Add TransformerParameters proto
PiperOrigin-RevId: 529213840
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@ -93,3 +93,8 @@ mediapipe_proto_library(
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"//mediapipe/framework:calculator_proto",
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"//mediapipe/framework:calculator_proto",
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],
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],
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)
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)
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mediapipe_proto_library(
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name = "transformer_params_proto",
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srcs = ["transformer_params.proto"],
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)
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@ -0,0 +1,46 @@
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/* Copyright 2023 The MediaPipe Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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syntax = "proto3";
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package mediapipe.tasks.components.processors.proto;
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option java_package = "com.google.mediapipe.tasks.components.processors.proto";
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option java_outer_classname = "TransformerParametersProto";
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// The parameters of transformer (https://arxiv.org/pdf/1706.03762.pdf)
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message TransformerParameters {
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// Batch size of tensors.
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int32 batch_size = 1;
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// Maximum sequence length of the input/output tensor.
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int32 max_seq_length = 2;
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// Embedding dimension (or model dimension), `d_model` in the paper.
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// `d_k` == `d_v` == `d_model`/`h`.
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int32 embedding_dim = 3;
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// Hidden dimension used in the feedforward layer, `d_ff` in the paper.
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int32 hidden_dimension = 4;
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// Head dimension, `d_k` or `d_v` in the paper.
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int32 head_dimension = 5;
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// Number of heads, `h` in the paper.
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int32 num_heads = 6;
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// Number of stacked transformers, `N` in the paper.
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int32 num_stacks = 7;
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}
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