Softmax-8 python API (#9057)

* added ngraph api for softmax-8

* Updated comments

src/bindings/python/src/compatibility/ngraph/opset8/__init__.py

@@ -146,7 +146,7 @@ from ngraph.opset1.ops import sign
 from ngraph.opset1.ops import sin
 from ngraph.opset1.ops import sinh
 from ngraph.opset8.ops import slice
-from ngraph.opset1.ops import softmax
+from ngraph.opset8.ops import softmax
 from ngraph.opset4.ops import softplus
 from ngraph.opset2.ops import space_to_batch
 from ngraph.opset1.ops import space_to_depth

src/bindings/python/src/compatibility/ngraph/opset8/ops.py

@@ -777,3 +777,16 @@ def detection_output(
     inputs = as_nodes(*inputs)
 
     return _get_node_factory_opset8().create("DetectionOutput", inputs, attrs)
+
+
+@nameable_op
+def softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node:
+    """
+    Apply softmax operation on each element of input tensor.
+
+    @param data: The tensor providing input data.
+    @param axis: An axis along which Softmax should be calculated. Can be positive or negative.
+    @param name: Optional name for the node
+    @return The new node with softmax operation applied on each element.
+    """
+    return _get_node_factory_opset8().create("Softmax", [as_node(data)], {"axis": axis})

src/bindings/python/src/openvino/runtime/opset8/__init__.py

@@ -146,7 +146,7 @@ from openvino.runtime.opset1.ops import sign
 from openvino.runtime.opset1.ops import sin
 from openvino.runtime.opset1.ops import sinh
 from openvino.runtime.opset8.ops import slice
-from openvino.runtime.opset1.ops import softmax
+from openvino.runtime.opset8.ops import softmax
 from openvino.runtime.opset4.ops import softplus
 from openvino.runtime.opset2.ops import space_to_batch
 from openvino.runtime.opset1.ops import space_to_depth

src/bindings/python/src/openvino/runtime/opset8/ops.py

@@ -778,3 +778,16 @@ def detection_output(
     inputs = as_nodes(*inputs)
 
     return _get_node_factory_opset8().create("DetectionOutput", inputs, attrs)
+
+
+@nameable_op
+def softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node:
+    """
+    Apply softmax operation on each element of input tensor.
+
+    @param data: The tensor providing input data.
+    @param axis: An axis along which Softmax should be calculated. Can be positive or negative.
+    @param name: Optional name for the node.
+    @return The new node with softmax operation applied on each element.
+    """
+    return _get_node_factory_opset8().create("Softmax", [as_node(data)], {"axis": axis})

src/bindings/python/tests/test_ngraph/test_ops_unary.py

@@ -99,7 +99,7 @@ def test_sigmoid():
     assert np.allclose(result, expected)
 
 
-def test_softmax():
+def test_softmax_positive_axis():
     axis = 1
     input_tensor = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
 
@@ -110,6 +110,17 @@ def test_softmax():
     assert np.allclose(result, expected)
 
 
+def test_softmax_negative_axis():
+    axis = -1
+    input_tensor = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
+
+    result = run_op_node([input_tensor], ov.softmax, axis)
+
+    expected = [[0.09003056, 0.24472842, 0.6652409], [0.09003056, 0.24472842, 0.6652409]]
+
+    assert np.allclose(result, expected)
+
+
 def test_erf():
     input_tensor = np.array([-1.0, 0.0, 1.0, 2.5, 3.14, 4.0], dtype=np.float32)
     expected = [-0.842701, 0.0, 0.842701, 0.999593, 0.999991, 1.0]

src/bindings/python/tests_compatibility/test_ngraph/test_ops_unary.py

@@ -99,7 +99,7 @@ def test_sigmoid():
     assert np.allclose(result, expected)
 
 
-def test_softmax():
+def test_softmax_positive_axis():
     axis = 1
     input_tensor = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
 
@@ -110,6 +110,17 @@ def test_softmax():
     assert np.allclose(result, expected)
 
 
+def test_softmax_negative_axis():
+    axis = -1
+    input_tensor = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
+
+    result = run_op_node([input_tensor], ng.softmax, axis)
+
+    expected = [[0.09003056, 0.24472842, 0.6652409], [0.09003056, 0.24472842, 0.6652409]]
+
+    assert np.allclose(result, expected)
+
+
 def test_erf():
     input_tensor = np.array([-1.0, 0.0, 1.0, 2.5, 3.14, 4.0], dtype=np.float32)
     expected = [-0.842701, 0.0, 0.842701, 0.999593, 0.999991, 1.0]