Fix infer_discrete to work under the PyTorch jit

pyro/distributions/torch_patch.py

@@ -2,6 +2,7 @@
 
 import os
 
+import opt_einsum
 import torch
 
 if 'READTHEDOCS' not in os.environ:
@@ -84,4 +85,22 @@ def _einsum(equation, *operands):
     return _einsum._pyro_unpatched(equation, *operands)
 
 
+# This can be removed after https://github.com/dgasmith/opt_einsum/pull/77 is released.
+@patch_dependency('opt_einsum.helpers.compute_size_by_dict', opt_einsum)
+def _compute_size_by_dict(indices, idx_dict):
+    if torch._C._get_tracing_state():
+        # If running under the jit, convert all sizes from tensors to ints, the
+        # first time each idx_dict is seen.
+        last_idx_dict = getattr(_compute_size_by_dict, '_last_idx_dict', None)
+        if idx_dict is not last_idx_dict:
+            _compute_size_by_dict._last_idx_dict = idx_dict
+            for key, value in idx_dict.items():
+                idx_dict[key] = int(value)
+
+    ret = 1
+    for i in indices:
+        ret *= idx_dict[i]
+    return ret
+
+
 __all__ = []

pyro/infer/discrete.py

@@ -11,6 +11,7 @@
 from pyro.poutine.enumerate_messenger import EnumerateMessenger
 from pyro.poutine.replay_messenger import ReplayMessenger
 from pyro.poutine.util import prune_subsample_sites
+from pyro.util import jit_iter
 
 _RINGS = {0: MapRing, 1: SampleRing}
 
@@ -104,7 +105,7 @@ def _sample_posterior(model, first_available_dim, temperature, *args, **kwargs):
                 sample = log_prob._pyro_backward_result
                 if sample is not None:
                     new_value = packed.pack(node["value"], node["infer"]["_dim_to_symbol"])
-                    for index, dim in zip(sample, sample._pyro_sample_dims):
+                    for index, dim in zip(jit_iter(sample), sample._pyro_sample_dims):
                         if dim in new_value._pyro_dims:
                             index._pyro_dims = sample._pyro_dims[1:]
                             new_value = packed.gather(new_value, index, dim)

pyro/ops/einsum/adjoint.py

@@ -7,6 +7,7 @@
 from six import add_metaclass
 
 from pyro.ops import packed
+from pyro.util import jit_iter
 
 SAMPLE_SYMBOL = " "  # must be unique and precede alphanumeric characters
 
@@ -89,7 +90,7 @@ def einsum_backward_sample(operands, sample1, sample2):
         # Slice sample1 down based on choices in sample2.
         assert set(sample1._pyro_sample_dims).isdisjoint(sample2._pyro_sample_dims)
         sample_dims = sample1._pyro_sample_dims + sample2._pyro_sample_dims
-        for dim, index in zip(sample2._pyro_sample_dims, sample2):
+        for dim, index in zip(sample2._pyro_sample_dims, jit_iter(sample2)):
             if dim in sample1._pyro_dims:
                 index._pyro_dims = sample2._pyro_dims[1:]
                 sample1 = packed.gather(sample1, index, dim)

pyro/ops/einsum/torch_sample.py

@@ -9,6 +9,7 @@
 from pyro.ops import packed
 from pyro.ops.einsum.adjoint import Backward, einsum_backward_sample, transpose, unflatten
 from pyro.ops.einsum.util import Tensordot
+from pyro.util import jit_iter
 
 
 class _EinsumBackward(Backward):
@@ -25,7 +26,7 @@ def process(self, message):
         # Slice down operands before combining terms.
         sample2 = message
         if sample2 is not None:
-            for dim, index in zip(sample2._pyro_sample_dims, sample2):
+            for dim, index in zip(sample2._pyro_sample_dims, jit_iter(sample2)):
                 batch_dims = batch_dims.replace(dim, '')
                 for i, x in enumerate(operands):
                     if dim in x._pyro_dims:

pyro/util.py

@@ -348,6 +348,18 @@ def ignore_jit_warnings(filter=None):
         yield
 
 
+def jit_iter(tensor):
+    """
+    Iterate over a tensor, ignoring jit warnings.
+    """
+    # The "Iterating over a tensor" warning is erroneously a RuntimeWarning
+    # so we use a custom filter here.
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", "Iterating over a tensor")
+        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
+        return list(tensor)
+
+
 @contextmanager
 def optional(context_manager, condition):
     """

tests/infer/test_jit.py

@@ -14,9 +14,10 @@
 import pyro.ops.jit
 import pyro.poutine as poutine
 from pyro.infer import (SVI, JitTrace_ELBO, JitTraceEnum_ELBO, JitTraceGraph_ELBO, JitTraceMeanField_ELBO, Trace_ELBO,
-                        TraceEnum_ELBO, TraceGraph_ELBO, TraceMeanField_ELBO)
+                        TraceEnum_ELBO, TraceGraph_ELBO, TraceMeanField_ELBO, infer_discrete)
 from pyro.optim import Adam
 from pyro.poutine.indep_messenger import CondIndepStackFrame
+from pyro.util import ignore_jit_warnings
 from tests.common import assert_equal
 
 
@@ -399,6 +400,39 @@ def guide(data):
         svi.step(data)
 
 
+@pytest.mark.parametrize('length', [1, 2, 10])
+@pytest.mark.parametrize('temperature', [0, 1], ids=['map', 'sample'])
+def test_discrete(temperature, length):
+
+    @ignore_jit_warnings()
+    def hmm(transition, means, data):
+        states = [torch.tensor(0)]
+        for t in pyro.markov(range(len(data))):
+            states.append(pyro.sample("states_{}".format(t),
+                                      dist.Categorical(transition[states[-1]]),
+                                      infer={"enumerate": "parallel"}))
+            pyro.sample("obs_{}".format(t),
+                        dist.Normal(means[states[-1]], 1.),
+                        obs=data[t])
+        return tuple(states)
+
+    hidden_dim = 10
+    transition = 0.3 / hidden_dim + 0.7 * torch.eye(hidden_dim)
+    means = torch.arange(float(hidden_dim))
+    data = 1 + 2 * torch.randn(length)
+
+    decoder = infer_discrete(hmm, first_available_dim=-1, temperature=temperature)
+    jit_decoder = pyro.ops.jit.trace(decoder)
+
+    states = decoder(transition, means, data)
+    jit_states = jit_decoder(transition, means, data)
+    assert len(states) == len(jit_states)
+    for state, jit_state in zip(states, jit_states):
+        assert state.shape == jit_state.shape
+        if temperature == 0:
+            assert_equal(state, jit_state)
+
+
 @pytest.mark.parametrize("x,y", [
     (CondIndepStackFrame("a", -1, torch.tensor(2000), 2), CondIndepStackFrame("a", -1, 2000, 2)),
     (CondIndepStackFrame("a", -1, 1, 2), CondIndepStackFrame("a", -1, torch.tensor(1), 2)),