Removing doc strings from REGISTER_OP calls in core/ops.

PiperOrigin-RevId: 180670333

1 files changed, 2 insertions, 102 deletions

diff --git a/tensorflow/core/ops/checkpoint_ops.cc b/tensorflow/core/ops/checkpoint_ops.cc
index 08b00c8255..5fe82e1653 100644
--- a/tensorflow/core/ops/checkpoint_ops.cc
+++ b/tensorflow/core/ops/checkpoint_ops.cc
@@ -38,49 +38,7 @@ REGISTER_OP("GenerateVocabRemapping")
       c->set_output(0, c->Vector(num_new_vocab));
       c->set_output(1, c->Scalar());
       return Status::OK();
-    })
-    .Doc(R"doc(
-Given a path to new and old vocabulary files, returns a remapping Tensor of
-length `num_new_vocab`, where `remapping[i]` contains the row number in the old
-vocabulary that corresponds to row `i` in the new vocabulary (starting at line
-`new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i`
-in the new vocabulary is not in the old vocabulary.  The old vocabulary is
-constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the
-default value of -1.
-
-`num_vocab_offset` enables
-use in the partitioned variable case, and should generally be set through
-examining partitioning info.  The format of the files should be a text file,
-with each line containing a single entity within the vocabulary.
-
-For example, with `new_vocab_file` a text file containing each of the following
-elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3],
-`num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be
-`[0, -1, 2]`.
-
-The op also returns a count of how many entries in the new vocabulary
-were present in the old vocabulary, which is used to calculate the number of
-values to initialize in a weight matrix remapping
-
-This functionality can be used to remap both row vocabularies (typically,
-features) and column vocabularies (typically, classes) from TensorFlow
-checkpoints.  Note that the partitioning logic relies on contiguous vocabularies
-corresponding to div-partitioned variables.  Moreover, the underlying remapping
-uses an IndexTable (as opposed to an inexact CuckooTable), so client code should
-use the corresponding index_table_from_file() as the FeatureColumn framework
-does (as opposed to tf.feature_to_id(), which uses a CuckooTable).
-
-new_vocab_file: Path to the new vocab file.
-old_vocab_file: Path to the old vocab file.
-new_vocab_offset: How many entries into the new vocab file to start reading.
-num_new_vocab: Number of entries in the new vocab file to remap.
-old_vocab_size: Number of entries in the old vocab file to consider.  If -1,
-  use the entire old vocabulary.
-remapping: A Tensor of length num_new_vocab where the element at index i
-  is equal to the old ID that maps to the new ID i.  This element is -1 for any
-  new ID that is not found in the old vocabulary.
-num_present: Number of new vocab entries found in old vocab.
-)doc");
+    });
 
 REGISTER_OP("LoadAndRemapMatrix")
     .Input("ckpt_path: string")
@@ -109,63 +67,5 @@ REGISTER_OP("LoadAndRemapMatrix")
 
       c->set_output(0, c->Matrix(num_rows, num_cols));
       return Status::OK();
-    })
-    .Doc(R"doc(
-Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint
-at `ckpt_path` and potentially reorders its rows and columns using the
-specified remappings.
-
-Most users should use one of the wrapper initializers (such as
-`tf.contrib.framework.load_and_remap_matrix_initializer`) instead of this
-function directly.
-
-The remappings are 1-D tensors with the following properties:
-
-* `row_remapping` must have exactly `num_rows` entries. Row `i` of the output
-  matrix will be initialized from the row corresponding to index
-  `row_remapping[i]` in the old `Tensor` from the checkpoint.
-* `col_remapping` must have either 0 entries (indicating that no column
-  reordering is needed) or `num_cols` entries. If specified, column `j` of the
-  output matrix will be initialized from the column corresponding to index
-  `col_remapping[j]` in the old `Tensor` from the checkpoint.
-* A value of -1 in either of the remappings signifies a "missing" entry. In that
-  case, values from the `initializing_values` tensor will be used to fill that
-  missing row or column. If `row_remapping` has `r` missing entries and
-  `col_remapping` has `c` missing entries, then the following condition must be
-  true:
-
-`(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)`
-
-The remapping tensors can be generated using the GenerateVocabRemapping op.
-
-As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1],
-initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing
-the value from row i, column j of the old tensor in the checkpoint, the output
-matrix will look like the following:
-
-[[w(1, 0),  w(1, 2),  0.5],
- [w(0, 0),  w(0, 2), -0.5],
- [0.25,    -0.25,      42]]
-
-ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from
-  which the old matrix `Tensor` will be loaded.
-old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint.
-row_remapping: An int `Tensor` of row remappings (generally created by
-  `generate_vocab_remapping`).  Even if no row remapping is needed, this must
-  still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted
-  index-valued `Tensor` (e.g. [8, 9, 10, ...], for partitioned `Variables`).
-col_remapping: An int `Tensor` of column remappings (generally created by
-  `generate_vocab_remapping`).  May be a size-0 `Tensor` if only row remapping
-  is to be done (e.g. column ordering is the same).
-initializing_values: A float `Tensor` containing  values to fill in for cells
-  in the output matrix that are not loaded from the checkpoint. Length must be
-  exactly the same as the number of missing / new cells.
-num_rows: Number of rows (length of the 1st dimension) in the output matrix.
-num_cols: Number of columns (length of the 2nd dimension) in the output matrix.
-max_rows_in_memory: The maximum number of rows to load from the checkpoint at
-  once. If less than or equal to 0, the entire matrix will be loaded into
-  memory. Setting this arg trades increased disk reads for lower memory usage.
-output_matrix: Output matrix containing existing values loaded from the
-  checkpoint, and with any missing values filled in from initializing_values.
-)doc");
+    });
 }  // namespace tensorflow