[PyAPI][Opset15] Add EmbeddingBagPacked and EmbeddingBagOffsets to Py…

…API (openvinotoolkit#25101)

### Details:
 - *Add EmbeddingBagPacked and EmbeddingBagOffsets to PyAPI*
 - *...*

### Tickets:
 - *141864*
 - *141865*

---------

Co-authored-by: Michal Lukaszewski <[email protected]>

src/bindings/python/src/openvino/runtime/opset15/__init__.py

@@ -7,4 +7,6 @@
 # TODO (ticket 138273): Add previous opset operators at the end of opset15 development
 from openvino.runtime.opset1.ops import parameter
 from openvino.runtime.opset15.ops import col2im
+from openvino.runtime.opset15.ops import embedding_bag_offsets
+from openvino.runtime.opset15.ops import embedding_bag_packed
 from openvino.runtime.opset15.ops import scatter_nd_update

src/bindings/python/src/openvino/runtime/opset15/ops.py

@@ -4,9 +4,12 @@
 
 """Factory functions for ops added to openvino opset15."""
 from functools import partial
-from typing import Optional, Literal, List
+from typing import List, Literal, Optional
 
+import numpy as np
 from openvino.runtime import Node, Type
+from openvino.runtime.opset1 import convert_like
+from openvino.runtime.opset14 import constant
 from openvino.runtime.opset_utils import _get_node_factory
 from openvino.runtime.utils.decorators import nameable_op
 from openvino.runtime.utils.types import NodeInput, as_nodes
@@ -83,3 +86,61 @@ def col2im(
             "pads_end": pads_end,
         },
     )
+
+
+@nameable_op
+def embedding_bag_offsets(
+    emb_table: NodeInput,
+    indices: NodeInput,
+    offsets: NodeInput,
+    default_index: Optional[NodeInput] = None,
+    per_sample_weights: Optional[NodeInput] = None,
+    reduction: Literal["sum", "mean"] = "sum",
+    name: Optional[str] = None,
+) -> Node:
+    """Return a node which performs sums or means of bags of embeddings without the intermediate embeddings.
+
+    :param emb_table: Tensor containing the embedding lookup table.
+    :param indices: 1D Tensor with indices.
+    :param offsets: 1D Tensor containing the starting index positions of each bag in indices.
+    :param per_sample_weights: Tensor with weights for each sample.
+    :param default_index: Scalar containing default index in embedding table to fill empty bags.
+                          If unset or set to -1, empty bags will be filled with 0.
+                          Reverse indexing using negative indices is not supported.
+    :param reduction: String to select algorithm used to perform reduction of elements in bag.
+    :param name: Optional name for output node.
+    :return: The new node performing EmbeddingBagOffsets operation.
+    """
+    inputs = [emb_table, indices, offsets]
+    if default_index is not None:
+        inputs.append(default_index)
+    elif per_sample_weights is not None:
+        inputs.append(convert_like(constant(np.array(-1, np.int32)), inputs[1]))
+    if per_sample_weights is not None:
+        inputs.append(per_sample_weights)
+
+    return _get_node_factory_opset15().create("EmbeddingBagOffsets", as_nodes(*inputs, name=name), {"reduction": reduction})
+
+
+@nameable_op
+def embedding_bag_packed(
+    emb_table: NodeInput,
+    indices: NodeInput,
+    per_sample_weights: Optional[NodeInput] = None,
+    reduction: Literal["sum", "mean"] = "sum",
+    name: Optional[str] = None,
+) -> Node:
+    """Return a node which performs sums or means of "bags" of embeddings, without the intermediate embeddings.
+
+    :param emb_table: Tensor containing the embedding lookup table.
+    :param indices: 2D Tensor of shape [batch, indices_per_bag] with indices.
+    :param per_sample_weights: Tensor of weights to be multiplied with embedding table with same shape as indices.
+    :param reduction: Operator to perform reduction of elements in bag.
+    :param name: Optional name for output node.
+    :return: The new node performing EmbeddingBagPacked operation.
+    """
+    inputs = [emb_table, indices]
+    if per_sample_weights is not None:
+        inputs.append(per_sample_weights)
+
+    return _get_node_factory_opset15().create("EmbeddingBagPacked", as_nodes(*inputs, name=name), {"reduction": reduction})

src/bindings/python/tests/test_graph/test_ops_embedding.py

@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from openvino import Type
+from openvino.runtime import opset15
+
+
+def test_embedding_bag_offsets_15():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets)
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"
+
+
+def test_embedding_bag_offsets_15_default_index():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+    default_index = opset15.parameter([], name="default_index", dtype=np.int64)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets, default_index, reduction="MeAn")
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "mean"
+
+
+def test_embedding_bag_offsets_15_per_sample_weights():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+    per_sample_weights = opset15.parameter([4], name="per_sample_weights", dtype=np.float32)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets, per_sample_weights=per_sample_weights, reduction="SUM")
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"
+
+
+def test_embedding_bag_offsets_15_default_index_per_sample_weights():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+    default_index = opset15.parameter([], name="default_index", dtype=np.int64)
+    per_sample_weights = opset15.parameter([4], name="per_sample_weights", dtype=np.float32)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets, default_index, per_sample_weights, "sum")
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"
+
+
+def test_embedding_bag_packed_15():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([3, 3], name="indices", dtype=np.int64)
+
+    node = opset15.embedding_bag_packed(emb_table, indices, reduction="mEaN")
+
+    assert node.get_type_name() == "EmbeddingBagPacked"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "mean"
+
+
+def test_embedding_bag_packed_15_per_sample_weights():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([3, 3], name="indices", dtype=np.int64)
+    per_sample_weights = opset15.parameter([3, 3], name="per_sample_weights", dtype=np.float32)
+
+    node = opset15.embedding_bag_packed(emb_table, indices, per_sample_weights)
+
+    assert node.get_type_name() == "EmbeddingBagPacked"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"