1 files changed, 0 insertions, 66 deletions
diff --git a/tensorflow/contrib/tpu/proto/tpu_embedding_config.proto b/tensorflow/contrib/tpu/proto/tpu_embedding_config.proto
deleted file mode 100644
index 3476cc8953..0000000000
--- a/tensorflow/contrib/tpu/proto/tpu_embedding_config.proto
+++ /dev/null
@@ -1,66 +0,0 @@
-syntax = "proto3";
-
-package tensorflow.tpu;
-
-import "tensorflow/contrib/tpu/proto/optimization_parameters.proto";
-
-// The TPUEmbeddingConfiguration contains specification of TPU Embedding lookups
-// and gradient updates separate from the TF Graph.
-message TPUEmbeddingConfiguration {
-  // model_mode specifies whether the model is to be run in training or
-  // inference. In inference mode, gradient updates to embedding tables are not
-  // performed.
-  enum ModelMode {
-    INVALID = 0;
-    TRAINING = 1;
-    INFERENCE = 2;
-  }
-
-  ModelMode model_mode = 1;
-
-  // num_hosts is the number of host CPU systems in the training/inference job.
-  // Each embedding table must be sharded into num_hosts separate Variables,
-  // placed separately on the num_hosts CPU devices in the cluster. Sharding
-  // will be performed equivalently to the 'div' sharding_strategy option of
-  // embedding_lookup() and embedding_lookup_sparse().
-  int32 num_hosts = 2;
-
-  // The total number of TensorNodes. This is equal to num_hosts times the
-  // number of TensorNodes attached to each host.
-  int32 num_tensornodes = 3;
-
-  // The number of training examples per TensorNode.
-  int32 batch_size = 4;
-
-  // Each Embedding
-  message TPUEmbeddingTable {
-    // Name of the embedding table. This will be used to name Variables in the
-    // Tensorflow Graph.
-    string name = 1;
-
-    // Number of rows of the embedding table. The Variable created to hold the
-    // learned embedding table values will have shape (num_rows, width).
-    int32 num_rows = 3;
-
-    // Width of the embedding table. The Variable created to hold the
-    // learned embedding table values will have shape (num_rows, width).
-    int32 width = 4;
-
-    // Number of distinct embedding activation vectors per training example
-    // produced by lookups into this table during model evaluation. For each
-    // table, the Graph will receive an activations Tensor of shape
-    //   (batch_size * table.num_features, table.width).
-    // For example, num_features = 1 produces equivalent behavior to a single
-    // tf.nn.embedding_lookup() call. In the case of 'multivalent' embeddings,
-    // (i.e. tf.nn.embedding_lookup_sparse()) which compute weighted averages of
-    // embedding table rows, num_features is the number of vectors produced
-    // after averaging. In sequence models num_features is typically equal
-    // to the sequence length, since each sequence element must be represented
-    // separately to the convolutional or recurrent network.
-    int32 num_features = 5;
-
-    OptimizationParameters optimization_parameters = 6;
-  }
-
-  repeated TPUEmbeddingTable table_config = 5;
-}