[Opset15][Spec] Add and update specifications for EmbeddingBag ops (o…

…penvinotoolkit#25100)

### Details:
- *Add specification for EmbeddingBagOffsets-15 and
EmbeddingBagPacked-15*
- *Remove dead links and improve specification of
EmbeddingBagOffsetsSum-3 and EmbeddingBagPackedSum-3*

### Tickets:
 - *141862*
 - *141863*

docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-15.rst

@@ -0,0 +1,184 @@
+.. {#openvino_docs_ops_sparse_EmbeddingBagOffsets_15}
+
+EmbeddingBagOffsets
+======================
+
+
+.. meta::
+  :description: Learn about EmbeddingBagOffsets-15 - a sparse operation, which
+                can be performed on three required and two optional input tensors.
+
+**Versioned name**: *EmbeddingBagOffsets-15*
+
+**Category**: *Sparse*
+
+**Short description**: Computes sums or means of "bags" of embeddings, without instantiating the intermediate embeddings.
+
+**Detailed description**:
+
+Operation EmbeddingBagOffsets is an implementation of ``torch.nn.EmbeddingBag`` with indices and offsets inputs being 1D tensors.
+
+For each index in ``indices`` this operator gathers values from ``emb_table`` embedding table. Then values at indices in the range of the same bag (based on ``offset`` input) are reduced according to ``reduction`` attribute.
+
+Values in ``offsets`` define starting index in ``indices`` tensor of each "bag",
+e.g. ``offsets`` with value ``[0, 3, 4, 4, 6]`` define 5 "bags" containing ``[3, 1, 0, 2, num_indices-6]`` elements corresponding to ``[indices[0:3], indices[3:4], empty_bag, indices[4:6], indices[6:]]`` slices of indices per bag.
+
+EmbeddingBagOffsets is an equivalent to following NumPy snippet:
+
+.. code-block:: py
+
+    def embedding_bag_offsets(
+        emb_table: np.ndarray,
+        indices: np.ndarray,
+        offsets: np.ndarray,
+        default_index: Optional[int] = None,
+        per_sample_weights: Optional[np.ndarray] = None,
+        reduction: Literal["sum", "mean"] = "sum",
+    ):
+        assert (
+            reduction == "sum" or per_sample_weights is None
+        ), "Attribute per_sample_weights is only supported in sum reduction."
+        if per_sample_weights is None:
+            per_sample_weights = np.ones_like(indices)
+        embeddings = []
+        for emb_idx, emb_weight in zip(indices, per_sample_weights):
+            embeddings.append(emb_table[emb_idx] * emb_weight)
+        previous_offset = offsets[0]
+        bags = []
+        offsets = np.append(offsets, len(indices))
+        for bag_offset in offsets[1:]:
+            bag_size = bag_offset - previous_offset
+            if bag_size != 0:
+                embedding_bag = embeddings[previous_offset:bag_offset]
+                reduced_bag = np.add.reduce(embedding_bag)
+                if reduction == "mean":
+                    reduced_bag = reduced_bag / bag_size
+                bags.append(reduced_bag)
+            else:
+                # Empty bag case
+                if default_index is not None and default_index != -1:
+                    bags.append(emb_table[default_index])
+                else:
+                    bags.append(np.zeros(emb_table.shape[1:]))
+            previous_offset = bag_offset
+        return np.stack(bags, axis=0)
+
+
+**Attributes**:
+
+* *reduction*
+
+  * **Description**: reduction mode. 
+  * **Range of values**:
+
+    * sum - compute weighted sum, using corresponding values of ``per_sample_weights`` as weights if provided.
+    * mean - compute average of values in bag. Input ``per_sample_weights`` is not supported and will raise exception.
+
+  * **Type**: ``string``
+  * **Default value**: sum
+  * **Required**: *no*
+
+**Inputs**:
+
+* **1**: ``emb_table`` tensor containing the embedding lookup table of the module of shape ``[num_emb, emb_dim1, emb_dim2, ...]`` and  of type *T*. **Required.**
+* **2**: ``indices`` tensor of shape ``[num_indices]`` and of type *T_IND*. **Required.**
+* **3**: ``offsets`` tensor of shape ``[batch]`` and of type *T_IND* containing the starting index positions of each "bag" in ``indices``. Maximum value of offsets cannot be greater than length of ``indices``. **Required.**
+* **4**: ``default_index`` scalar of type *T_IND* containing default index in embedding table to fill empty "bags". If set to ``-1`` or not provided, empty "bags" are filled with zeros. Reverse indexing using negative values is not supported. **Optional.**
+* **5**: ``per_sample_weights`` tensor of the same shape as ``indices`` and of type *T*. Supported only when *reduction* attribute is set to ``"sum"``. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones. **Optional.**
+
+**Outputs**:
+
+* **1**: tensor of shape ``[batch, emb_dim1, emb_dim2, ...]`` and of type *T* containing embeddings for each bag.
+
+**Types**
+
+* *T*: any numeric type.
+* *T_IND*: ``int32`` or ``int64``.
+
+**Example**
+
+*Example 1: per_sample_weights are provided, default_index is set to 0 to fill empty bag with values gathered form emb_table on given index.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <data reduction="sum"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+           <port id="3"/>    <!-- default_index value is: 0 -->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.5, 0.5, 0.5] -->
+               <dim>4</dim>
+           </port>
+       </input>
+       <output>
+           <port id="5">     <!-- output value is: [[-1.05, -1.2], [-0.2, -0.6], [-0.1, 0.4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
+
+*Example 2: per_sample_weights are provided, default_index is set to -1 to fill empty bag with 0.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <data reduction="sum"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+           <port id="3"/>    <!-- default_index value is: -1 - fill empty bag with 0-->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.2, -2, 1] -->
+               <dim>4</dim>
+           </port>
+       </input>
+       <output>
+           <port id="5">     <!-- output value is: [[-0.48, -0.66], [0., 0.], [2.8, -3.7]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
+
+*Example 3: Example of reduction set to mean.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <data reduction="mean"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+       </input>
+       <output>
+           <port id="3">     <!-- output value is: [[-1.05, -1.2], [0., 0.], [-0.1, 0.4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>

docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-sum-3.rst

@@ -14,15 +14,56 @@ EmbeddingBagOffsetsSum
 
 **Short description**: Computes sums of "bags" of embeddings, without instantiating the intermediate embeddings.
 
-**Detailed description**: This is the second case of the PyTorch `EmbeddingBag <https://pytorch.org/docs/stable/nn.html#embeddingbag>`__ , it has indices in two 1D tensors provided as 2nd and 3rd inputs. For each index in ``indices`` this operator gets values from ``data`` embedding table and sums all values belonging to each bag. Values in ``offsets`` define starting index in ``indices`` tensor of each "bag", e.g. ``offsets`` with value ``[0,3,4,4,6]`` define 5 "bags" containing ``[3,1,0,2,n-6]`` elements.
+**Detailed description**:
+
+Operation EmbeddingBagOffsets is an implementation of ``torch.nn.EmbeddingBag`` with indices and offsets inputs being 1D tensors.
+
+For each index in ``indices`` this operator gathers values from ``emb_table`` embedding table. Then values at indices in the range of the same bag (based on ``offset`` input) are reduced according to ``reduction`` attribute.
+
+Values in ``offsets`` define starting index in ``indices`` tensor of each "bag",
+e.g. ``offsets`` with value ``[0, 3, 4, 4, 6]`` define 5 "bags" containing ``[3, 1, 0, 2, num_indices-6]`` elements corresponding to ``[indices[0:3], indices[3:4], empty_bag, indices[4:6], indices[6:]]`` slices of indices per bag.
+
+EmbeddingBagOffsetsSum is an equivalent to following NumPy snippet:
+
+.. code-block:: py
+
+    def embedding_bag_offsets(
+        emb_table: np.ndarray,
+        indices: np.ndarray,
+        offsets: np.ndarray,
+        default_index: Optional[int] = None,
+        per_sample_weights: Optional[np.ndarray] = None,
+    ):
+        if per_sample_weights is None:
+            per_sample_weights = np.ones_like(indices)
+        embeddings = []
+        for emb_idx, emb_weight in zip(indices, per_sample_weights):
+            embeddings.append(emb_table[emb_idx] * emb_weight)
+        previous_offset = offsets[0]
+        bags = []
+        offsets = np.append(offsets, len(indices))
+        for bag_offset in offsets[1:]:
+            bag_size = bag_offset - previous_offset
+            if bag_size != 0:
+                embedding_bag = embeddings[previous_offset:bag_offset]
+                reduced_bag = np.add.reduce(embedding_bag)
+                bags.append(reduced_bag)
+            else:
+                # Empty bag case
+                if default_index is not None and default_index != -1:
+                    bags.append(emb_table[default_index])
+                else:
+                    bags.append(np.zeros(emb_table.shape[1:]))
+            previous_offset = bag_offset
+        return np.stack(bags, axis=0)
 
 **Attributes**: EmbeddingBagOffsetsSum operation has no attributes.
 
 **Inputs**:
 
 * **1**: ``emb_table`` tensor containing the embedding lookup table of the module of shape ``[num_emb, emb_dim1, emb_dim2, ...]`` and  of type *T*. **Required.**
 * **2**: ``indices`` tensor of shape ``[num_indices]`` and of type *T_IND*. **Required.**
-* **3**: ``offsets`` tensor of shape ``[batch]`` and of type *T_IND* containing the starting index positions of each "bag" in ``indices``. **Required.**
+* **3**: ``offsets`` tensor of shape ``[batch]`` and of type *T_IND* containing the starting index positions of each "bag" in ``indices``. Maximum value of offsets cannot be greater than length of ``indices``. **Required.**
 * **4**: ``default_index`` scalar of type *T_IND* containing default index in embedding table to fill empty "bags". If set to ``-1`` or not provided, empty "bags" are filled with zeros. Reverse indexing using negative values is not supported. **Optional.**
 * **5**: ``per_sample_weights`` tensor of the same shape as ``indices`` and of type *T*. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones. **Optional.**
 
@@ -37,7 +78,9 @@ EmbeddingBagOffsetsSum
 
 **Example**
 
-.. code-block:: cpp
+*Example 1: per_sample_weights are provided, default_index is set to 0 to fill empty bag with values gathered form emb_table on given index.*
+
+.. code-block:: xml
 
    <layer ... type="EmbeddingBagOffsetsSum" ... >
        <input>
@@ -52,7 +95,7 @@ EmbeddingBagOffsetsSum
                <dim>3</dim>
            </port>
            <port id="3"/>    <!-- default_index value is: 0 -->
-           <port id="4"/>    <!-- per_sample_weigths value is: [0.5, 0.5, 0.5, 0.5] -->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.5, 0.5, 0.5] -->
                <dim>4</dim>
            </port>
        </input>
@@ -64,4 +107,31 @@ EmbeddingBagOffsetsSum
        </output>
    </layer>
 
+*Example 2: per_sample_weights are provided, default_index is set to -1 to fill empty bag with 0.*
+
+.. code-block:: xml
 
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+           <port id="3"/>    <!-- default_index value is: -1 - fill empty bag with 0-->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.5, 0.5, 0.5] -->
+               <dim>4</dim>
+           </port>
+       </input>
+       <output>
+           <port id="5">     <!-- output value is: [[-1.05, -1.2], [0., 0.], [-0.1, 0.4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>