Revert D22227939: [TB] Add support for hparam domain_discrete

Test Plan: revert-hammer

Differential Revision:
D22227939 (4c25428c8c)

Original commit changeset: d2f0cd8e5632

fbshipit-source-id: c4329fcead69cb0f3d368a254d8756fb04be742d

test/expect/TestTensorBoard.test_hparams_domain_discrete.expect

@@ -1,25 +0,0 @@
-(value {
-   tag: "_hparams_/experiment"
-   metadata {
-     plugin_data {
-       plugin_name: "hparams"
-       content: "\022V\"\023\n\002lr \003*\013\n\t\021\232\231\231\231\231\231\271?\"\031\n\010bool_var \003*\013\n\t\021\000\000\000\000\000\000\360?\"\026\n\nstring_var \001*\006\n\004\032\002hi*\014\n\n\022\010accuracy"
-     }
-   }
- }, value {
-   tag: "_hparams_/session_start_info"
-   metadata {
-     plugin_data {
-       plugin_name: "hparams"
-       content: "\032<\n\017\n\002lr\022\t\021\232\231\231\231\231\231\271?\n\025\n\010bool_var\022\t\021\000\000\000\000\000\000\360?\n\022\n\nstring_var\022\004\032\002hi"
-     }
-   }
- }, value {
-   tag: "_hparams_/session_end_info"
-   metadata {
-     plugin_data {
-       plugin_name: "hparams"
-       content: "\"\002\010\001"
-     }
-   }
- })

test/test_tensorboard.py

@@ -465,12 +465,6 @@ class TestTensorBoardSummary(BaseTestCase):
         mt = {'accuracy': 0.1}
         self.assertTrue(compare_proto(summary.hparams(hp, mt), self))
 
-    def test_hparams_domain_discrete(self):
-        hp = {"lr": 0.1, "bool_var": True, "string_var": "hi"}
-        mt = {"accuracy": 0.1}
-        hp_domain = {"lr": [0.1], "bool_var": [True], "string_var": ["hi"]}
-        self.assertTrue(compare_proto(summary.hparams(hp, mt, hp_domain), self))
-
     def test_mesh(self):
         v = np.array([[[1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1]]], dtype=float)
         c = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255]]], dtype=int)

torch/utils/tensorboard/summary.py

@@ -10,7 +10,6 @@ import os
 # pylint: disable=unused-import
 from six.moves import range
 
-from google.protobuf import struct_pb2
 from tensorboard.compat.proto.summary_pb2 import Summary
 from tensorboard.compat.proto.summary_pb2 import HistogramProto
 from tensorboard.compat.proto.summary_pb2 import SummaryMetadata
@@ -51,7 +50,7 @@ def _draw_single_box(image, xmin, ymin, xmax, ymax, display_str, color='black',
     return image
 
 
-def hparams(hparam_dict=None, metric_dict=None, hparam_domain_discrete=None):
+def hparams(hparam_dict=None, metric_dict=None):
     """Outputs three `Summary` protocol buffers needed by hparams plugin.
     `Experiment` keeps the metadata of an experiment, such as the name of the
       hyperparameters and the name of the metrics.
@@ -63,8 +62,6 @@ def hparams(hparam_dict=None, metric_dict=None, hparam_domain_discrete=None):
         and their values.
       metric_dict: A dictionary that contains names of the metrics
         and their values.
-      hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that
-        contains names of the hyperparameters and all discrete values they can hold
 
     Returns:
       The `Summary` protobufs for Experiment, SessionStartInfo and
@@ -102,21 +99,6 @@ def hparams(hparam_dict=None, metric_dict=None, hparam_domain_discrete=None):
         logging.warning('parameter: metric_dict should be a dictionary, nothing logged.')
         raise TypeError('parameter: metric_dict should be a dictionary, nothing logged.')
 
-    hparam_domain_discrete = hparam_domain_discrete or {}
-    if not isinstance(hparam_domain_discrete, dict):
-        raise TypeError(
-            "parameter: hparam_domain_discrete should be a dictionary, nothing logged."
-        )
-    for k, v in hparam_domain_discrete.items():
-        if (
-            k not in hparam_dict
-            or not isinstance(v, list)
-            or not all(isinstance(d, type(hparam_dict[k])) for d in v)
-        ):
-            raise TypeError(
-                "parameter: hparam_domain_discrete[{}] should be a list of same type as "
-                "hparam_dict[{}].".format(k, k)
-            )
     hps = []
 
 
@@ -126,68 +108,17 @@ def hparams(hparam_dict=None, metric_dict=None, hparam_domain_discrete=None):
             continue
         if isinstance(v, int) or isinstance(v, float):
             ssi.hparams[k].number_value = v
-
-            if k in hparam_domain_discrete:
-                domain_discrete = struct_pb2.ListValue(
-                    values=[
-                        struct_pb2.Value(number_value=d)
-                        for d in hparam_domain_discrete[k]
-                    ]
-                )
-            else:
-                domain_discrete = None
-
-            hps.append(
-                HParamInfo(
-                    name=k,
-                    type=DataType.Value("DATA_TYPE_FLOAT64"),
-                    domain_discrete=domain_discrete,
-                )
-            )
+            hps.append(HParamInfo(name=k, type=DataType.Value("DATA_TYPE_FLOAT64")))
             continue
 
         if isinstance(v, string_types):
             ssi.hparams[k].string_value = v
-
-            if k in hparam_domain_discrete:
-                domain_discrete = struct_pb2.ListValue(
-                    values=[
-                        struct_pb2.Value(string_value=d)
-                        for d in hparam_domain_discrete[k]
-                    ]
-                )
-            else:
-                domain_discrete = None
-
-            hps.append(
-                HParamInfo(
-                    name=k,
-                    type=DataType.Value("DATA_TYPE_STRING"),
-                    domain_discrete=domain_discrete,
-                )
-            )
+            hps.append(HParamInfo(name=k, type=DataType.Value("DATA_TYPE_STRING")))
             continue
 
         if isinstance(v, bool):
             ssi.hparams[k].bool_value = v
-
-            if k in hparam_domain_discrete:
-                domain_discrete = struct_pb2.ListValue(
-                    values=[
-                        struct_pb2.Value(bool_value=d)
-                        for d in hparam_domain_discrete[k]
-                    ]
-                )
-            else:
-                domain_discrete = None
-
-            hps.append(
-                HParamInfo(
-                    name=k,
-                    type=DataType.Value("DATA_TYPE_BOOL"),
-                    domain_discrete=domain_discrete,
-                )
-            )
+            hps.append(HParamInfo(name=k, type=DataType.Value("DATA_TYPE_BOOL")))
             continue
 
         if isinstance(v, torch.Tensor):

torch/utils/tensorboard/writer.py

@@ -268,9 +268,7 @@ class SummaryWriter(object):
         """Returns the directory where event files will be written."""
         return self.log_dir
 
-    def add_hparams(
-        self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None
-    ):
+    def add_hparams(self, hparam_dict, metric_dict, run_name=None):
         """Add a set of hyperparameters to be compared in TensorBoard.
 
         Args:
@@ -283,8 +281,6 @@ class SummaryWriter(object):
               here should be unique in the tensorboard record. Otherwise the value
               you added by ``add_scalar`` will be displayed in hparam plugin. In most
               cases, this is unwanted.
-            hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that
-              contains names of the hyperparameters and all discrete values they can hold
             run_name (str): Name of the run, to be included as part of the logdir.
               If unspecified, will use current timestamp.
 
@@ -305,7 +301,7 @@ class SummaryWriter(object):
         torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
         if type(hparam_dict) is not dict or type(metric_dict) is not dict:
             raise TypeError('hparam_dict and metric_dict should be dictionary.')
-        exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)
+        exp, ssi, sei = hparams(hparam_dict, metric_dict)
 
         if not run_name:
             run_name = str(time.time())