Support non-static shape in tf.distributions.Categorical.

PiperOrigin-RevId: 200596358

tensorflow/python/kernel_tests/distributions/BUILD

@@ -93,6 +93,7 @@ cuda_py_test(
     size = "small",
     srcs = ["categorical_test.py"],
     additional_deps = [
+        "@absl_py//absl/testing:parameterized",
         "//tensorflow/python/ops/distributions",
         "//third_party/py/numpy",
         "//tensorflow/python:array_ops",

tensorflow/python/kernel_tests/distributions/categorical_test.py

@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl.testing import parameterized
 import numpy as np
 
 from tensorflow.python.framework import constant_op
@@ -40,7 +41,7 @@ def make_categorical(batch_shape, num_classes, dtype=dtypes.int32):
   return categorical.Categorical(logits, dtype=dtype)
 
 
-class CategoricalTest(test.TestCase):
+class CategoricalTest(test.TestCase, parameterized.TestCase):
 
   def testP(self):
     p = [0.2, 0.8]
@@ -131,7 +132,7 @@ class CategoricalTest(test.TestCase):
     with self.test_session():
       self.assertAllClose(dist.prob(0).eval(), 0.2)
 
-  def testCDFWithDynamicEventShape(self):
+  def testCDFWithDynamicEventShapeKnownNdims(self):
     """Test that dynamically-sized events with unknown shape work."""
     batch_size = 2
     histograms = array_ops.placeholder(dtype=dtypes.float32,
@@ -167,6 +168,21 @@ class CategoricalTest(test.TestCase):
     self.assertAllClose(actual_cdf_one, expected_cdf_one)
     self.assertAllClose(actual_cdf_two, expected_cdf_two)
 
+  @parameterized.named_parameters(
+      ("test1", [0, 1], [[0.5, 0.3, 0.2], [1.0, 0.0, 0.0]], [0.0, 1.0]),
+      ("test2", [2, 5], [[0.9, 0.0, 0.0, 0.0, 0.0, 0.1],
+                         [0.15, 0.2, 0.05, 0.35, 0.13, 0.12]], [0.9, 0.88]))
+  def testCDFWithDynamicEventShapeUnknownNdims(
+      self, events, histograms, expected_cdf):
+    """Test that dynamically-sized events with unknown shape work."""
+    event_ph = array_ops.placeholder_with_default(events, shape=None)
+    histograms_ph = array_ops.placeholder_with_default(histograms, shape=None)
+    dist = categorical.Categorical(probs=histograms_ph)
+    cdf_op = dist.cdf(event_ph)
+
+    actual_cdf = self.evaluate(cdf_op)
+    self.assertAllClose(actual_cdf, expected_cdf)
+
   def testCDFWithBatch(self):
     histograms = [[0.1, 0.2, 0.3, 0.25, 0.15],
                   [0.0, 0.75, 0.2, 0.05, 0.0]]

tensorflow/python/ops/distributions/categorical.py

@@ -32,12 +32,8 @@ from tensorflow.python.ops.distributions import util as distribution_util
 from tensorflow.python.util.tf_export import tf_export
 
 
-def _broadcast_cat_event_and_params(event, params, base_dtype=dtypes.int32):
+def _broadcast_cat_event_and_params(event, params, base_dtype):
   """Broadcasts the event or distribution parameters."""
-  if event.shape.ndims is None:
-    raise NotImplementedError(
-        "Cannot broadcast with an event tensor of unknown rank.")
-
   if event.dtype.is_integer:
     pass
   elif event.dtype.is_floating:
@@ -47,15 +43,18 @@ def _broadcast_cat_event_and_params(event, params, base_dtype=dtypes.int32):
   else:
     raise TypeError("`value` should have integer `dtype` or "
                     "`self.dtype` ({})".format(base_dtype))
-
-  if params.get_shape()[:-1] == event.get_shape():
-    params = params
-  else:
-    params *= array_ops.ones_like(
-        array_ops.expand_dims(event, -1), dtype=params.dtype)
+  shape_known_statically = (
+      params.shape.ndims is not None and
+      params.shape[:-1].is_fully_defined() and
+      event.shape.is_fully_defined())
+  if not shape_known_statically or params.shape[:-1] != event.shape:
+    params *= array_ops.ones_like(event[..., array_ops.newaxis],
+                                  dtype=params.dtype)
     params_shape = array_ops.shape(params)[:-1]
     event *= array_ops.ones(params_shape, dtype=event.dtype)
-    event.set_shape(tensor_shape.TensorShape(params.get_shape()[:-1]))
+    if params.shape.ndims is not None:
+      event.set_shape(tensor_shape.TensorShape(params.shape[:-1]))
+
   return event, params