dim in name_to_dim.items()}
         # also return value output, dim_to_name for converting results back to funsor
-        return raw_dist, self.value.output, dim_to_name
+        value_output = self.inputs[value_name]
+        return raw_dist, value_name, value_output, dim_to_name
 
     @property
     def has_rsample(self):
@@ -130,16 +130,15 @@ def has_enumerate_support(self):
         return getattr(self.dist_class, "has_enumerate_support", False)
 
     def unscaled_sample(self, sampled_vars, sample_inputs, rng_key=None):
-        params = OrderedDict(self.params)
-        value = params.pop("value")
-        assert all(isinstance(v, (Number, Tensor)) for v in params.values())
-        assert isinstance(value, Variable) and value.name in sampled_vars
 
-        value_name = value.name
-        raw_dist, value_output, dim_to_name = self._get_raw_dist()
+        # note this should handle transforms correctly via distribution_to_data
+        raw_dist, value_name, value_output, dim_to_name = self._get_raw_dist()
         for d, name in zip(range(len(sample_inputs), 0, -1), sample_inputs.keys()):
             dim_to_name[-d - len(raw_dist.batch_shape)] = name
 
+        if value_name not in sampled_vars:
+            return self
+
         sample_shape = tuple(v.size for v in sample_inputs.values())
         sample_args = (sample_shape,) if get_backend() == "torch" else (rng_key, sample_shape)
         if self.has_rsample:
@@ -161,23 +160,23 @@ def unscaled_sample(self, sampled_vars, sample_inputs, rng_key=None):
 
     def enumerate_support(self, expand=False):
         assert self.has_enumerate_support and isinstance(self.value, Variable)
-        raw_dist, value_output, dim_to_name = self._get_raw_dist()
+        raw_dist, value_name, value_output, dim_to_name = self._get_raw_dist()
         raw_value = raw_dist.enumerate_support(expand=expand)
-        dim_to_name[min(dim_to_name.keys(), default=0)-1] = self.value.name
+        dim_to_name[min(dim_to_name.keys(), default=0)-1] = value_name
         return to_funsor(raw_value, output=value_output, dim_to_name=dim_to_name)
 
     def entropy(self):
-        raw_dist, value_output, dim_to_name = self._get_raw_dist()
+        raw_dist, value_name, value_output, dim_to_name = self._get_raw_dist()
         raw_value = raw_dist.entropy()
         return to_funsor(raw_value, output=self.output, dim_to_name=dim_to_name)
 
     def mean(self):
-        raw_dist, value_output, dim_to_name = self._get_raw_dist()
+        raw_dist, value_name, value_output, dim_to_name = self._get_raw_dist()
         raw_value = raw_dist.mean
         return to_funsor(raw_value, output=value_output, dim_to_name=dim_to_name)
 
     def variance(self):
-        raw_dist, value_output, dim_to_name = self._get_raw_dist()
+        raw_dist, value_name, value_output, dim_to_name = self._get_raw_dist()
         raw_value = raw_dist.variance
         return to_funsor(raw_value, output=value_output, dim_to_name=dim_to_name)
 
@@ -234,7 +233,6 @@ def _infer_param_domain(cls, name, raw_shape):
 # Distribution Wrappers
 ################################################################################
 
-
 def make_dist(backend_dist_class, param_names=(), generate_eager=True, generate_to_funsor=True):
     if not param_names:
         param_names = tuple(name for name in inspect.getfullargspec(backend_dist_class.__init__)[0][1:]
@@ -312,8 +310,19 @@ def maskeddist_to_funsor(backend_dist, output=None, dim_to_name=None):
     return mask * funsor_base_dist
 
 
+# converts TransformedDistributions
 def transformeddist_to_funsor(backend_dist, output=None, dim_to_name=None):
-    raise NotImplementedError("TODO implement conversion of TransformedDistribution")
+    dist_module = import_module(BACKEND_TO_DISTRIBUTIONS_BACKEND[get_backend()]).dist
+    base_dist, transforms = backend_dist, []
+    while isinstance(base_dist, dist_module.TransformedDistribution):
+        transforms = base_dist.transforms + transforms
+        base_dist = base_dist.base_dist
+    funsor_base_dist = to_funsor(base_dist, output=output, dim_to_name=dim_to_name)
+    # TODO make this work with transforms that change the output type
+    transform = to_funsor(dist_module.transforms.ComposeTransform(transforms),
+                          funsor_base_dist.inputs["value"], dim_to_name)
+    _, inv_transform, ldj = funsor.delta.solve(transform, to_funsor("value", funsor_base_dist.inputs["value"]))
+    return -ldj + funsor_base_dist(value=inv_transform)
 
 
 class CoerceDistributionToFunsor:
@@ -396,6 +405,17 @@ def distribution_to_data(funsor_dist, name_to_dim=None):
     pyro_dist = funsor_dist.dist_class(**dict(zip(funsor_dist._ast_fields[:-1], params)))
     funsor_event_shape = funsor_dist.value.output.shape
     pyro_dist = pyro_dist.to_event(max(len(funsor_event_shape) - len(pyro_dist.event_shape), 0))
+
+    # TODO get this working for all backends
+    if not isinstance(funsor_dist.value, Variable):
+        if get_backend() != "torch":
+            raise NotImplementedError("transformed distributions not yet supported under this backend,"
+                                      "try set_backend('torch')")
+        inv_value = funsor.delta.solve(funsor_dist.value, Variable("value", funsor_dist.value.output))[1]
+        transforms = to_data(inv_value, name_to_dim=name_to_dim)
+        backend_dist = import_module(BACKEND_TO_DISTRIBUTIONS_BACKEND[get_backend()]).dist
+        pyro_dist = backend_dist.TransformedDistribution(pyro_dist, transforms)
+
     if pyro_dist.event_shape != funsor_event_shape:
         raise ValueError("Event shapes don't match, something went wrong")
     return pyro_dist

funsor/torch/distributions.py

@@ -41,7 +41,7 @@
 from funsor.domains import Real, Reals
 import funsor.ops as ops
 from funsor.tensor import Tensor, dummy_numeric_array
-from funsor.terms import Binary, Funsor, Variable, eager, to_data, to_funsor
+from funsor.terms import Binary, Funsor, Unary, Variable, eager, to_data, to_funsor
 from funsor.util import methodof
 
 
@@ -221,10 +221,94 @@ def deltadist_to_data(funsor_dist, name_to_dim=None):
     return dist.Delta(v, log_density, event_dim=len(funsor_dist.v.output.shape))
 
 
+@functools.singledispatch
+def op_to_torch_transform(op, name_to_dim=None):
+    raise NotImplementedError("cannot convert {} to a Transform".format(op))
+
+
+@op_to_torch_transform.register(ops.TransformOp)
+def transform_to_torch_transform(op, name_to_dim=None):
+    raise NotImplementedError("{} is not a currently supported transform".format(op))
+
+
+@op_to_torch_transform.register(ops.ExpOp)
+def exp_to_torch_transform(op, name_to_dim=None):
+    return torch.distributions.transforms.ExpTransform()
+
+
+@op_to_torch_transform.register(ops.LogOp)
+def log_to_torch_transform(op, name_to_dim=None):
+    return torch.distributions.transforms.ExpTransform().inv
+
+
+@op_to_torch_transform.register(ops.SigmoidOp)
+def sigmoid_to_torch_transform(op, name_to_dim=None):
+    return torch.distributions.transforms.SigmoidTransform()
+
+
+@op_to_torch_transform.register(ops.TanhOp)
+def tanh_to_torch_transform(op, name_to_dim=None):
+    return torch.distributions.transforms.TanhTransform()
+
+
+@op_to_torch_transform.register(ops.AtanhOp)
+def atanh_to_torch_transform(op, name_to_dim=None):
+    return torch.distributions.transforms.TanhTransform().inv
+
+
+@to_data.register(Unary[ops.TransformOp, Union[Unary, Variable]])
+def transform_to_data(expr, name_to_dim=None):
+    if isinstance(expr.op, ops.TransformOp):
+        tfm = op_to_torch_transform(expr.op, name_to_dim=name_to_dim)
+        if isinstance(expr.arg, Unary):
+            tfm = torch.distributions.transforms.ComposeTransform([to_data(expr.arg, name_to_dim=name_to_dim), tfm])
+        return tfm
+    raise NotImplementedError("cannot convert to data: {}".format(expr))
+
+
 ###############################################
 # Converting PyTorch Distributions to funsors
 ###############################################
 
+@to_funsor.register(torch.distributions.Transform)
+def transform_to_funsor(tfm, output=None, dim_to_name=None, real_inputs=None):
+    raise NotImplementedError("{} is not a currently supported transform".format(tfm))
+
+
+@to_funsor.register(torch.distributions.transforms.ExpTransform)
+def exptransform_to_funsor(tfm, output=None, dim_to_name=None, real_inputs=None):
+    name = next(real_inputs.keys()) if real_inputs else "value"
+    return ops.exp(Variable(name, output))
+
+
+@to_funsor.register(torch.distributions.transforms.TanhTransform)
+def exptransform_to_funsor(tfm, output=None, dim_to_name=None, real_inputs=None):
+    name = next(real_inputs.keys()) if real_inputs else "value"
+    return ops.tanh(Variable(name, output))
+
+
+@to_funsor.register(torch.distributions.transforms.SigmoidTransform)
+def exptransform_to_funsor(tfm, output=None, dim_to_name=None, real_inputs=None):
+    name = next(real_inputs.keys()) if real_inputs else "value"
+    return ops.sigmoid(Variable(name, output))
+
+
+@to_funsor.register(torch.distributions.transforms._InverseTransform)
+def inversetransform_to_funsor(tfm, output=None, dim_to_name=None, real_inputs=None):
+    expr = to_funsor(tfm._inv, output=output, dim_to_name=dim_to_name, real_inputs=real_inputs)
+    assert isinstance(expr, Unary)
+    return expr.op.inv(expr.arg)
+
+
+@to_funsor.register(torch.distributions.transforms.ComposeTransform)
+def composetransform_to_funsor(tfm, output=None, dim_to_name=None, real_inputs=None):
+    name = next(real_inputs.keys()) if real_inputs else "value"
+    expr = Variable(name, output)
+    for part in tfm.parts:
+        expr = to_funsor(part, output=output, dim_to_name=dim_to_name, real_inputs=real_inputs)(**{name: expr})
+    return expr
+
+
 to_funsor.register(torch.distributions.Independent)(indepdist_to_funsor)
 to_funsor.register(MaskedDistribution)(maskeddist_to_funsor)
 to_funsor.register(torch.distributions.TransformedDistribution)(transformeddist_to_funsor)