histogram: make summary_v2.histogram_pb TPU compatible

tensorboard/plugins/histogram/summary_test.py

@@ -175,6 +175,41 @@ class SummaryV2PbTest(SummaryBaseTest, tf.test.TestCase):
     def histogram(self, *args, **kwargs):
         return summary.histogram_pb(*args, **kwargs)
 
+    def test_singleton_input(self):
+        pb = self.histogram("twelve", [12])
+        buckets = tensor_util.make_ndarray(pb.value[0].tensor)
+        # By default there will be 30 buckets.
+        expected_buckets = np.array(
+            [[12, 12, 0] for _ in range(29)] + [[12, 12, 1]]
+        )
+        np.testing.assert_allclose(buckets, expected_buckets)
+
+    def test_input_with_all_same_values(self):
+        pb = self.histogram("twelven", [12, 12, 12])
+        buckets = tensor_util.make_ndarray(pb.value[0].tensor)
+        # By default there will be 30 buckets.
+        expected_buckets = np.array(
+            [[12, 12, 0] for _ in range(29)] + [[12, 12, 3]]
+        )
+        np.testing.assert_allclose(buckets, expected_buckets)
+
+    def test_empty_input(self):
+        pb = self.histogram("empty", [])
+        buckets = tensor_util.make_ndarray(pb.value[0].tensor)
+        # By default there will be 30 buckets.
+        np.testing.assert_allclose(buckets, np.zeros((30, 3)))
+
+    def test_empty_input_of_high_rank(self):
+        pb = self.histogram("empty_but_fancy", [[[], []], [[], []]])
+        buckets = tensor_util.make_ndarray(pb.value[0].tensor)
+        # By default there will be 30 buckets.
+        np.testing.assert_allclose(buckets, np.zeros((30, 3)))
+
+    def test_zero_bucket_count(self):
+        pb = self.histogram("zero_bucket_count", [1, 1, 1], buckets=0)
+        buckets = tensor_util.make_ndarray(pb.value[0].tensor)
+        np.testing.assert_array_equal(buckets, np.array([]).reshape((0, 3)))
+
 
 class SummaryV2OpTest(SummaryBaseTest, tf.test.TestCase):
     def setUp(self):

tensorboard/plugins/histogram/summary_v2.py

@@ -14,15 +14,22 @@
 # ==============================================================================
 """Histogram summaries and TensorFlow operations to create them, V2 versions.
 
-A histogram summary stores a list of buckets. Each bucket is encoded as
-a triple `[left_edge, right_edge, count]`. Thus, a full histogram is
-encoded as a tensor of dimension `[k, 3]`.
-
-In general, the value of `k` (the number of buckets) will be a constant,
-like 30. There are two edge cases: if there is no data, then there are
-no buckets (the shape is `[0, 3]`); and if there is data but all points
-have the same value, then there is one bucket whose left and right
-endpoints are the same (the shape is `[1, 3]`).
+A histogram summary stores a list of buckets. Each bucket is encoded as a triple
+`[left_edge, right_edge, count]`. Thus, a full histogram is encoded as a tensor
+of dimension `[k, 3]`, where the first `k - 1` buckets are closed-open and the
+last bucket is closed-closed.
+
+In general, the value of `k` (the number of buckets) will be a constant, like 30.
+For V2 format, there are two edge cases: if there is no data, then there are no
+buckets (the shape is `[0, 3]`); and if there is data but all points have the
+same value, then there is one bucket whose left and right endpoints are the same
+(the shape is `[1, 3]`).
+
+For V3 format, the shape of the output histogram is always constant (`[k, 3]`).
+In the case of empty data, the output will be an all-zero histogram of shape
+`[k, 3]`, where all edges and counts are zeros. If there is data but all points
+have the same value, then all buckets' left and right edges are the same and only
+the last bucket has nonzero count.
 """
 
 import contextlib
@@ -257,11 +264,11 @@ def histogram_pb(tag, data, buckets=None, description=None):
       tag: String tag for the summary.
       data: A `np.array` or array-like form of any shape. Must have type
         castable to `float`.
-      buckets: Optional positive `int`. The output will have this
-        many buckets, except in two edge cases. If there is no data, then
-        there are no buckets. If there is data but all points have the
-        same value, then there is one bucket whose left and right
-        endpoints are the same.
+      buckets: Optional positive `int`. The output shape will always be
+        [buckets, 3]. If there is no data, then an all-zero array of shape
+        [buckets, 3] will be returned. If there is data but all points have
+        the same value, then all buckets' left and right endpoints are the
+        same and only the last bucket has nonzero count.
       description: Optional long-form description for this summary, as a
         `str`. Markdown is supported. Defaults to empty.
 
@@ -270,15 +277,18 @@ def histogram_pb(tag, data, buckets=None, description=None):
     """
     bucket_count = DEFAULT_BUCKET_COUNT if buckets is None else buckets
     data = np.array(data).flatten().astype(float)
-    if data.size == 0:
-        buckets = np.array([]).reshape((0, 3))
+    if bucket_count == 0 or data.size == 0:
+        histogram_buckets = np.zeros((bucket_count, 3))
     else:
         min_ = np.min(data)
         max_ = np.max(data)
         range_ = max_ - min_
         if range_ == 0:
-            center = min_
-            buckets = np.array([[center - 0.5, center + 0.5, float(data.size)]])
+            left_edges = right_edges = np.array([min_] * bucket_count)
+            bucket_counts = np.array([0] * (bucket_count - 1) + [data.size])
+            histogram_buckets = np.array(
+                [left_edges, right_edges, bucket_counts]
+            ).transpose()
         else:
             bucket_width = range_ / bucket_count
             offsets = data - min_
@@ -295,10 +305,10 @@ def histogram_pb(tag, data, buckets=None, description=None):
             edges = np.linspace(min_, max_, bucket_count + 1)
             left_edges = edges[:-1]
             right_edges = edges[1:]
-            buckets = np.array(
+            histogram_buckets = np.array(
                 [left_edges, right_edges, bucket_counts]
             ).transpose()
-    tensor = tensor_util.make_tensor_proto(buckets, dtype=np.float64)
+    tensor = tensor_util.make_tensor_proto(histogram_buckets, dtype=np.float64)
 
     summary_metadata = metadata.create_summary_metadata(
         display_name=None, description=description