Add pattern for MultivariateNormal(affine)

funsor/affine.py

@@ -8,6 +8,11 @@
 from funsor.torch import Tensor
 
 
+# FIXME change this to a sound but incomplete test using pattern matching.
+def is_affine(fn):
+    return True
+
+
 def extract_affine(fn):
     """
     Extracts an affine representation of a funsor, which is exact for affine

funsor/cnf.py

@@ -18,6 +18,8 @@
 class Contraction(Funsor):
     """
     Declarative representation of a finitary sum-product operation
+
+    :ivar bool is_affine: Whether this contraction is affine.
     """
     def __init__(self, red_op, bin_op, reduced_vars, terms):
         terms = (terms,) if isinstance(terms, Funsor) else terms

funsor/distributions.py

@@ -7,9 +7,10 @@
 
 import funsor.delta
 import funsor.ops as ops
+from funsor.affine import extract_affine, is_affine
 from funsor.cnf import Contraction
 from funsor.domains import bint, reals
-from funsor.gaussian import BlockMatrix, BlockVector, Gaussian, cholesky_inverse
+from funsor.gaussian import BlockMatrix, BlockVector, Gaussian
 from funsor.interpreter import interpretation
 from funsor.terms import Funsor, FunsorMeta, Number, Variable, eager, lazy, to_funsor
 from funsor.torch import Tensor, align_tensors, ignore_jit_warnings, materialize, torch_stack
@@ -449,23 +450,80 @@ def eager_mvn(loc, scale_tril, value):
 
 
 # Create a Gaussian from a ground observation.
-@eager.register(MultivariateNormal, Tensor, Tensor, Variable)
-@eager.register(MultivariateNormal, Variable, Tensor, Tensor)
+# TODO refactor this logic into Gaussian.eager_subs() and
+#   here return Gaussian(...scale_tril...)(value=loc-value).
+@eager.register(MultivariateNormal, (Variable, Contraction), Tensor, (Variable, Contraction))
+@eager.register(MultivariateNormal, (Variable, Contraction), Tensor, Tensor)
+@eager.register(MultivariateNormal, Tensor, Tensor, (Variable, Contraction))
 def eager_mvn(loc, scale_tril, value):
-    if isinstance(loc, Variable):
-        loc, value = value, loc
+    assert len(loc.shape) == 1
+    assert len(scale_tril.shape) == 2
+    assert value.output == loc.output
+    if not is_affine(loc) or not is_affine(value):
+        return None  # lazy
+
+    # Extract an affine representation.
+    eye = torch.eye(scale_tril.data.size(-1)).expand(scale_tril.data.shape)
+    prec_sqrt = Tensor(eye.triangular_solve(scale_tril.data, upper=False).solution,
+                       scale_tril.inputs)
+    affine = prec_sqrt @ (loc - value)
+    const, coeffs = extract_affine(affine)
+    if not isinstance(const, Tensor):
+        return None  # lazy
+    if not all(isinstance(coeff, Tensor) for coeff, _ in coeffs.values()):
+        return None  # lazy
+
+    # Compute log_prob using funsors.
+    scale_diag = Tensor(scale_tril.data.diagonal(dim1=-1, dim2=-2), scale_tril.inputs)
+    log_prob = (-0.5 * scale_diag.shape[0] * math.log(2 * math.pi)
+                - scale_diag.log().sum() - 0.5 * (const ** 2).sum())
+
+    # Dovetail to avoid variable name collision in einsum.
+    equations1 = [''.join(c if c in ',->' else chr(ord(c) * 2 - ord('a')) for c in eqn)
+                  for _, eqn in coeffs.values()]
+    equations2 = [''.join(c if c in ',->' else chr(ord(c) * 2 - ord('a') + 1) for c in eqn)
+                  for _, eqn in coeffs.values()]
 
-    dim, = loc.output.shape
-    inputs, (loc, scale_tril) = align_tensors(loc, scale_tril)
-    inputs.update(value.inputs)
-    int_inputs = OrderedDict((k, v) for k, v in inputs.items() if v.dtype != 'real')
+    real_inputs = OrderedDict((k, v) for k, v in affine.inputs.items() if v.dtype == 'real')
+    assert tuple(real_inputs) == tuple(coeffs)
 
-    precision = cholesky_inverse(scale_tril)
-    info_vec = precision.matmul(loc.unsqueeze(-1)).squeeze(-1)
-    log_prob = (-0.5 * dim * math.log(2 * math.pi)
-                - scale_tril.diagonal(dim1=-1, dim2=-2).log().sum(-1)
-                - 0.5 * (loc * info_vec).sum(-1))
-    return Tensor(log_prob, int_inputs) + Gaussian(info_vec, precision, inputs)
+    # Align and broadcast tensors.
+    neg_const = -const
+    tensors = [neg_const] + [coeff for coeff, _ in coeffs.values()]
+    inputs, tensors = align_tensors(*tensors, expand=True)
+    neg_const, coeffs = tensors[0], tensors[1:]
+    dim = sum(d.num_elements for d in real_inputs.values())
+    batch_shape = neg_const.shape[:-1]
+
+    info_vec = BlockVector(batch_shape + (dim,))
+    precision = BlockMatrix(batch_shape + (dim, dim))
+    offset1 = 0
+    for i1, (v1, c1) in enumerate(zip(real_inputs, coeffs)):
+        size1 = real_inputs[v1].num_elements
+        slice1 = slice(offset1, offset1 + size1)
+        inputs1, output1 = equations1[i1].split('->')
+        input11, input12 = inputs1.split(',')
+        assert input11 == input12 + output1
+        info_vec[..., slice1] = torch.einsum(
+            f'...{input11},...{output1}->...{input12}', c1, neg_const) \
+            .reshape(batch_shape + (size1,))
+        offset2 = 0
+        for i2, (v2, c2) in enumerate(zip(real_inputs, coeffs)):
+            size2 = real_inputs[v2].num_elements
+            slice2 = slice(offset2, offset2 + size2)
+            inputs2, output2 = equations2[i2].split('->')
+            input21, input22 = inputs2.split(',')
+            assert input21 == input22 + output2
+            precision[..., slice1, slice2] = torch.einsum(
+                f'...{input11},...{input22}{output1}->...{input12}{input22}', c1, c2) \
+                .reshape(batch_shape + (size1, size2))
+            offset2 += size2
+        offset1 += size1
+
+    info_vec = info_vec.as_tensor()
+    precision = precision.as_tensor()
+    inputs.update(real_inputs)
+    return log_prob + Gaussian(info_vec, precision, inputs)
 
 
 class Poisson(Distribution):

funsor/ops.py

@@ -53,6 +53,7 @@ def log_abs_det_jacobian(x, y):
         raise NotImplementedError
 
 
+# FIXME Most code assumes this is an AssociativeCommutativeOp.
 class AssociativeOp(Op):
     pass
 
@@ -65,7 +66,7 @@ class MulOp(AssociativeOp):
     pass
 
 
-class MatmulOp(AssociativeOp):
+class MatmulOp(Op):  # Associtive but not commutative.
     pass
 
 

funsor/torch.py

@@ -470,10 +470,6 @@ def eager_binary_tensor_tensor(op, lhs, rhs):
             shape = shape[:cut] + (1,) * (lhs_dim - rhs_dim) + shape[cut:]
             rhs_data = rhs_data.reshape(shape)
 
-    print(f"lhs.data.shape = {lhs.data.shape}")
-    print(f"rhs.data.shape = {rhs.data.shape}")
-    print(f"lhs_data.shape = {lhs_data.shape}")
-    print(f"rhs_data.shape = {rhs_data.shape}")
     data = op(lhs_data, rhs_data)
     if len(lhs.shape) == 1:
         data = data.squeeze(-2)

test/examples/test_sensor_fusion.py

@@ -3,6 +3,7 @@
 import pytest
 import torch
 
+import funsor.distributions as dist
 import funsor.ops as ops
 from funsor.cnf import Contraction
 from funsor.domains import bint, reals
@@ -13,8 +14,49 @@
 from funsor.torch import Tensor
 
 
+# This version constructs factors using funsor.distributions.
+@pytest.mark.parametrize('state_dim,obs_dim', [(3, 2), (2, 3)])
+def test_distributions(state_dim, obs_dim):
+    data = Tensor(torch.randn(2, obs_dim))["time"]
+
+    bias = Variable("bias", reals(obs_dim))
+    bias_dist = dist_to_funsor(random_mvn((), obs_dim))(value=bias)
+
+    prev = Variable("prev", reals(state_dim))
+    curr = Variable("curr", reals(state_dim))
+    trans_mat = Tensor(torch.eye(state_dim) + 0.1 * torch.randn(state_dim, state_dim))
+    trans_mvn = random_mvn((), state_dim)
+    trans_dist = dist.MultivariateNormal(
+        loc=trans_mvn.loc,
+        scale_tril=trans_mvn.scale_tril,
+        value=curr - prev @ trans_mat)
+
+    state = Variable("state", reals(state_dim))
+    obs = Variable("obs", reals(obs_dim))
+    obs_mat = Tensor(torch.randn(state_dim, obs_dim))
+    obs_mvn = random_mvn((), obs_dim)
+    obs_dist = dist.MultivariateNormal(
+        loc=obs_mvn.loc,
+        scale_tril=obs_mvn.scale_tril,
+        value=state @ obs_mat + bias - obs)
+
+    log_prob = 0
+    log_prob += bias_dist
+
+    state_0 = Variable("state_0", reals(state_dim))
+    log_prob += obs_dist(state=state_0, obs=data(time=0))
+
+    state_1 = Variable("state_1", reals(state_dim))
+    log_prob += trans_dist(prev=state_0, curr=state_1)
+    log_prob += obs_dist(state=state_1, obs=data(time=1))
+
+    log_prob = log_prob.reduce(ops.logaddexp)
+    assert isinstance(log_prob, Tensor), log_prob.pretty()
+
+
+# This version constructs factors using funsor.pyro.convert.
 @pytest.mark.xfail(reason="missing pattern")
-def test_end_to_end():
+def test_pyro_convert():
     data = Tensor(torch.randn(2, 2), OrderedDict([("time", bint(2))]))
 
     bias_dist = dist_to_funsor(random_mvn((), 2))
@@ -44,7 +86,7 @@ def test_end_to_end():
 
 @pytest.mark.xfail(reason="missing pattern")
 def test_affine_subs():
-    # This was recorded from test_end_to_end.
+    # This was recorded from test_pyro_convert.
     x = Subs(
      Gaussian(
       torch.tensor([1.3027106523513794, 1.4167094230651855, -0.9750942587852478, 0.5321089029312134, -0.9039931297302246], dtype=torch.float32),  # noqa

test/test_affine.py

@@ -80,6 +80,7 @@ def test_affine_subs(expr, expected_type, expected_inputs):
 
 @pytest.mark.parametrize('expr', [
     "Variable('x', reals()) + 0.5",
+    "Variable('x', reals(2, 3)) + Variable('y', reals(2, 3))",
     "Variable('x', reals(2)) + Variable('y', reals(2))",
     "Variable('x', reals(2)) + torch.ones(2)",
     "Variable('x', reals(2)) * torch.randn(2)",

test/test_distributions.py

@@ -7,12 +7,14 @@
 
 import funsor
 import funsor.distributions as dist
-from funsor.cnf import Contraction
+from funsor.cnf import Contraction, GaussianMixture
 from funsor.delta import Delta
 from funsor.domains import bint, reals
-from funsor.terms import Independent, Variable
-from funsor.testing import assert_close, check_funsor, random_tensor
-from funsor.torch import Tensor
+from funsor.interpreter import interpretation, reinterpret
+from funsor.pyro.convert import dist_to_funsor
+from funsor.terms import Independent, Variable, lazy
+from funsor.testing import assert_close, check_funsor, random_mvn, random_tensor
+from funsor.torch import Einsum, Tensor
 
 
 @pytest.mark.parametrize('batch_shape', [(), (5,), (2, 3)], ids=str)
@@ -398,7 +400,6 @@ def test_normal_gaussian_3(batch_shape):
     'dist.Normal(x - y, scale, 0)',
     'dist.Normal(0, scale, y - x)',
     'dist.Normal(2 * x - y, scale, x)',
-    # TODO should we expect these to work without correction terms?
     'dist.Normal(0, 1, (x - y) / scale) - scale.log()',
     'dist.Normal(2 * y, 2 * scale, 2 * x) + math.log(2)',
 ]
@@ -488,6 +489,75 @@ def test_mvn_gaussian(batch_shape):
     assert_close(actual, expected, atol=1e-3, rtol=1e-4)
 
 
+def _check_mvn_affine(d1, data):
+    assert isinstance(d1, dist.MultivariateNormal)
+    d2 = reinterpret(d1)
+    assert issubclass(type(d2), GaussianMixture)
+    actual = d2(**data)
+    expected = d1(**data)
+    assert_close(actual, expected)
+
+
+def test_mvn_affine_one_var():
+    x = Variable('x', reals(2))
+    data = dict(x=Tensor(torch.randn(2)))
+    with interpretation(lazy):
+        d = dist_to_funsor(random_mvn((), 2))
+        d = d(value=2 * x + 1)
+    _check_mvn_affine(d, data)
+
+
+def test_mvn_affine_two_vars():
+    x = Variable('x', reals(2))
+    y = Variable('y', reals(2))
+    data = dict(x=Tensor(torch.randn(2)), y=Tensor(torch.randn(2)))
+    with interpretation(lazy):
+        d = dist_to_funsor(random_mvn((), 2))
+        d = d(value=x - y)
+    _check_mvn_affine(d, data)
+
+
+def test_mvn_affine_matmul():
+    x = Variable('x', reals(2))
+    y = Variable('y', reals(3))
+    m = Tensor(torch.randn(2, 3))
+    data = dict(x=Tensor(torch.randn(2)), y=Tensor(torch.randn(3)))
+    with interpretation(lazy):
+        d = dist_to_funsor(random_mvn((), 3))
+        d = d(value=x @ m - y)
+    _check_mvn_affine(d, data)
+
+
+def test_mvn_affine_einsum():
+    c = Tensor(torch.randn(3, 2, 2))
+    x = Variable('x', reals(2, 2))
+    y = Variable('y', reals())
+    data = dict(x=Tensor(torch.randn(2, 2)), y=Tensor(torch.randn(())))
+    with interpretation(lazy):
+        d = dist_to_funsor(random_mvn((), 3))
+        d = d(value=Einsum("abc,bc->a", c, x) + y)
+    _check_mvn_affine(d, data)
+
+
+def test_mvn_affine_getitem():
+    x = Variable('x', reals(2, 2))
+    data = dict(x=Tensor(torch.randn(2, 2)))
+    with interpretation(lazy):
+        d = dist_to_funsor(random_mvn((), 2))
+        d = d(value=x[0] - x[1])
+    _check_mvn_affine(d, data)
+
+
+def test_mvn_affine_reshape():
+    x = Variable('x', reals(2, 2))
+    y = Variable('y', reals(4))
+    data = dict(x=Tensor(torch.randn(2, 2)), y=Tensor(torch.randn(4)))
+    with interpretation(lazy):
+        d = dist_to_funsor(random_mvn((), 4))
+        d = d(value=x.reshape((4,)) - y)
+    _check_mvn_affine(d, data)
+
+
 @pytest.mark.parametrize('batch_shape', [(), (5,), (2, 3)], ids=str)
 @pytest.mark.parametrize('syntax', ['eager', 'lazy'])
 def test_poisson_probs_density(batch_shape, syntax):