[ONNX FE] Fix issues of com.microsoft.EmbedLayerNormalization (#13034)
1. Add implement of EmbedLayerNormalization which seq_len != pos_embed_len 2. Align EmbedLayerNormalization implement in OpenVINO with ONNX-Runtime
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sheng.gui@intel.com authored and 
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@@ -15,8 +15,8 @@ OutputVector embed_layer_normalization(const Node& node) {
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auto nodes = node.get_ng_inputs();
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auto num_nodes = nodes.size();
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NGRAPH_CHECK(num_nodes >= 7 && num_nodes <= 8,
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"EmbedLayerNormalization takes 7 or 8 inputs. Provided " + std::to_string(num_nodes));
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NGRAPH_CHECK(num_nodes >= 7 && num_nodes <= 9,
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"EmbedLayerNormalization takes 7 or 9 inputs. Provided " + std::to_string(num_nodes));
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NGRAPH_CHECK(nodes[0].get_element_type() == element::i32, "input_ids must have int32 type");
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const auto& input_ids = nodes[0];
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@@ -27,10 +27,29 @@ OutputVector embed_layer_normalization(const Node& node) {
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const auto& gamma = nodes[5];
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const auto& beta = nodes[6];
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auto zero = default_opset::Constant::create(element::i32, Shape{1}, {0});
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const auto zero = default_opset::Constant::create(element::i32, Shape{1}, {0});
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std::shared_ptr<ngraph::Node> input = std::make_shared<default_opset::Gather>(word_embeddings, input_ids, zero, 0);
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input = std::make_shared<default_opset::Add>(input, position_embeddings);
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// add position embeddings
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if (num_nodes > 8 && !ngraph::op::is_null(nodes[8])) {
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// if we have position_ids
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const auto& position_ids = nodes[8];
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const auto gathered_position_embeddings =
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std::make_shared<default_opset::Gather>(position_embeddings, position_ids, zero, 0);
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input = std::make_shared<default_opset::Add>(input, gathered_position_embeddings);
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} else {
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// input_ids' shape is [batchsize, sequence_length]
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// input's shape is [batchsize, sequence_length, hidden_size]
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// position_embeddings's shape is [max_sequence_length, hidden_size]
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// therefore input and position_embeddings cannot be added together
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// so we need slice the position_embeddings to [sequence_length, hidden_size] first
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// then add it with input.
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const auto one = default_opset::Constant::create(element::i32, Shape{1}, {1});
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const auto input_ids_shape = std::make_shared<default_opset::ShapeOf>(input_ids, element::i32);
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const auto seqlen = std::make_shared<default_opset::Gather>(input_ids_shape, one, zero, 0);
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const auto gathered_position_embeddings =
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std::make_shared<default_opset::Slice>(position_embeddings, zero, seqlen, one, zero);
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input = std::make_shared<default_opset::Add>(input, gathered_position_embeddings);
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}
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// add segment embeddings if available
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if (!ngraph::op::is_null(segment_ids)) {
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NGRAPH_CHECK(!ngraph::op::is_null(segment_embeddings),
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+203
@@ -0,0 +1,203 @@
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ir_version: 6
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producer_name: "nGraph"
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graph {
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node {
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input: "input_ids"
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input: "segment_ids"
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input: "word_embeddings"
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input: "position_embeddings"
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input: "segment_embeddings"
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input: "gamma"
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input: "beta"
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input: "mask"
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input: "position_ids"
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output: "output"
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output: "mask_index"
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name: "EmbedLayerNormalization_1"
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op_type: "EmbedLayerNormalization"
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attribute {
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name: "epsilon"
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f: 9.999999960041972e-13
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type: FLOAT
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}
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domain: "com.microsoft"
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}
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name: "graph"
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input {
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name: "input_ids"
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type {
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tensor_type {
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elem_type: 6
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shape {
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dim {
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dim_param: "batch_size"
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}
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dim {
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dim_param: "seq_len"
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}
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}
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}
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}
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}
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input {
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name: "segment_ids"
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type {
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tensor_type {
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elem_type: 6
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shape {
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dim {
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dim_param: "batch_size"
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}
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dim {
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dim_param: "seq_len"
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}
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}
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}
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}
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}
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input {
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name: "word_embeddings"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_param: "word_embed_len"
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}
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dim {
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dim_value: 5
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}
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}
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}
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}
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}
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input {
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name: "position_embeddings"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_param: "pos_embed_len"
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}
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dim {
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dim_value: 5
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}
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}
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}
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}
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}
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input {
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name: "segment_embeddings"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_param: "segment_embed_len"
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}
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dim {
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dim_value: 5
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}
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}
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}
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}
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}
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input {
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name: "gamma"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 5
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}
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}
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}
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}
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}
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input {
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name: "beta"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_value: 5
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}
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}
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}
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}
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}
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input {
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name: "mask"
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type {
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tensor_type {
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elem_type: 6
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shape {
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dim {
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dim_param: "batch_size"
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}
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dim {
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dim_param: "seq_len"
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}
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}
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}
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}
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}
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input {
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name: "position_ids"
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type {
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tensor_type {
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elem_type: 6
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shape {
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dim {
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dim_param: "batch_size"
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}
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dim {
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dim_param: "seq_len"
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}
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}
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}
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}
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}
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output {
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name: "output"
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type {
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tensor_type {
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elem_type: 1
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shape {
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dim {
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dim_param: "batch_size"
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}
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dim {
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dim_param: "seq_len"
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}
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dim {
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dim_value: 5
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}
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}
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}
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}
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}
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output {
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name: "mask_index"
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type {
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tensor_type {
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elem_type: 6
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shape {
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dim {
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dim_param: "batch_size"
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}
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}
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}
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}
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}
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}
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opset_import {
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version: 11
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}
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opset_import {
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domain: "com.microsoft"
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version: 1
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}
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@@ -405,6 +405,206 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_dynamic_shapes
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test_case.run_with_tolerance_as_fp(1e-6);
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}
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NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_diff_seq_len_pos_embed_len) {
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const auto function = onnx_import::import_onnx_model(
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file_util::path_join(CommonTestUtils::getExecutableDirectory(),
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SERIALIZED_ZOO,
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"onnx/com.microsoft/embed_layer_normalization_dynamic_shapes.onnx"));
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std::vector<int> input_ids = {
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8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
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};
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std::vector<int> segment_ids = {
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0, 2, 0, 2, 2, 0, 2, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 0, 1, 1, 1,
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};
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std::vector<float> word_embeddings = {
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0.96980906, 0.65314001, 0.17090958, 0.35815218, 0.75068617, 0.60783064, 0.32504722, 0.03842543, 0.63427407,
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0.95894927, 0.65279031, 0.63505888, 0.99529958, 0.58185035, 0.41436860, 0.47469750, 0.62351012, 0.33800763,
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0.67475230, 0.31720173, 0.77834547, 0.94957107, 0.66252685, 0.01357164, 0.62284607, 0.67365962, 0.97194499,
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0.87819350, 0.50962436, 0.05571469, 0.45115921, 0.01998767, 0.44171092, 0.97958672, 0.35944447, 0.48089352,
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0.68866116, 0.88047588, 0.91823548, 0.21682213, 0.56518888, 0.86510259, 0.50896895, 0.91672295, 0.92115760,
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0.08311249, 0.27771857, 0.00935670, 0.84234208, 0.64717412,
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};
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std::vector<float> position_embeddings = {
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0.84138614, 0.26473016, 0.39782074, 0.55282146, 0.16494046, 0.36980811, 0.14644176, 0.56961840, 0.70373726,
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0.28847644, 0.43328807, 0.75610667, 0.39609829, 0.89603841, 0.63892108, 0.89155442, 0.68005556, 0.44919774,
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0.97857094, 0.11620191, 0.76702368, 0.41182014, 0.67543906, 0.24979627, 0.31321833, 0.96541619, 0.58846509,
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0.65966839, 0.53320622, 0.23053302, 0.39486930, 0.61880857, 0.47486752, 0.47013220, 0.71607453, 0.28799102,
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0.38346222, 0.74916983, 0.87845218, 0.10286336, 0.84138614, 0.26473016, 0.39782074, 0.55282146, 0.16494046,
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};
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std::vector<float> segment_embeddings = {
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0.09237389,
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0.35404667,
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0.55181628,
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0.03362509,
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0.96896178,
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0.32099724,
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0.22126268,
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0.14126390,
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0.09725992,
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0.98404223,
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0.26034093,
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0.53702253,
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0.44792616,
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0.09956909,
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0.35231167,
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};
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std::vector<float> gamma = {
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0.46924916,
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0.84114015,
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0.90464777,
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0.03755938,
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0.50831544,
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};
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std::vector<float> beta = {
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0.16684751,
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0.77905101,
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0.86493331,
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0.41139671,
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0.13997258,
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};
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std::vector<int> mask = {
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1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
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};
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std::vector<float> expected_output = {
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-0.04089922, 0.35108989, 0.30442458, 0.39546335, 1.15422225, 0.10419128, -0.19301927, 0.01070970,
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0.43977541, 0.89119899, -0.51436460, 1.99256825, 1.41077507, 0.38642293, 0.17583044, 0.03320138,
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1.16508031, -0.24356931, 0.47440714, -0.17844005, 0.20463173, 1.90038323, 1.14138567, 0.34504607,
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0.16403235, -0.24976699, 0.29362509, 0.34502214, 0.41751838, 1.09390712, 0.12354189, 1.83025289,
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1.05569196, 0.34413773, 0.35469764, -0.69760042, 0.76338542, 1.75443077, 0.44126555, 0.18181801,
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0.73277575, 0.45443264, 0.17068321, 0.36591727, 0.72869974, -0.56090516, 0.14415455, 1.47314119,
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0.42908576, 0.73084539, -0.22373237, 2.26550221, 0.05606699, 0.39417523, 0.35234636, 0.78569502,
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0.77521765, -0.65131050, 0.40168875, 0.45527256, 0.38715565, 0.98521245, 2.21446753, 0.36345237,
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-0.33269632, 0.36558092, 1.36846578, 1.37523413, 0.33698002, 0.28889543, -0.40639281, 1.01643157,
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0.59668219, 0.39197800, 1.03101778, 0.02551098, -0.03612846, -0.01371557, 0.43444607, 0.96746695,
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0.60583955, -0.10362893, 0.40574494, 0.38046724, 0.87445319, -0.00880148, -0.15437943, 0.08118075,
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0.44650543, 0.85956848, -0.27865338, 2.10837507, 0.04798460, 0.43948367, -0.10185169, 0.19978794,
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1.32323360, 1.20525467, 0.44288942, -0.84200430, 0.52563053, 0.69949460, 0.73987913, 0.34668452,
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0.74545687, 0.57696682, 0.22452033, -0.27099937, 0.39649010, 0.87083614, -0.18965788, 0.58206403,
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-0.08108193, 0.42067638, 1.05117214, -0.34287399, 0.20424896, 0.27994895, 0.46011117, 0.70890665,
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};
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std::vector<int> expected_mask_index = {
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6,
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5,
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5,
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};
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auto test_case = test::TestCase(function, s_device);
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test_case.add_input<int>(Shape{3, 8}, input_ids);
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test_case.add_input<int>(Shape{3, 8}, segment_ids);
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test_case.add_input<float>(Shape{10, 5}, word_embeddings);
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test_case.add_input<float>(Shape{9, 5}, position_embeddings);
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test_case.add_input<float>(Shape{3, 5}, segment_embeddings);
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test_case.add_input<float>(Shape{5}, gamma);
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test_case.add_input<float>(Shape{5}, beta);
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test_case.add_input<int>(Shape{3, 8}, mask);
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test_case.add_expected_output<float>(Shape{3, 8, 5}, expected_output);
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test_case.add_expected_output<int>(Shape{3}, expected_mask_index);
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test_case.run_with_tolerance_as_fp(1e-6);
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}
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NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_with_position_ids) {
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const auto function = onnx_import::import_onnx_model(
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file_util::path_join(CommonTestUtils::getExecutableDirectory(),
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SERIALIZED_ZOO,
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"onnx/com.microsoft/embed_layer_normalization_dynamic_shapes_with_position_ids.onnx"));
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std::vector<int> input_ids = {
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8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
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};
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std::vector<int> segment_ids = {
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0, 2, 0, 2, 2, 0, 2, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 0, 1, 1, 1,
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};
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std::vector<float> word_embeddings = {
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0.96980906, 0.65314001, 0.17090958, 0.35815218, 0.75068617, 0.60783064, 0.32504722, 0.03842543, 0.63427407,
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0.95894927, 0.65279031, 0.63505888, 0.99529958, 0.58185035, 0.41436860, 0.47469750, 0.62351012, 0.33800763,
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0.67475230, 0.31720173, 0.77834547, 0.94957107, 0.66252685, 0.01357164, 0.62284607, 0.67365962, 0.97194499,
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0.87819350, 0.50962436, 0.05571469, 0.45115921, 0.01998767, 0.44171092, 0.97958672, 0.35944447, 0.48089352,
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0.68866116, 0.88047588, 0.91823548, 0.21682213, 0.56518888, 0.86510259, 0.50896895, 0.91672295, 0.92115760,
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0.08311249, 0.27771857, 0.00935670, 0.84234208, 0.64717412,
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};
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std::vector<float> position_embeddings = {
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0.84138614, 0.26473016, 0.39782074, 0.55282146, 0.16494046, 0.43328807, 0.75610667, 0.39609829, 0.89603841,
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0.63892108, 0.36980811, 0.14644176, 0.56961840, 0.70373726, 0.28847644, 0.89155442, 0.68005556, 0.44919774,
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0.97857094, 0.11620191, 0.76702368, 0.41182014, 0.67543906, 0.24979627, 0.31321833, 0.28799102, 0.38346222,
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0.74916983, 0.87845218, 0.10286336, 0.96541619, 0.58846509, 0.65966839, 0.53320622, 0.23053302, 0.39486930,
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0.61880857, 0.47486752, 0.47013220, 0.71607453, 0.84138614, 0.26473016, 0.39782074, 0.55282146, 0.16494046,
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};
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std::vector<float> segment_embeddings = {
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0.09237389,
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0.35404667,
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0.55181628,
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0.03362509,
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0.96896178,
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0.32099724,
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0.22126268,
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0.14126390,
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0.09725992,
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0.98404223,
|
||||
0.26034093,
|
||||
0.53702253,
|
||||
0.44792616,
|
||||
0.09956909,
|
||||
0.35231167,
|
||||
};
|
||||
std::vector<float> gamma = {
|
||||
0.46924916,
|
||||
0.84114015,
|
||||
0.90464777,
|
||||
0.03755938,
|
||||
0.50831544,
|
||||
};
|
||||
std::vector<float> beta = {
|
||||
0.16684751,
|
||||
0.77905101,
|
||||
0.86493331,
|
||||
0.41139671,
|
||||
0.13997258,
|
||||
};
|
||||
std::vector<int> mask = {
|
||||
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
|
||||
};
|
||||
std::vector<int> position_ids = {
|
||||
0, 2, 1, 3, 4, 6, 7, 5, 8, 2, 1, 3, 4, 6, 7, 5, 0, 2, 1, 3, 4, 6, 7, 5,
|
||||
};
|
||||
std::vector<float> expected_output = {
|
||||
-0.04089922, 0.35108989, 0.30442458, 0.39546335, 1.15422225, 0.10419128, -0.19301927, 0.01070970,
|
||||
0.43977541, 0.89119899, -0.51436460, 1.99256825, 1.41077507, 0.38642293, 0.17583044, 0.03320138,
|
||||
1.16508031, -0.24356931, 0.47440714, -0.17844005, 0.20463173, 1.90038323, 1.14138567, 0.34504607,
|
||||
0.16403235, -0.24976699, 0.29362509, 0.34502214, 0.41751838, 1.09390712, 0.12354189, 1.83025289,
|
||||
1.05569196, 0.34413773, 0.35469764, -0.69760042, 0.76338542, 1.75443077, 0.44126555, 0.18181801,
|
||||
0.73277575, 0.45443264, 0.17068321, 0.36591727, 0.72869974, -0.56090516, 0.14415455, 1.47314119,
|
||||
0.42908576, 0.73084539, -0.22373237, 2.26550221, 0.05606699, 0.39417523, 0.35234636, 0.78569502,
|
||||
0.77521765, -0.65131050, 0.40168875, 0.45527256, 0.38715565, 0.98521245, 2.21446753, 0.36345237,
|
||||
-0.33269632, 0.36558092, 1.36846578, 1.37523413, 0.33698002, 0.28889543, -0.40639281, 1.01643157,
|
||||
0.59668219, 0.39197800, 1.03101778, 0.02551098, -0.03612846, -0.01371557, 0.43444607, 0.96746695,
|
||||
0.60583955, -0.10362893, 0.40574494, 0.38046724, 0.87445319, -0.00880148, -0.15437943, 0.08118075,
|
||||
0.44650543, 0.85956848, -0.27865338, 2.10837507, 0.04798460, 0.43948367, -0.10185169, 0.19978794,
|
||||
1.32323360, 1.20525467, 0.44288942, -0.84200430, 0.52563053, 0.69949460, 0.73987913, 0.34668452,
|
||||
0.74545687, 0.57696682, 0.22452033, -0.27099937, 0.39649010, 0.87083614, -0.18965788, 0.58206403,
|
||||
-0.08108193, 0.42067638, 1.05117214, -0.34287399, 0.20424896, 0.27994895, 0.46011117, 0.70890665,
|
||||
};
|
||||
std::vector<int> expected_mask_index = {
|
||||
6,
|
||||
5,
|
||||
5,
|
||||
};
|
||||
|
||||
auto test_case = test::TestCase(function, s_device);
|
||||
test_case.add_input<int>(Shape{3, 8}, input_ids);
|
||||
test_case.add_input<int>(Shape{3, 8}, segment_ids);
|
||||
test_case.add_input<float>(Shape{10, 5}, word_embeddings);
|
||||
test_case.add_input<float>(Shape{9, 5}, position_embeddings);
|
||||
test_case.add_input<float>(Shape{3, 5}, segment_embeddings);
|
||||
test_case.add_input<float>(Shape{5}, gamma);
|
||||
test_case.add_input<float>(Shape{5}, beta);
|
||||
test_case.add_input<int>(Shape{3, 8}, mask);
|
||||
test_case.add_input<int>(Shape{3, 8}, position_ids);
|
||||
test_case.add_expected_output<float>(Shape{3, 8, 5}, expected_output);
|
||||
test_case.add_expected_output<int>(Shape{3}, expected_mask_index);
|
||||
test_case.run_with_tolerance_as_fp(1e-6);
|
||||
}
|
||||
|
||||
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention) {
|
||||
const auto function = onnx_import::import_onnx_model(file_util::path_join(CommonTestUtils::getExecutableDirectory(),
|
||||
SERIALIZED_ZOO,
|
||||
|
||||
Reference in new issue
Block a user