1 files changed, 0 insertions, 150 deletions
diff --git a/tensorflow/core/grappler/optimizers/data/vectorization/add_vectorizer.cc b/tensorflow/core/grappler/optimizers/data/vectorization/add_vectorizer.cc
deleted file mode 100644
index d90a51b01a..0000000000
--- a/tensorflow/core/grappler/optimizers/data/vectorization/add_vectorizer.cc
+++ /dev/null
@@ -1,150 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/cc/framework/ops.h"
-#include "tensorflow/cc/framework/scope_internal.h"
-#include "tensorflow/cc/ops/array_ops.h"
-#include "tensorflow/cc/ops/math_ops.h"
-#include "tensorflow/cc/ops/standard_ops.h"
-#include "tensorflow/core/graph/node_builder.h"
-#include "tensorflow/core/grappler/optimizers/data/vectorization/vectorizer_registry.h"
-
-namespace tensorflow {
-namespace grappler {
-
-namespace {
-
-const char* const kExpandDimsPrefix = "vectorized/expanddims/";
-
-// Reshapes stacked inputs for broadcast. Stacked inputs have an extra leading
-// dimension, which may cause automatic broadcasting rules to expand the
-// input dimensions wrongly when the unstacked shapes have different ranks.
-// To avoid that, we reshape stacked inputs to the maximum rank they need
-// to be broadcasted to.
-//
-// For example, suppose we have inputs A and B, where A is a stacked tensor with
-// shape [n, 5] (where n is the stack size) and B is an unstacked tensor with
-// shape [12, 7, 5]. If we added them directly, tensorflow broadcasting rules
-// would expand the dimensions of A to [1, n, 5], then (incorrectly) check that
-// the dimensions n and 7 are compatible, and if so, create an output of shape
-// [12, 7, 5]. However, correct addition of these inputs would create an output
-// with shape [n, 12, 7, 5]: we need to manually expand the dimensions of A
-// *after* the leading dimension, i.e. expand A to the shape [n, 1, 1, 5] before
-// broadcasting.
-Status ExpandDimsForBroadcast(std::vector<WrappedTensor>* inputs, Graph* g) {
-  Status status;
-  Scope parent = NewInternalScope(g, &status, nullptr);
-  Scope s = parent.NewSubScope(kExpandDimsPrefix);
-
-  // TODO(rachelim): We can potentially get rid of all these ops if shapes are
-  // known statically
-
-  Output const_0 = ops::Const(s, 0);
-  Output const_1 = ops::Const(s, 1);
-
-  std::vector<Output> ranks;
-  ranks.reserve(inputs->size());
-
-  // Get the stacked rank of each input
-  for (const auto& input : *inputs) {
-    Output rank = ops::Rank(s, Output(input.node, input.output_index));
-
-    if (!input.stacked) {
-      // If the input is unstacked, add 1
-      rank = ops::Add(s, rank, const_1);
-    }
-
-    ranks.push_back(rank);
-  }
-
-  // Pack the ranks into one tensor to get the max
-  Output packed_ranks = ops::Stack(s, ranks);
-
-  Output max_rank =
-      ops::Max(s, packed_ranks, const_0, ops::Max::Attrs().KeepDims(true));
-
-  std::vector<WrappedTensor> expanded_inputs;
-  expanded_inputs.reserve(inputs->size());
-
-  // For all inputs that are stacked, expand dimensions after dim 0.
-  for (size_t i = 0; i < inputs->size(); ++i) {
-    if (!inputs->at(i).stacked) {
-      expanded_inputs.push_back(inputs->at(i));
-      continue;
-    }
-
-    Output input(inputs->at(i).node, inputs->at(i).output_index);
-
-    // Number of dimensions to expand
-    Output rank_diff = ops::Sub(s, max_rank, ranks[i]);
-
-    // [1] * rank_diff
-    Output ones = ops::Tile(s, ops::Const(s, {1}), rank_diff);
-
-    Output const_vec_1 = ops::Const(s, {1});
-
-    Output shape = ops::Shape(s, input);
-
-    // shape[:1]
-    Output concat_pre =
-        ops::StridedSlice(s, shape, const_vec_1, const_vec_1, const_vec_1,
-                          ops::StridedSlice::Attrs().BeginMask(1));
-
-    // shape[1:]
-    Output concat_post =
-        ops::StridedSlice(s, shape, const_vec_1, const_vec_1, const_vec_1,
-                          ops::StridedSlice::Attrs().EndMask(1));
-
-    // tf.concat([shape[:1], ones, shape[1:]], 0)
-    Output new_shape = ops::Concat(s, {concat_pre, ones, concat_post}, const_0);
-
-    Output result = ops::Reshape(s, input, new_shape);
-
-    expanded_inputs.push_back({result.node(), 0, true});
-  }
-
-  inputs->swap(expanded_inputs);
-  return status;
-}
-
-class AddVectorizer : public Vectorizer {
- public:
-  Status Vectorize(const Node& node, Graph* outer_scope,
-                   std::vector<WrappedTensor>&& inputs,
-                   std::vector<WrappedTensor>* outputs) override {
-    if (node.num_inputs() != 2) {
-      return errors::Internal("Add op should only have two inputs.");
-    }
-
-    TF_RETURN_IF_ERROR(ExpandDimsForBroadcast(&inputs, outer_scope));
-
-    // Add new Add node with the same op and attrs as the original node
-    Node* new_add_node;
-    TF_RETURN_IF_ERROR(NodeBuilder("Add", "Add")
-                           .Input(inputs[0].node, inputs[0].output_index)
-                           .Input(inputs[1].node, inputs[1].output_index)
-                           .Finalize(outer_scope, &new_add_node));
-
-    // Add output mappings
-    outputs->push_back({new_add_node, 0, true});
-    return Status::OK();
-  }
-};
-
-REGISTER_VECTORIZER("Add", AddVectorizer);
-
-}  // namespace
-}  // namespace grappler
-}  // namespace tensorflow