Add plated einsum implementation

funsor/__init__.py

@@ -3,9 +3,9 @@
 from funsor.domains import Domain, bint, find_domain, reals
 from funsor.interpreter import reinterpret
 from funsor.terms import Funsor, Number, Variable, of_shape, to_funsor
-from funsor.torch import Function, Tensor, arange, einsum, function
+from funsor.torch import Function, Tensor, arange, torch_einsum, function
 
-from . import distributions, domains, gaussian, handlers, interpreter, minipyro, ops, terms, torch
+from . import distributions, domains, einsum, gaussian, handlers, interpreter, minipyro, ops, terms, torch
 
 __all__ = [
     'Domain',
@@ -33,4 +33,5 @@
     'terms',
     'to_funsor',
     'torch',
+    'torch_einsum',
 ]

funsor/einsum.py

@@ -0,0 +1,133 @@
+from __future__ import absolute_import, division, print_function
+
+from collections import defaultdict, OrderedDict
+from six.moves import reduce
+
+import funsor.ops as ops
+from funsor.interpreter import interpretation, reinterpret
+from funsor.optimizer import apply_optimizer
+from funsor.terms import Funsor, reflect
+
+
+def naive_einsum(eqn, *terms, **kwargs):
+    backend = kwargs.pop('backend', 'torch')
+    if backend == 'torch':
+        sum_op, prod_op = ops.add, ops.mul
+    elif backend == 'pyro.ops.einsum.torch_log':
+        sum_op, prod_op = ops.logaddexp, ops.add
+    else:
+        raise ValueError("{} backend not implemented".format(backend))
+
+    assert isinstance(eqn, str)
+    assert all(isinstance(term, Funsor) for term in terms)
+    inputs, output = eqn.split('->')
+    assert len(output.split(',')) == 1
+    input_dims = frozenset(d for inp in inputs.split(',') for d in inp)
+    output_dims = frozenset(output)
+    reduce_dims = input_dims - output_dims
+    return reduce(prod_op, terms).reduce(sum_op, reduce_dims)
+
+
+def _partition(terms, sum_vars):
+    # Construct a bipartite graph between terms and the vars
+    neighbors = OrderedDict([(t, []) for t in terms])
+    for term in terms:
+        for dim in term.inputs.keys():
+            if dim in sum_vars:
+                neighbors[term].append(dim)
+                neighbors.setdefault(dim, []).append(term)
+
+    # Partition the bipartite graph into connected components for contraction.
+    components = []
+    while neighbors:
+        v, pending = neighbors.popitem()
+        component = OrderedDict([(v, None)])  # used as an OrderedSet
+        for v in pending:
+            component[v] = None
+        while pending:
+            v = pending.pop()
+            for v in neighbors.pop(v):
+                if v not in component:
+                    component[v] = None
+                    pending.append(v)
+
+        # Split this connected component into tensors and dims.
+        component_terms = tuple(v for v in component if isinstance(v, Funsor))
+        if component_terms:
+            component_dims = frozenset(v for v in component if not isinstance(v, Funsor))
+            components.append((component_terms, component_dims))
+    return components
+
+
+def naive_plated_einsum(eqn, *terms, **kwargs):
+    """
+    Implements Tensor Variable Elimination (Algorithm 1 in [Obermeyer et al 2019])
+
+    [Obermeyer et al 2019] Obermeyer, F., Bingham, E., Jankowiak, M., Chiu, J.,
+        Pradhan, N., Rush, A., and Goodman, N.  Tensor Variable Elimination for
+        Plated Factor Graphs, 2019
+    """
+    plates = kwargs.pop('plates', '')
+    if not plates:
+        return naive_einsum(eqn, *terms, **kwargs)
+
+    backend = kwargs.pop('backend', 'torch')
+    if backend == 'torch':
+        sum_op, prod_op = ops.add, ops.mul
+    elif backend == 'pyro.ops.einsum.torch_log':
+        sum_op, prod_op = ops.logaddexp, ops.add
+    else:
+        raise ValueError("{} backend not implemented".format(backend))
+
+    assert isinstance(eqn, str)
+    assert all(isinstance(term, Funsor) for term in terms)
+    inputs, output = eqn.split('->')
+    assert len(output.split(',')) == 1
+    input_dims = frozenset(d for inp in inputs.split(',') for d in inp)
+    output_dims = frozenset(d for d in output)
+    plate_dims = frozenset(plates) - output_dims
+    reduce_vars = input_dims - output_dims - frozenset(plates)
+
+    if output_dims:
+        raise NotImplementedError("TODO")
+
+    var_tree = {}
+    term_tree = defaultdict(list)
+    for term in terms:
+        ordinal = frozenset(term.inputs) & plate_dims
+        term_tree[ordinal].append(term)
+        for var in term.inputs:
+            if var not in plate_dims:
+                var_tree[var] = var_tree.get(var, ordinal) & ordinal
+
+    ordinal_to_var = defaultdict(set)
+    for var, ordinal in var_tree.items():
+        ordinal_to_var[ordinal].add(var)
+
+    # Direct translation of Algorithm 1
+    scalars = []
+    while term_tree:
+        leaf = max(term_tree, key=len)
+        leaf_terms = term_tree.pop(leaf)
+        leaf_reduce_vars = ordinal_to_var[leaf]
+        for (group_terms, group_vars) in _partition(leaf_terms, leaf_reduce_vars):
+            term = reduce(prod_op, group_terms).reduce(sum_op, group_vars)
+            remaining_vars = frozenset(term.inputs) & reduce_vars
+            if not remaining_vars:
+                scalars.append(term.reduce(prod_op, leaf))
+            else:
+                new_plates = frozenset().union(
+                    *(var_tree[v] for v in remaining_vars))
+                if new_plates == leaf:
+                    raise ValueError("intractable!")
+                term = term.reduce(prod_op, leaf - new_plates)
+                term_tree[new_plates].append(term)
+
+    return reduce(prod_op, scalars)
+
+
+def einsum(eqn, *terms, **kwargs):
+    with interpretation(reflect):
+        naive_ast = naive_plated_einsum(eqn, *terms, **kwargs)
+        optimized_ast = apply_optimizer(naive_ast)
+    return reinterpret(optimized_ast)  # eager by default

funsor/ops.py

@@ -83,6 +83,16 @@ def sample(x, y):
     raise ValueError
 
 
+def reciprocal(x):
+    if isinstance(x, Number):
+        return 1. / x
+    if isinstance(x, torch.Tensor):
+        result = x.reciprocal()
+        result.clamp_(max=torch.finfo(result.dtype).max)
+        return result
+    raise ValueError("No reciprocal for type {}".format(type(x)))
+
+
 REDUCE_OP_TO_TORCH = {
     add: torch.sum,
     mul: torch.prod,
@@ -115,10 +125,17 @@ def sample(x, y):
 ])
 
 
+PRODUCT_INVERSES = {
+    mul: reciprocal,
+    add: neg,
+}
+
+
 __all__ = [
-    'REDUCE_OP_TO_TORCH',
     'ASSOCIATIVE_OPS',
     'DISTRIBUTIVE_OPS',
+    'PRODUCT_INVERSES',
+    'REDUCE_OP_TO_TORCH',
     'abs',
     'add',
     'and_',
@@ -137,6 +154,7 @@ def sample(x, y):
     'neg',
     'or_',
     'pow',
+    'reciprocal',
     'sample',
     'sub',
     'truediv',

funsor/testing.py

@@ -9,10 +9,9 @@
 import torch
 from six.moves import reduce
 
-import funsor.ops as ops
 from funsor.domains import Domain, bint
 from funsor.gaussian import Gaussian
-from funsor.terms import Binary, Funsor
+from funsor.terms import Funsor
 from funsor.torch import Tensor
 
 
@@ -140,27 +139,3 @@ def random_gaussian(inputs):
     prec_sqrt = torch.randn(batch_shape + event_shape + event_shape)
     precision = torch.matmul(prec_sqrt, prec_sqrt.transpose(-1, -2))
     return Gaussian(log_density, loc, precision, inputs)
-
-
-def naive_einsum(eqn, *terms, **kwargs):
-    backend = kwargs.pop('backend', 'torch')
-    if backend == 'torch':
-        sum_op, prod_op = ops.add, ops.mul
-    elif backend == 'pyro.ops.einsum.torch_log':
-        sum_op, prod_op = ops.logaddexp, ops.add
-    else:
-        raise ValueError("{} backend not implemented".format(backend))
-
-    assert isinstance(eqn, str)
-    assert all(isinstance(term, Funsor) for term in terms)
-    inputs, output = eqn.split('->')
-    assert len(output.split(',')) == 1
-    input_dims = frozenset(d for inp in inputs.split(',') for d in inp)
-    output_dims = frozenset(d for d in output)
-    reduce_dims = tuple(d for d in input_dims - output_dims)
-    prod = terms[0]
-    for term in terms[1:]:
-        prod = Binary(prod_op, prod, term)
-    for reduce_dim in reduce_dims:
-        prod = prod.reduce(sum_op, reduce_dim)
-    return prod

funsor/torch.py

@@ -383,7 +383,7 @@ def mvn_log_prob(loc, scale_tril, x):
     return functools.partial(_function, inputs, output)
 
 
-def einsum(equation, *operands):
+def torch_einsum(equation, *operands):
     """
     Wrapper around :func:`torch.einsum` to operate on real-valued Funsors.
 
@@ -412,7 +412,7 @@ def einsum(equation, *operands):
     'align_tensor',
     'align_tensors',
     'arange',
-    'einsum',
+    'torch_einsum',
     'function',
     'materialize',
 ]

test/test_einsum.py

@@ -15,15 +15,9 @@
 from funsor.torch import Tensor
 from funsor.interpreter import interpretation, reinterpret
 from funsor.optimizer import apply_optimizer
+from funsor.testing import assert_close, make_einsum_example
 
-from funsor.testing import assert_close, make_einsum_example, naive_einsum
-
-
-def naive_plated_einsum(eqn, *terms, **kwargs):
-    assert isinstance(eqn, str)
-    assert all(isinstance(term, funsor.Funsor) for term in terms)
-    # ...
-    raise NotImplementedError("TODO implement naive plated einsum")
+from funsor.einsum import naive_einsum, naive_plated_einsum
 
 
 EINSUM_EXAMPLES = [
@@ -103,25 +97,34 @@ def test_einsum_categorical(equation):
             assert actual.inputs[output_dim].dtype == sizes[output_dim]
 
 
-PLATED_EINSUM_EXAMPLES = [(ex, '') for ex in EINSUM_EXAMPLES] + [
+PLATED_EINSUM_EXAMPLES = [
     ('i->', 'i'),
-    ('i->i', 'i'),
     (',i->', 'i'),
-    (',i->i', 'i'),
     ('ai->', 'i'),
-    ('ai->i', 'i'),
-    ('ai->ai', 'i'),
-    (',ai,abij->aij', 'ij'),
-    ('a,ai,bij->bij', 'ij'),
+    (',ai,abij->', 'ij'),
+    ('a,ai,bij->', 'ij'),
+    ('ai,abi,bci,cdi->', 'i'),
+    ('aij,abij,bcij,cdij->', 'ij'),
+    ('a,abi,bcij,cdij->', 'ij'),
 ]
 
 
-@pytest.mark.xfail(reason="naive plated einsum not implemented")
 @pytest.mark.parametrize('equation,plates', PLATED_EINSUM_EXAMPLES)
-def test_plated_einsum(equation, plates):
+@pytest.mark.parametrize('backend', ['torch', 'pyro.ops.einsum.torch_log'])
+def test_plated_einsum(equation, plates, backend):
     inputs, outputs, sizes, operands, funsor_operands = make_einsum_example(equation)
-    expected = naive_ubersum(equation, *operands, plates=plates, backend='torch', modulo_total=False)[0]
-    actual = naive_plated_einsum(equation, *funsor_operands, plates=plates)
+    expected = naive_ubersum(equation, *operands, plates=plates, backend=backend, modulo_total=False)[0]
+    with interpretation(reflect):
+        naive_ast = naive_plated_einsum(equation, *funsor_operands, plates=plates, backend=backend)
+        optimized_ast = apply_optimizer(naive_ast)
+    actual_optimized = reinterpret(optimized_ast)  # eager by default
+    actual = naive_plated_einsum(equation, *funsor_operands, plates=plates, backend=backend)
+
+    assert_close(actual, actual_optimized, atol=1e-4)
+
+    if len(outputs[0]) > 0:
+        actual = actual.align(tuple(outputs[0]))
+
     assert expected.shape == actual.data.shape
     assert torch.allclose(expected, actual.data)
     for output in outputs:

test/test_optimizer.py

@@ -5,6 +5,7 @@
 
 import opt_einsum
 import torch
+import pyro.ops.contract as pyro_einsum
 
 import funsor
 
@@ -15,7 +16,8 @@
 from funsor.terms import reflect, Variable
 from funsor.torch import Tensor
 
-from funsor.testing import make_einsum_example, naive_einsum
+from funsor.testing import make_einsum_example, assert_close
+from funsor.einsum import naive_einsum, naive_plated_einsum, einsum
 
 
 def make_chain_einsum(num_steps):
@@ -101,3 +103,49 @@ def test_nested_einsum(eqn1, eqn2, optimize1, optimize2, backend1, backend2):
 
     assert torch.allclose(expected1, actual1.data)
     assert torch.allclose(expected2, actual2.data)
+
+
+def make_plated_hmm_einsum(num_steps, num_obs_plates=1, num_hidden_plates=0):
+
+    assert num_obs_plates >= num_hidden_plates
+    t0 = num_obs_plates
+
+    obs_plates = ''.join(opt_einsum.get_symbol(i) for i in range(num_obs_plates))
+    hidden_plates = ''.join(opt_einsum.get_symbol(i) for i in range(num_hidden_plates))
+
+    inputs = [str(opt_einsum.get_symbol(t0))]
+    for t in range(t0, num_steps+t0):
+        inputs.append(str(opt_einsum.get_symbol(t)) + str(opt_einsum.get_symbol(t+1)) + hidden_plates)
+        inputs.append(str(opt_einsum.get_symbol(t+1)) + obs_plates)
+    equation = ",".join(inputs) + "->"
+    return (equation, ''.join(set(obs_plates + hidden_plates)))
+
+
+PLATED_EINSUM_EXAMPLES = [
+    make_plated_hmm_einsum(num_steps, num_obs_plates=b, num_hidden_plates=a)
+    for num_steps in range(2, 6)
+    for (a, b) in [(0, 1), (0, 2), (0, 0), (1, 1), (1, 2), (1, 2)]
+]
+
+
+@pytest.mark.parametrize('equation,plates', PLATED_EINSUM_EXAMPLES)
+@pytest.mark.parametrize('backend', ['pyro.ops.einsum.torch_log'])
+def test_optimized_plated_einsum(equation, plates, backend):
+    inputs, outputs, sizes, operands, funsor_operands = make_einsum_example(equation)
+    expected = pyro_einsum.einsum(equation, *operands, plates=plates, backend=backend)[0]
+    actual = einsum(equation, *funsor_operands, plates=plates, backend=backend)
+
+    if len(equation) < 10:
+        actual_naive = naive_plated_einsum(equation, *funsor_operands, plates=plates, backend=backend)
+        assert_close(actual, actual_naive)
+
+    assert isinstance(actual, funsor.Tensor) and len(outputs) == 1
+    if len(outputs[0]) > 0:
+        actual = actual.align(tuple(outputs[0]))
+
+    assert expected.shape == actual.data.shape
+    assert torch.allclose(expected, actual.data)
+    for output in outputs:
+        for i, output_dim in enumerate(output):
+            assert output_dim in actual.inputs
+            assert actual.inputs[output_dim].dtype == sizes[output_dim]