histogram: remove backwards buckets in v1 histogram migration (#5404)

* remove empty buckets on both ends to avoid having `backwards` buckets

* fix typo

* use data min/max as bucket left/right hand limit

* grammar fix

* improve readability

  - improve docs
  - test extremal histogram values
  - fix tag names in test

* small change

tensorboard/data_compat.py

@@ -81,13 +81,39 @@ def make_summary(tag, metadata, data):
 
 
 def _migrate_histogram_value(value):
+    """Convert `old-style` histogram value to `new-style`.
+
+    The "old-style" format can have outermost bucket limits of -DBL_MAX and
+    DBL_MAX, which are problematic for visualization. We replace those here
+    with the actual min and max values seen in the input data, but then in
+    order to avoid introducing "backwards" buckets (where left edge > right
+    edge), we first must drop all empty buckets on the left and right ends.
+    """
     histogram_value = value.histo
-    bucket_lefts = [histogram_value.min] + histogram_value.bucket_limit[:-1]
-    bucket_rights = histogram_value.bucket_limit[:-1] + [histogram_value.max]
     bucket_counts = histogram_value.bucket
-    buckets = np.array(
-        [bucket_lefts, bucket_rights, bucket_counts], dtype=np.float32
-    ).transpose()
+    # Find the indices of the leftmost and rightmost non-empty buckets.
+    n = len(bucket_counts)
+    start = next((i for i in range(n) if bucket_counts[i] > 0), n)
+    end = next((i for i in reversed(range(n)) if bucket_counts[i] > 0), -1)
+    if start > end:
+        # If all input buckets were empty, treat it as a zero-bucket
+        # new-style histogram.
+        buckets = np.zeros([0, 3], dtype=np.float32)
+    else:
+        # Discard empty buckets on both ends, and keep only the "inner"
+        # edges from the remaining buckets. Note that bucket indices range
+        # from `start` to `end` inclusive, but bucket_limit indices are
+        # exclusive of `end` - this is because bucket_limit[i] is the
+        # right-hand edge for bucket[i].
+        bucket_counts = bucket_counts[start : end + 1]
+        inner_edges = histogram_value.bucket_limit[start:end]
+        # Use min as the left-hand limit for the first non-empty bucket.
+        bucket_lefts = [histogram_value.min] + inner_edges
+        # Use max as the right-hand limit for the last non-empty bucket.
+        bucket_rights = inner_edges + [histogram_value.max]
+        buckets = np.array(
+            [bucket_lefts, bucket_rights, bucket_counts], dtype=np.float32
+        ).transpose()
 
     summary_metadata = histogram_metadata.create_summary_metadata(
         display_name=value.metadata.display_name or value.tag,

tensorboard/data_compat_test.py

@@ -201,10 +201,100 @@ def test_histogram(self):
         self.assertEqual(expected_metadata, new_value.metadata)
         self.assertTrue(new_value.HasField("tensor"))
         buckets = tensor_util.make_ndarray(new_value.tensor)
+        for bucket in buckets:
+            # No `backwards` buckets.
+            self.assertLessEqual(bucket[0], bucket[1])
         self.assertEqual(old_value.histo.min, buckets[0][0])
         self.assertEqual(old_value.histo.max, buckets[-1][1])
         self.assertEqual(23 * 45, buckets[:, 2].astype(int).sum())
 
+    def test_empty_histogram(self):
+        with tf.compat.v1.Graph().as_default():
+            old_op = tf.compat.v1.summary.histogram(
+                "empty_yet_important", tf.constant([])
+            )
+            old_value = self._value_from_op(old_op)
+        assert old_value.HasField("histo"), old_value
+        new_value = data_compat.migrate_value(old_value)
+
+        self.assertEqual("empty_yet_important", new_value.tag)
+        expected_metadata = histogram_metadata.create_summary_metadata(
+            display_name="empty_yet_important", description=""
+        )
+        self.assertEqual(expected_metadata, new_value.metadata)
+        self.assertTrue(new_value.HasField("tensor"))
+        buckets = tensor_util.make_ndarray(new_value.tensor)
+        self.assertEmpty(buckets)
+
+    def test_single_value_histogram(self):
+        with tf.compat.v1.Graph().as_default():
+            old_op = tf.compat.v1.summary.histogram(
+                "single_value_data", tf.constant([1] * 1024)
+            )
+            old_value = self._value_from_op(old_op)
+        assert old_value.HasField("histo"), old_value
+        new_value = data_compat.migrate_value(old_value)
+
+        self.assertEqual("single_value_data", new_value.tag)
+        expected_metadata = histogram_metadata.create_summary_metadata(
+            display_name="single_value_data", description=""
+        )
+        self.assertEqual(expected_metadata, new_value.metadata)
+        self.assertTrue(new_value.HasField("tensor"))
+        buckets = tensor_util.make_ndarray(new_value.tensor)
+        # Only one bucket is kept.
+        self.assertEqual((1, 3), buckets.shape)
+        self.assertEqual(1, buckets[0][0])
+        self.assertEqual(1, buckets[-1][1])
+        self.assertEqual(1024, buckets[0][2])
+
+    def test_histogram_with_empty_buckets_on_both_ends(self):
+        with tf.compat.v1.Graph().as_default():
+            old_op = tf.compat.v1.summary.histogram(
+                "data_with_empty_buckets_on_both_ends",
+                tf.constant([1, 1, 1, 2, 2, 3, 3, 3, 3]),
+            )
+            old_value = self._value_from_op(old_op)
+        assert old_value.HasField("histo"), old_value
+        new_value = data_compat.migrate_value(old_value)
+
+        self.assertEqual("data_with_empty_buckets_on_both_ends", new_value.tag)
+        expected_metadata = histogram_metadata.create_summary_metadata(
+            display_name="data_with_empty_buckets_on_both_ends", description=""
+        )
+        self.assertEqual(expected_metadata, new_value.metadata)
+        self.assertTrue(new_value.HasField("tensor"))
+        buckets = tensor_util.make_ndarray(new_value.tensor)
+        for bucket in buckets:
+            # No `backwards` buckets.
+            self.assertLessEqual(bucket[0], bucket[1])
+        self.assertEqual(1, buckets[0][0])
+        self.assertEqual(3, buckets[-1][1])
+        self.assertEqual(9, buckets[:, 2].astype(int).sum())
+
+    def test_histogram_with_extremal_values(self):
+        with tf.compat.v1.Graph().as_default():
+            old_op = tf.compat.v1.summary.histogram(
+                "extremal_values", tf.constant([-1e20, 1e20])
+            )
+            old_value = self._value_from_op(old_op)
+        assert old_value.HasField("histo"), old_value
+        new_value = data_compat.migrate_value(old_value)
+
+        self.assertEqual("extremal_values", new_value.tag)
+        expected_metadata = histogram_metadata.create_summary_metadata(
+            display_name="extremal_values", description=""
+        )
+        self.assertEqual(expected_metadata, new_value.metadata)
+        self.assertTrue(new_value.HasField("tensor"))
+        buckets = tensor_util.make_ndarray(new_value.tensor)
+        for bucket in buckets:
+            # No `backwards` buckets.
+            self.assertLessEqual(bucket[0], bucket[1])
+        self.assertEqual(old_value.histo.min, buckets[0][0])
+        self.assertEqual(old_value.histo.max, buckets[-1][1])
+        self.assertEqual(2, buckets[:, 2].astype(int).sum())
+
     def test_new_style_histogram(self):
         with tf.compat.v1.Graph().as_default():
             op = histogram_summary.op(