Add support for ONNX op "com.microsoft.EmbedLayerNormalization"

ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp

@@ -0,0 +1,58 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op/com.microsoft/embed_layer_normalization.hpp"
+
+#include "default_opset.hpp"
+#include "onnx_import/core/null_node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+OutputVector embed_layer_normalization(const Node& node) {
+    auto nodes = node.get_ng_inputs();
+    auto num_nodes = nodes.size();
+    NGRAPH_CHECK(num_nodes >= 7 && num_nodes <= 8,
+                 "EmbedLayerNormalization takes 7 or 8 inputs. Provided " + std::to_string(num_nodes));
+    NGRAPH_CHECK(nodes[0].get_element_type() == element::i32, "input_ids must have int32 type");
+    auto zero = default_opset::Constant::create(element::i32, Shape{1}, {0});
+    auto word_embedding = std::make_shared<default_opset::Gather>(nodes[2], nodes[0], zero, 0);
+    auto input = std::make_shared<default_opset::Add>(word_embedding, nodes[3]);
+    if (!ngraph::op::is_null(nodes[1])) {
+        NGRAPH_CHECK(!ngraph::op::is_null(nodes[4]), "segment_ids provided, but segment_embedding input is missing");
+        NGRAPH_CHECK(nodes[1].get_element_type() == element::i32, "segment_ids must have int32 type");
+        auto segment_embedding = std::make_shared<default_opset::Gather>(nodes[4], nodes[1], zero, 0);
+        input = std::make_shared<default_opset::Add>(input, segment_embedding);
+    }
+    float eps = node.get_attribute_value<float>("epsilon");
+    // reduce over hidden_size
+    // hidden_size dimension is 2 here, because the shape after Gather(word_embedding, input_ids)
+    // is (batch_size, seq_len, hidden_size)
+    int hidden_size_dim = 2;
+    const auto reduction_axes = default_opset::Constant::create(element::i32, Shape{1}, {hidden_size_dim});
+    std::shared_ptr<ngraph::Node> result =
+        std::make_shared<default_opset::MVN>(input, reduction_axes, true, eps, ngraph::op::MVNEpsMode::INSIDE_SQRT);
+    // multiply by gamma
+    result = std::make_shared<default_opset::Multiply>(result, nodes[5]);
+    // add beta
+    result = std::make_shared<default_opset::Add>(result, nodes[6]);
+    std::shared_ptr<ngraph::Node> mask_index;
+    if (num_nodes > 7 && !ngraph::op::is_null(nodes[7])) {
+        NGRAPH_CHECK(nodes[7].get_element_type() == element::i32, "mask must have int32 type");
+        auto axis = default_opset::Constant::create(element::i32, Shape{}, {1});
+        mask_index = std::make_shared<default_opset::ReduceSum>(nodes[7], axis, false);
+    } else {
+        auto batch_size = std::make_shared<default_opset::Gather>(std::make_shared<default_opset::ShapeOf>(nodes[0]),
+                                                                  zero,  // indices
+                                                                  zero   // axis
+        );
+        mask_index = std::make_shared<default_opset::Broadcast>(zero, batch_size);
+    }
+    return {result, mask_index};
+}
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph

ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp

@@ -0,0 +1,17 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "onnx_import/core/node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+OutputVector embed_layer_normalization(const Node& node);
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph

ngraph/frontend/onnx/frontend/src/ops_bridge.cpp

@@ -30,6 +30,7 @@
 #include "op/ceil.hpp"
 #include "op/clip.hpp"
 #include "op/com.microsoft/bias_gelu.hpp"
+#include "op/com.microsoft/embed_layer_normalization.hpp"
 #include "op/com.microsoft/skip_layer_normalization.hpp"
 #include "op/compress.hpp"
 #include "op/concat.hpp"
@@ -480,6 +481,7 @@ OperatorsBridge::OperatorsBridge() {
     REGISTER_OPERATOR_WITH_DOMAIN(OPENVINO_ONNX_DOMAIN, "Swish", 1, swish);
 
     REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "BiasGelu", 1, bias_gelu);
+    REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "EmbedLayerNormalization", 1, embed_layer_normalization);
     REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "SkipLayerNormalization", 1, skip_layer_normalization);
 }
 

ngraph/test/engines_util/ie_engines.cpp

@@ -337,6 +337,9 @@ testing::AssertionResult test::IE_Engine::compare_results_with_tolerance_as_fp(c
             comparison_result = test::compare_with_tolerance(test_results.first, test_results.second, tolerance);
             break;
         }
+        case InferenceEngine::Precision::I32:
+            comparison_result = compare_blobs<int32_t>(computed_output_blob, expected_output_blob, 0);
+            break;
         default:
             comparison_result = testing::AssertionFailure() << "Unsupported data type encountered in "
                                                                "'compare_results_with_tolerance_as_fp' method";

ngraph/test/engines_util/interpreter_engine.cpp

@@ -124,6 +124,9 @@ testing::AssertionResult test::INTERPRETER_Engine::compare_results_with_toleranc
         case element::Type_t::f32:
             comparison_result = compare_with_fp_tolerance(expected_result_constant, result_tensor, tolerance);
             break;
+        case element::Type_t::i32:
+            comparison_result = compare_values<int32_t>(expected_result_constant, result_tensor, 0);
+            break;
         default:
             comparison_result = testing::AssertionFailure() << "Unsupported data type encountered in "
                                                                "'compare_results_with_tolerance_as_fp' method";

ngraph/test/models/onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.prototxt

@@ -0,0 +1,186 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
+  node {
+    input: "input_ids"
+    input: "segment_ids"
+    input: "word_embeddings"
+    input: "position_embeddings"
+    input: "segment_embeddings"
+    input: "gamma"
+    input: "beta"
+    input: "mask"
+    output: "output"
+    output: "mask_index"
+    name: "EmbedLayerNormalization_1"
+    op_type: "EmbedLayerNormalization"
+    attribute {
+      name: "epsilon"
+      f: 9.999999960041972e-13
+      type: FLOAT
+    }
+    domain: "com.microsoft"
+  }
+  name: "graph"
+  input {
+    name: "input_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "segment_ids"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "word_embeddings"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "word_embed_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "position_embeddings"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "pos_embed_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "segment_embeddings"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "segment_embed_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "gamma"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "beta"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "mask"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+          dim {
+            dim_param: "seq_len"
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "mask_index"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_param: "batch_size"
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}