histogram: set v3 as default

tensorboard/plugins/histogram/summary.py

@@ -16,13 +16,20 @@
 
 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]`.
+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. 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]`).
+like 30. For V2 format (deprecated), 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.
 
 NOTE: This module is in beta, and its API is subject to change, but the
 data that it stores to disk will be supported forever.
@@ -35,13 +42,10 @@
 from tensorboard.plugins.histogram import summary_v2
 
 
-# Export V2 versions.
+# Export the latest versions.
 histogram = summary_v2.histogram
 histogram_pb = summary_v2.histogram_pb
 
-# Export V3 versions.
-histogram_v3 = summary_v2.histogram_v3
-
 
 def _buckets(data, bucket_count=None):
     """Create a TensorFlow op to group data into histogram buckets.

tensorboard/plugins/histogram/summary_test.py

@@ -264,50 +264,6 @@ def test_default_step(self):
             # Reset to default state for other tests.
             tf2.summary.experimental.set_step(None)
 
-
-class SummaryV2OpGraphTest(SummaryV2OpTest, tf.test.TestCase):
-    def write_histogram_event(self, *args, **kwargs):
-        kwargs.setdefault("step", 1)
-        # Hack to extract current scope since there's no direct API for it.
-        with tf.name_scope("_") as temp_scope:
-            scope = temp_scope.rstrip("/_")
-
-        @tf2.function
-        def graph_fn():
-            # Recreate the active scope inside the defun since it won't propagate.
-            with tf.name_scope(scope):
-                self.call_histogram_op(*args, **kwargs)
-
-        writer = tf2.summary.create_file_writer(self.get_temp_dir())
-        with writer.as_default():
-            graph_fn()
-        writer.close()
-
-    def test_no_gradient_error_xla(self):
-        @tf2.function(jit_compile=True)
-        def graph_fn():
-            x = tf.constant(1.0)
-            with tf2.GradientTape() as tape1:
-                with tf2.GradientTape() as tape2:
-                    tape1.watch(x)
-                    tape2.watch(x)
-                    self.call_histogram_op(
-                        name="loss", step=0, data=x, buckets=10
-                    )
-
-        # Note that XLA CPU/GPU has no outside compilation support, so summaries
-        # won't actually run in a jit_compiled function. TPUs do, and follow
-        # some similar codepaths, so this test stops at graph building to
-        # exercise those paths without a TPU available.
-        writer = tf2.summary.create_file_writer(self.get_temp_dir())
-        with writer.as_default():
-            graph_fn.get_concrete_function()
-
-
-class SummaryV3OpTest(SummaryV2OpTest, tf.test.TestCase):
-    def call_histogram_op(self, *args, **kwargs):
-        summary.histogram_v3(*args, **kwargs)
-
     def test_singleton_input(self):
         pb = self.histogram("twelve", [12])
         buckets = tensor_util.make_ndarray(pb.value[0].tensor)
@@ -344,7 +300,7 @@ def test_zero_bucket_count(self):
         np.testing.assert_array_equal(buckets, np.array([]).reshape((0, 3)))
 
 
-class SummaryV3OpGraphTest(SummaryV3OpTest, tf.test.TestCase):
+class SummaryV2OpGraphTest(SummaryV2OpTest, tf.test.TestCase):
     def write_histogram_event(self, *args, **kwargs):
         kwargs.setdefault("step", 1)
         # Hack to extract current scope since there's no direct API for it.