/* Copyright 2023 The MediaPipe Authors.

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.
==============================================================================*/

syntax = "proto3";

package mediapipe.tasks.components.processors.proto;

option java_package = "com.google.mediapipe.tasks.components.processors.proto";
option java_outer_classname = "TransformerParametersProto";

// The parameters of transformer (https://arxiv.org/pdf/1706.03762.pdf)
message TransformerParameters {
  // Batch size of tensors.
  int32 batch_size = 1;

  // Maximum sequence length of the input/output tensor.
  int32 max_seq_length = 2;

  // Embedding dimension (or model dimension), `d_model` in the paper.
  // `d_k` == `d_v` == `d_model`/`h`.
  int32 embedding_dim = 3;

  // Hidden dimension used in the feedforward layer, `d_ff` in the paper.
  int32 hidden_dimension = 4;

  // Head dimension, `d_k` or `d_v` in the paper.
  int32 head_dimension = 5;

  // Number of heads, `h` in the paper.
  int32 num_heads = 6;

  // Number of stacked transformers, `N` in the paper.
  int32 num_stacks = 7;
}
