Merge remote-tracking branch 'origin/master' into aprocter/cf-element-types

cmake/external_onnx.cmake

@@ -21,7 +21,7 @@ include(ExternalProject)
 # ONNX.proto definition version
 #------------------------------------------------------------------------------
 
-set(ONNX_VERSION 1.3.0)
+set(ONNX_VERSION 1.5.0)
 
 #------------------------------------------------------------------------------
 # Download and install libonnx ...
@@ -30,6 +30,9 @@ set(ONNX_VERSION 1.3.0)
 set(ONNX_GIT_REPO_URL https://github.com/onnx/onnx.git)
 set(ONNX_GIT_BRANCH rel-${ONNX_VERSION})
 
+add_definitions(-DONNX_BUILD_SHARED_LIBS=ON)
+add_definitions(-DONNX_ML=ON)
+
 ExternalProject_Add(
     ext_onnx
     PREFIX onnx
@@ -58,8 +61,8 @@ ExternalProject_Add(
 
 ExternalProject_Get_Property(ext_onnx SOURCE_DIR BINARY_DIR)
 
-set(ONNX_INCLUDE_DIR ${SOURCE_DIR}/onnx)
-set(ONNX_PROTO_INCLUDE_DIR ${BINARY_DIR}/onnx)
+set(ONNX_INCLUDE_DIR ${SOURCE_DIR})
+set(ONNX_PROTO_INCLUDE_DIR ${BINARY_DIR})
 if (WIN32)
     set(ONNX_LIBRARY ${BINARY_DIR}/${CMAKE_BUILD_TYPE}/onnx.lib)
     set(ONNX_PROTO_LIBRARY ${BINARY_DIR}/${CMAKE_BUILD_TYPE}/onnx_proto.lib)

doc/sphinx/source/python_api/_autosummary/ngraph.ops.rst

@@ -37,6 +37,7 @@ ngraph.ops
       equal
       exp
       floor
+      gelu
       get_output_element
       greater
       greater_eq

python/ngraph/__init__.py

@@ -50,6 +50,7 @@ from ngraph.ops import elu
 from ngraph.ops import equal
 from ngraph.ops import exp
 from ngraph.ops import floor
+from ngraph.ops import gelu
 from ngraph.ops import get_output_element
 from ngraph.ops import greater
 from ngraph.ops import greater_eq

python/ngraph/impl/op/__init__.py

@@ -74,6 +74,7 @@ from _pyngraph.op import Elu
 from _pyngraph.op import Equal
 from _pyngraph.op import Exp
 from _pyngraph.op import Floor
+from _pyngraph.op import Gelu
 from _pyngraph.op import GetOutputElement
 from _pyngraph.op import Greater
 from _pyngraph.op import GreaterEq

python/ngraph/ops.py

@@ -23,7 +23,7 @@ from ngraph.impl import AxisSet, AxisVector, Coordinate, CoordinateDiff, Functio
 from ngraph.impl.op import Abs, Acos, Add, And, Asin, ArgMax, ArgMin, Atan, AvgPool, \
     BatchNormTraining, BatchNormInference, Broadcast, Ceiling, Clamp, Concat, Constant, Convert, \
     Convolution, ConvolutionBackpropData, Cos, Cosh, Divide, Dot, Elu, Equal, Exp, Floor, \
-    GetOutputElement, Greater, GreaterEq, Less, LessEq, Log, LRN, Max, Maximum, MaxPool, \
+    Gelu, GetOutputElement, Greater, GreaterEq, Less, LessEq, Log, LRN, Max, Maximum, MaxPool, \
     Min, Minimum, Multiply, Negative, Not, NotEqual, OneHot, Or, Pad, Parameter, Product, \
     Power, Relu, ReplaceSlice, Reshape, Reverse, Select, Sign, Sin, Sinh, Slice, Softmax, \
     Sqrt, Subtract, Sum, Tan, Tanh, TopK
@@ -527,6 +527,24 @@ def convert(node, new_type, name=None):  # type: (Node, NumericType, str) -> Nod
     return Convert(node, new_element_type)
 
 
+@nameable_op
+def gelu(node, name=None):  # type: (NodeInput, str) -> Node
+    r"""Perform Gaussian Error Linear Unit operation element-wise on data from input node.
+
+    Computes GELU function:
+
+    .. math:: f(x) = 0.5\cdot x\cdot(1 + erf( \dfrac{x}{\sqrt{2}})
+
+    For more information refer to:
+    `Gaussian Error Linear Unit (GELU) <https://arxiv.org/pdf/1606.08415.pdf>`_
+
+    :param node: Input tensor. One of: input node, array or scalar.
+    :param name: Optional output node name.
+    :return: The new node performing a GELU operation on its input data element-wise.
+    """
+    return Gelu(as_node(node))
+
+
 @nameable_op
 def select(selection_node, input_node1, input_node2, name=None):
     # type: (Node, Node, Node, str) -> Node

python/pyngraph/ops/fused/gelu.cpp

+//*****************************************************************************
+// Copyright 2017-2019 Intel Corporation
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//*****************************************************************************
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include "ngraph/op/fused/gelu.hpp"
+#include "pyngraph/ops/fused/gelu.hpp"
+
+namespace py = pybind11;
+
+void regclass_pyngraph_op_Gelu(py::module m)
+{
+    py::class_<ngraph::op::Gelu, std::shared_ptr<ngraph::op::Gelu>, ngraph::op::Op> gelu(m, "Gelu");
+    gelu.doc() = "ngraph.impl.op.Gelu wraps ngraph::op::Gelu";
+    gelu.def(py::init<const std::shared_ptr<ngraph::Node>&>());
+}

python/pyngraph/ops/fused/gelu.hpp

+//*****************************************************************************
+// Copyright 2017-2019 Intel Corporation
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//*****************************************************************************
+
+#pragma once
+
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+void regclass_pyngraph_op_Gelu(py::module m);

python/pyngraph/ops/regmodule_pyngraph_op.cpp

@@ -54,6 +54,7 @@ void regmodule_pyngraph_op(py::module m_op)
     regclass_pyngraph_op_Equal(m_op);
     regclass_pyngraph_op_Exp(m_op);
     regclass_pyngraph_op_Floor(m_op);
+    regclass_pyngraph_op_Gelu(m_op);
     regclass_pyngraph_op_GetOutputElement(m_op);
     regclass_pyngraph_op_Greater(m_op);
     regclass_pyngraph_op_GreaterEq(m_op);

python/pyngraph/ops/regmodule_pyngraph_op.hpp

@@ -44,6 +44,7 @@
 #include "pyngraph/ops/floor.hpp"
 #include "pyngraph/ops/fused/clamp.hpp"
 #include "pyngraph/ops/fused/elu.hpp"
+#include "pyngraph/ops/fused/gelu.hpp"
 #include "pyngraph/ops/get_output_element.hpp"
 #include "pyngraph/ops/greater.hpp"
 #include "pyngraph/ops/greater_eq.hpp"

python/setup.py

@@ -184,6 +184,7 @@ sources = [
     'pyngraph/ops/equal.cpp',
     'pyngraph/ops/exp.cpp',
     'pyngraph/ops/floor.cpp',
+    'pyngraph/ops/fused/gelu.cpp',
     'pyngraph/ops/greater.cpp',
     'pyngraph/ops/greater_eq.cpp',
     'pyngraph/ops/less.cpp',

python/test/ngraph/test_ops_fused.py

@@ -19,7 +19,7 @@ import ngraph as ng
 from test.ngraph.util import get_runtime
 
 
-def test_elu_operator():
+def test_elu_operator_with_parameters():
     runtime = get_runtime()
 
     data_shape = [2, 2]
@@ -69,6 +69,38 @@ def test_elu_operator_with_scalar():
     assert np.allclose(result, expected)
 
 
+def test_gelu_operator_with_parameters():
+    runtime = get_runtime()
+
+    data_value = np.array([[-5, 1], [-2, 3]], dtype=np.float32)
+
+    data_shape = [2, 2]
+    parameter_data = ng.parameter(data_shape, name='Data', dtype=np.float32)
+
+    model = ng.gelu(parameter_data)
+    computation = runtime.computation(model, parameter_data)
+
+    result = computation(data_value)
+    expected = np.array([[-1.4901161e-06, 8.4134471e-01], [-4.5500278e-02, 2.9959502]],
+                        dtype=np.float32)
+    assert np.allclose(result, expected)
+
+
+def test_gelu_operator_with_array():
+    runtime = get_runtime()
+
+    data_value = np.array([[-5, 1], [-2, 3]], dtype=np.float32)
+
+    model = ng.gelu(data_value)
+    computation = runtime.computation(model)
+
+    result = computation()
+    expected = np.array([[-1.4901161e-06, 8.4134471e-01], [-4.5500278e-02, 2.9959502]],
+                        dtype=np.float32)
+
+    assert np.allclose(result, expected)
+
+
 def test_clamp_operator():
     runtime = get_runtime()
 
@@ -99,4 +131,5 @@ def test_clamp_operator_with_array():
 
     result = computation()
     expected = np.clip(data_value, min_value, max_value)
+
     assert np.allclose(result, expected)

src/ngraph/frontend/onnx_import/core/attribute.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "ngraph/except.hpp"
 #include "tensor.hpp"

src/ngraph/frontend/onnx_import/core/graph.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 #include <string>
 #include <vector>
 

src/ngraph/frontend/onnx_import/core/model.cpp

@@ -14,7 +14,7 @@
 // limitations under the License.
 //*****************************************************************************
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "model.hpp"
 #include "ngraph/log.hpp"

src/ngraph/frontend/onnx_import/core/model.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 #include <ostream>
 #include <string>
 #include <unordered_map>

src/ngraph/frontend/onnx_import/core/node.cpp

@@ -14,7 +14,7 @@
 // limitations under the License.
 //*****************************************************************************
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "attribute.hpp"
 #include "graph.hpp"

src/ngraph/frontend/onnx_import/core/tensor.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 #include <utility>
 #include <vector>
 

src/ngraph/frontend/onnx_import/core/value_info.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "ngraph/op/constant.hpp"
 #include "ngraph/op/parameter.hpp"

src/ngraph/frontend/onnx_import/utils/common.cpp

@@ -13,7 +13,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 //*****************************************************************************
-#include <onnx-ml.pb.h> // onnx types
+#include <onnx/onnx_pb.h> // onnx types
 
 #include "common.hpp"
 

src/ngraph/frontend/onnxifi/backend_manager.cpp

@@ -15,7 +15,7 @@
 //*****************************************************************************
 
 #include <cstdlib> // std::size_t, std::uintptr_t
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 #include <stdexcept> // std::invalid_agrument, std::out_of_rage
 
 #include "backend.hpp"

src/ngraph/frontend/onnxifi/backend_manager.hpp

@@ -19,7 +19,7 @@
 #include <cstddef> // std::size_t, std::uintptr_t
 #include <map>     // std::map
 #include <mutex>   // std::mutex
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 
 #include "backend.hpp"
 #include "ngraph/runtime/backend.hpp"

src/ngraph/frontend/onnxifi/exceptions.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 
 namespace ngraph
 {

src/ngraph/frontend/onnxifi/onnxifi.cpp

@@ -16,7 +16,7 @@
 
 #include <cstddef>
 #include <cstdint>
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 #include <stdexcept>
 
 #include "backend_manager.hpp"

src/ngraph/frontend/onnxifi/tensor.hpp

@@ -17,7 +17,7 @@
 #pragma once
 
 #include <memory>
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 
 #include "ngraph/runtime/backend.hpp"
 #include "ngraph/runtime/tensor.hpp"

src/ngraph/pass/constant_folding.cpp

@@ -21,7 +21,9 @@
 #include "ngraph/graph_util.hpp"
 #include "ngraph/op/abs.hpp"
 #include "ngraph/op/add.hpp"
+#include "ngraph/op/all.hpp"
 #include "ngraph/op/and.hpp"
+#include "ngraph/op/any.hpp"
 #include "ngraph/op/broadcast.hpp"
 #include "ngraph/op/ceiling.hpp"
 #include "ngraph/op/concat.hpp"
@@ -41,7 +43,9 @@
 #include "ngraph/op/greater_eq.hpp"
 #include "ngraph/op/less.hpp"
 #include "ngraph/op/less_eq.hpp"
+#include "ngraph/op/max.hpp"
 #include "ngraph/op/maximum.hpp"
+#include "ngraph/op/min.hpp"
 #include "ngraph/op/minimum.hpp"
 #include "ngraph/op/multiply.hpp"
 #include "ngraph/op/negative.hpp"
@@ -64,7 +68,9 @@
 #include "ngraph/pattern/op/label.hpp"
 #include "ngraph/runtime/reference/abs.hpp"
 #include "ngraph/runtime/reference/add.hpp"
+#include "ngraph/runtime/reference/all.hpp"
 #include "ngraph/runtime/reference/and.hpp"
+#include "ngraph/runtime/reference/any.hpp"
 #include "ngraph/runtime/reference/broadcast.hpp"
 #include "ngraph/runtime/reference/ceiling.hpp"
 #include "ngraph/runtime/reference/concat.hpp"
@@ -78,7 +84,9 @@
 #include "ngraph/runtime/reference/greater_eq.hpp"
 #include "ngraph/runtime/reference/less.hpp"
 #include "ngraph/runtime/reference/less_eq.hpp"
+#include "ngraph/runtime/reference/max.hpp"
 #include "ngraph/runtime/reference/maximum.hpp"
+#include "ngraph/runtime/reference/min.hpp"
 #include "ngraph/runtime/reference/minimum.hpp"
 #include "ngraph/runtime/reference/multiply.hpp"
 #include "ngraph/runtime/reference/negate.hpp"
@@ -1608,180 +1616,207 @@ void pass::ConstantFolding::construct_constant_reverse()
 }
 
 template <typename T>
-static shared_ptr<op::Constant> fold_constant_product_helper(shared_ptr<op::Constant> constant,
-                                                             const AxisSet& reduction_axes,
-                                                             const Shape& result_shape)
+static shared_ptr<op::Constant>
+    fold_constant_arithmetic_reduction_helper(shared_ptr<op::Constant> constant,
+                                              shared_ptr<Node> reduction_node)
 {
-    vector<T> out_vec(shape_size(result_shape));
+    vector<T> out_vec(shape_size(reduction_node->get_shape()));
 
-    runtime::reference::product<T>(constant->get_vector<T>().data(),
+    if (auto max = dynamic_pointer_cast<op::Max>(reduction_node))
+    {
+        runtime::reference::max<T>(constant->get_vector<T>().data(),
+                                   out_vec.data(),
+                                   constant->get_output_shape(0),
+                                   reduction_node->get_shape(),
+                                   max->get_reduction_axes());
+    }
+    else if (auto min = dynamic_pointer_cast<op::Min>(reduction_node))
+    {
+        runtime::reference::min<T>(constant->get_vector<T>().data(),
+                                   out_vec.data(),
+                                   constant->get_output_shape(0),
+                                   reduction_node->get_shape(),
+                                   min->get_reduction_axes());
+    }
+    else if (auto prod = dynamic_pointer_cast<op::Product>(reduction_node))
+    {
+        runtime::reference::product<T>(constant->get_vector<T>().data(),
+                                       out_vec.data(),
+                                       constant->get_output_shape(0),
+                                       reduction_node->get_shape(),
+                                       prod->get_reduction_axes());
+    }
+    else if (auto sum = dynamic_pointer_cast<op::Sum>(reduction_node))
+    {
+        runtime::reference::sum<T>(constant->get_vector<T>().data(),
                                    out_vec.data(),
                                    constant->get_output_shape(0),
-                                   result_shape,
-                                   reduction_axes);
+                                   reduction_node->get_shape(),
+                                   sum->get_reduction_axes());
+    }
+    else
+    {
+        NGRAPH_CHECK(false,
+                     "Internal nGraph error: Ops handled in "
+                     "fold_constant_arithmetic_reduction_helper must be consistent with those "
+                     "matched in construct_constant_arithmetic_reduction");
+    }
 
-    return make_shared<op::Constant>(constant->get_output_element_type(0), result_shape, out_vec);
+    return make_shared<op::Constant>(
+        reduction_node->get_output_element_type(0), reduction_node->get_shape(), out_vec);
 }
 
-static shared_ptr<op::Constant> fold_constant_product(shared_ptr<op::Constant> constant,
-                                                      const AxisSet& reduction_axes,
-                                                      const Shape& result_shape)
+static shared_ptr<op::Constant>
+    fold_constant_arithmetic_reduction(shared_ptr<op::Constant> constant,
+                                       shared_ptr<Node> reduction_node)
 {
     auto& input_element_type = constant->get_output_element_type(0);
 
     switch (input_element_type.get_type_enum())
     {
     case element::Type_t::undefined:
-        NGRAPH_CHECK(false, "Encountered 'undefined' element type in fold_constant_product");
+        NGRAPH_CHECK(false,
+                     "Encountered 'undefined' element type in fold_constant_arithmetic_reduction");
         break;
     case element::Type_t::dynamic:
-        NGRAPH_CHECK(false, "Encountered 'dynamic' element type in fold_constant_product");
+        NGRAPH_CHECK(false,
+                     "Encountered 'dynamic' element type in fold_constant_arithmetic_reduction");
         break;
     case element::Type_t::boolean:
-        return fold_constant_product_helper<char>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<char>(constant, reduction_node);
     case element::Type_t::bf16:
-        return fold_constant_product_helper<bfloat16>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<bfloat16>(constant, reduction_node);
     case element::Type_t::f16:
-        return fold_constant_product_helper<float16>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<float16>(constant, reduction_node);
     case element::Type_t::f32:
-        return fold_constant_product_helper<float>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<float>(constant, reduction_node);
     case element::Type_t::f64:
-        return fold_constant_product_helper<double>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<double>(constant, reduction_node);
     case element::Type_t::i8:
-        return fold_constant_product_helper<int8_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<int8_t>(constant, reduction_node);
     case element::Type_t::i16:
-        return fold_constant_product_helper<int16_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<int16_t>(constant, reduction_node);
     case element::Type_t::i32:
-        return fold_constant_product_helper<int32_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<int32_t>(constant, reduction_node);
     case element::Type_t::i64:
-        return fold_constant_product_helper<int64_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<int64_t>(constant, reduction_node);
     case element::Type_t::u8:
-        return fold_constant_product_helper<uint8_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<uint8_t>(constant, reduction_node);
     case element::Type_t::u16:
-        return fold_constant_product_helper<uint16_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<uint16_t>(constant, reduction_node);
     case element::Type_t::u32:
-        return fold_constant_product_helper<uint32_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<uint32_t>(constant, reduction_node);
     case element::Type_t::u64:
-        return fold_constant_product_helper<uint64_t>(constant, reduction_axes, result_shape);
+        return fold_constant_arithmetic_reduction_helper<uint64_t>(constant, reduction_node);
     }
 
     NGRAPH_UNREACHABLE("Unexpected switch case");
 }
 
-void pass::ConstantFolding::construct_constant_product()
+void pass::ConstantFolding::construct_constant_arithmetic_reduction()
 {
-    auto constant_label = make_shared<pattern::op::Label>(
+    auto constant_data_label = make_shared<pattern::op::Label>(
         element::i32, Shape{2, 3, 4}, pattern::has_class<op::Constant>());
-    auto convert_op = make_shared<op::Product>(constant_label, AxisSet{0, 1, 2});
-
-    auto constant_product_callback = [constant_label](pattern::Matcher& m) {
-        NGRAPH_DEBUG << "In callback for constant_product_callback against node = "
+    auto constant_axes_label =
+        make_shared<pattern::op::Label>(element::i64, Shape{2}, pattern::has_class<op::Constant>());
+    auto is_supported_reduction = [](std::shared_ptr<Node> n) {
+        return (pattern::has_class<op::Max>()(n) || pattern::has_class<op::Min>()(n) ||
+                pattern::has_class<op::Product>()(n) || pattern::has_class<op::Sum>()(n));
+    };
+    auto reduction =
+        std::make_shared<pattern::op::Any>(element::i32,
+                                           Shape{2},
+                                           is_supported_reduction,
+                                           NodeVector{constant_data_label, constant_axes_label});
+
+    auto constant_arithmetic_reduction_callback = [constant_data_label](pattern::Matcher& m) {
+        NGRAPH_DEBUG << "In callback for constant_arithmetic_reduction_callback against node = "
                      << m.get_match_root()->get_name();
 
         auto pattern_map = m.get_pattern_map();
 
-        auto constant_match = static_pointer_cast<op::Constant>(pattern_map[constant_label]);
-        auto product_match = static_pointer_cast<op::Product>(m.get_match_root());
+        auto constant_match = static_pointer_cast<op::Constant>(pattern_map[constant_data_label]);
+        auto reduction_match = m.get_match_root();
 
-        replace_node(m.get_match_root(),
-                     fold_constant_product(constant_match,
-                                           product_match->get_reduction_axes(),
-                                           product_match->get_output_shape(0)));
+        replace_node(reduction_match,
+                     fold_constant_arithmetic_reduction(constant_match, reduction_match));
         return true;
     };
 
-    auto convert_matcher =
-        make_shared<pattern::Matcher>(convert_op, "ConstantFolding.ConstantProduct");
-    this->add_matcher(convert_matcher, constant_product_callback, all_pass_property_off);
+    auto arithmetic_reduction_matcher =
+        make_shared<pattern::Matcher>(reduction, "ConstantFolding.ConstantArithmeticReduction");
+    this->add_matcher(arithmetic_reduction_matcher,
+                      constant_arithmetic_reduction_callback,
+                      all_pass_property_off);
 }
 
-// TODO(amprocte): Find a way to reduce duplication with Product. (The fact
-// that we bottom out in a reference call makes it a bit tricky.)
-template <typename T>
-static shared_ptr<op::Constant> fold_constant_sum_helper(shared_ptr<op::Constant> constant,
-                                                         const AxisSet& reduction_axes,
-                                                         const Shape& result_shape)
+static shared_ptr<op::Constant> fold_constant_logical_reduction(shared_ptr<op::Constant> constant,
+                                                                shared_ptr<Node> reduction_node)
 {
-    vector<T> out_vec(shape_size(result_shape));
-
-    runtime::reference::sum<T>(constant->get_vector<T>().data(),
-                               out_vec.data(),
-                               constant->get_output_shape(0),
-                               result_shape,
-                               reduction_axes);
-
-    return make_shared<op::Constant>(constant->get_output_element_type(0), result_shape, out_vec);
-}
+    vector<char> out_vec(shape_size(reduction_node->get_shape()));
 
-static shared_ptr<op::Constant> fold_constant_sum(shared_ptr<op::Constant> constant,
-                                                  const AxisSet& reduction_axes,
-                                                  const Shape& result_shape)
-{
-    auto& input_element_type = constant->get_output_element_type(0);
-
-    switch (input_element_type.get_type_enum())
+    if (auto all = dynamic_pointer_cast<::ngraph::op::All>(reduction_node))
     {
-    case element::Type_t::undefined:
-        NGRAPH_CHECK(false, "Encountered 'undefined' element type in fold_constant_sum");
-        break;
-    case element::Type_t::dynamic:
-        NGRAPH_CHECK(false, "Encountered 'dynamic' element type in fold_constant_sum");
-        break;
-    case element::Type_t::boolean:
-        return fold_constant_sum_helper<char>(constant, reduction_axes, result_shape);
-    case element::Type_t::bf16:
-        return fold_constant_sum_helper<bfloat16>(constant, reduction_axes, result_shape);
-    case element::Type_t::f16:
-        return fold_constant_sum_helper<float16>(constant, reduction_axes, result_shape);
-    case element::Type_t::f32:
-        return fold_constant_sum_helper<float>(constant, reduction_axes, result_shape);
-    case element::Type_t::f64:
-        return fold_constant_sum_helper<double>(constant, reduction_axes, result_shape);
-    case element::Type_t::i8:
-        return fold_constant_sum_helper<int8_t>(constant, reduction_axes, result_shape);
-    case element::Type_t::i16:
-        return fold_constant_sum_helper<int16_t>(constant, reduction_axes, result_shape);
-    case element::Type_t::i32:
-        return fold_constant_sum_helper<int32_t>(constant, reduction_axes, result_shape);
-    case element::Type_t::i64:
-        return fold_constant_sum_helper<int64_t>(constant, reduction_axes, result_shape);
-    case element::Type_t::u8:
-        return fold_constant_sum_helper<uint8_t>(constant, reduction_axes, result_shape);
-    case element::Type_t::u16:
-        return fold_constant_sum_helper<uint16_t>(constant, reduction_axes, result_shape);
-    case element::Type_t::u32:
-        return fold_constant_sum_helper<uint32_t>(constant, reduction_axes, result_shape);
-    case element::Type_t::u64:
-        return fold_constant_sum_helper<uint64_t>(constant, reduction_axes, result_shape);
+        runtime::reference::all(constant->get_vector<char>().data(),
+                                out_vec.data(),
+                                constant->get_output_shape(0),
+                                reduction_node->get_shape(),
+                                all->get_reduction_axes());
+    }
+    else if (auto any = dynamic_pointer_cast<::ngraph::op::Any>(reduction_node))
+    {
+        runtime::reference::any(constant->get_vector<char>().data(),
+                                out_vec.data(),
+                                constant->get_output_shape(0),
+                                reduction_node->get_shape(),
+                                any->get_reduction_axes());
+    }
+    else
+    {
+        NGRAPH_CHECK(false,
+                     "Internal nGraph error: Ops handled in "
+                     "fold_constant_logical_reduction must be consistent with those "
+                     "matched in construct_constant_logical_reduction");
     }
 
-    NGRAPH_UNREACHABLE("Unexpected switch case");
+    return make_shared<op::Constant>(
+        reduction_node->get_output_element_type(0), reduction_node->get_shape(), out_vec);
 }
 
-void pass::ConstantFolding::construct_constant_sum()
+void pass::ConstantFolding::construct_constant_logical_reduction()
 {
-    auto constant_label = make_shared<pattern::op::Label>(
-        element::i32, Shape{2, 3, 4}, pattern::has_class<op::Constant>());
-    auto convert_op = make_shared<op::Sum>(constant_label, AxisSet{0, 1, 2});
-
-    auto constant_sum_callback = [constant_label](pattern::Matcher& m) {
-        NGRAPH_DEBUG << "In callback for constant_sum_callback against node = "
+    auto constant_data_label = make_shared<pattern::op::Label>(
+        element::boolean, Shape{2, 3, 4}, pattern::has_class<op::Constant>());
+    auto constant_axes_label =
+        make_shared<pattern::op::Label>(element::i64, Shape{2}, pattern::has_class<op::Constant>());
+    auto is_supported_reduction = [](std::shared_ptr<Node> n) {
+        return (pattern::has_class<::ngraph::op::All>()(n) ||
+                pattern::has_class<::ngraph::op::Any>()(n));
+    };
+    auto reduction =
+        std::make_shared<pattern::op::Any>(element::i32,
+                                           Shape{2},
+                                           is_supported_reduction,
+                                           NodeVector{constant_data_label, constant_axes_label});
+
+    auto constant_logical_reduction_callback = [constant_data_label](pattern::Matcher& m) {
+        NGRAPH_DEBUG << "In callback for constant_logical_reduction_callback against node = "
                      << m.get_match_root()->get_name();
 
         auto pattern_map = m.get_pattern_map();
 
-        auto constant_match = static_pointer_cast<op::Constant>(pattern_map[constant_label]);
-        auto sum_match = static_pointer_cast<op::Sum>(m.get_match_root());
+        auto constant_match = static_pointer_cast<op::Constant>(pattern_map[constant_data_label]);
+        auto reduction_match = m.get_match_root();
 
-        replace_node(m.get_match_root(),
-                     fold_constant_sum(constant_match,
-                                       sum_match->get_reduction_axes(),
-                                       sum_match->get_output_shape(0)));
+        replace_node(reduction_match,
+                     fold_constant_logical_reduction(constant_match, reduction_match));
         return true;
     };
 
-    auto convert_matcher = make_shared<pattern::Matcher>(convert_op, "ConstantFolding.ConstantSum");
-    this->add_matcher(convert_matcher, constant_sum_callback, all_pass_property_off);
+    auto logical_reduction_matcher =
+        make_shared<pattern::Matcher>(reduction, "ConstantFolding.ConstantLogicalReduction");
+    this->add_matcher(
+        logical_reduction_matcher, constant_logical_reduction_callback, all_pass_property_off);
 }
 
 template <typename T>

src/ngraph/pass/constant_folding.hpp

@@ -42,8 +42,8 @@ public:
         CONVERT,
         SHAPE_OF,
         REVERSE,
-        PRODUCT,
-        SUM,
+        ARITHMETIC_REDUCTION,
+        LOGICAL_REDUCTION,
         CONCAT,
         GATHER,
         SLICE,
@@ -68,8 +68,8 @@ public:
         construct_constant_convert();
         construct_constant_shape_of();
         construct_constant_reverse();
-        construct_constant_product();
-        construct_constant_sum();
+        construct_constant_arithmetic_reduction();
+        construct_constant_logical_reduction();
         construct_constant_concat();
         construct_constant_gather();
         construct_constant_slice();
@@ -101,8 +101,12 @@ public:
             case CFTransformations::CONVERT: construct_constant_convert(); break;
             case CFTransformations::SHAPE_OF: construct_constant_shape_of(); break;
             case CFTransformations::REVERSE: construct_constant_reverse(); break;
-            case CFTransformations::PRODUCT: construct_constant_product(); break;
-            case CFTransformations::SUM: construct_constant_sum(); break;
+            case CFTransformations::ARITHMETIC_REDUCTION:
+                construct_constant_arithmetic_reduction();
+                break;
+            case CFTransformations::LOGICAL_REDUCTION:
+                construct_constant_logical_reduction();
+                break;
             case CFTransformations::CONCAT: construct_constant_concat(); break;
             case CFTransformations::GATHER: construct_constant_gather(); break;
             case CFTransformations::SLICE: construct_constant_slice(); break;
@@ -126,8 +130,8 @@ private:
     void construct_constant_convert();
     void construct_constant_shape_of();
     void construct_constant_reverse();
-    void construct_constant_product();
-    void construct_constant_sum();
+    void construct_constant_arithmetic_reduction();
+    void construct_constant_logical_reduction();
     void construct_constant_concat();
     void construct_constant_gather();
     void construct_constant_slice();

src/ngraph/runtime/cpu/op/batch_mat_mul_transpose.hpp

@@ -17,7 +17,6 @@
 #pragma once
 
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -35,7 +34,6 @@ namespace ngraph
         class BatchMatMulTranspose : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             /// \brief Constructs a batch of matmul product operation.

src/ngraph/runtime/cpu/op/batch_norm_relu.hpp

@@ -30,7 +30,6 @@ namespace ngraph
         class BatchNormTrainingRelu : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             CPU_BACKEND_API BatchNormTrainingRelu(double eps,
@@ -60,7 +59,6 @@ namespace ngraph
         class BatchNormInferenceRelu : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             BatchNormInferenceRelu(double eps,

src/ngraph/runtime/cpu/op/bounded_relu.hpp

@@ -19,7 +19,6 @@
 #include "ngraph/node.hpp"
 #include "ngraph/op/op.hpp"
 #include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -30,7 +29,6 @@ namespace ngraph
         class BoundedRelu : public ngraph::op::util::UnaryElementwiseArithmetic
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             /// \brief Constructs a BoundedRelu operation.

src/ngraph/runtime/cpu/op/conv_add.hpp

@@ -18,7 +18,6 @@
 
 #include "ngraph/op/convolution.hpp"
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -27,7 +26,6 @@ namespace ngraph
         class ConvolutionAdd : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             ConvolutionAdd(const std::shared_ptr<op::Convolution>& conv,

src/ngraph/runtime/cpu/op/conv_relu.hpp

@@ -28,7 +28,6 @@ namespace ngraph
         class ConvolutionRelu : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             CPU_BACKEND_API ConvolutionRelu(const std::shared_ptr<op::Convolution>& conv);

src/ngraph/runtime/cpu/op/convert_layout.hpp

@@ -35,7 +35,6 @@ namespace ngraph
                 class ConvertLayout : public ngraph::op::Op
                 {
                 public:
-                    CPU_BACKEND_API
                     static const std::string type_name;
                     const std::string& description() const override { return type_name; }
                     CPU_BACKEND_API ConvertLayout(

src/ngraph/runtime/cpu/op/deconv.hpp

@@ -18,7 +18,6 @@
 
 #include "ngraph/op/convolution.hpp"
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -28,7 +27,6 @@ namespace ngraph
         class DeconvolutionBias : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             /// \brief Constructs a batched-convolution data batch-backprop operation.

src/ngraph/runtime/cpu/op/dropout.hpp

@@ -17,7 +17,6 @@
 #pragma once
 
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 #include "ngraph/util.hpp"
 
 namespace ngraph
@@ -27,7 +26,6 @@ namespace ngraph
         class Dropout : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             Dropout(const Output<Node>& input,

src/ngraph/runtime/cpu/op/group_conv_bias.hpp

@@ -17,7 +17,6 @@
 #pragma once
 #include "ngraph/op/fused/group_conv.hpp"
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -28,7 +27,6 @@ namespace ngraph
         class GroupConvolutionBias : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             GroupConvolutionBias(const std::shared_ptr<op::GroupConvolution>& conv,

src/ngraph/runtime/cpu/op/halide_op.hpp

@@ -20,7 +20,6 @@
 #include <vector>
 
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -33,7 +32,6 @@ namespace ngraph
                 class HalideOp : public ngraph::op::Op
                 {
                 public:
-                    CPU_BACKEND_API
                     static const std::string type_name;
                     const std::string& description() const override { return type_name; }
                     HalideOp(const OutputVector& args,

src/ngraph/runtime/cpu/op/leaky_relu.hpp

@@ -19,7 +19,6 @@
 #include "ngraph/node.hpp"
 #include "ngraph/op/op.hpp"
 #include "ngraph/op/util/unary_elementwise_arithmetic.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -31,7 +30,6 @@ namespace ngraph
         class CPULeakyRelu : public ngraph::op::util::UnaryElementwiseArithmetic
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             /// \brief Constructs a CPULeakyRelu operation.

src/ngraph/runtime/cpu/op/lstm.hpp

@@ -17,7 +17,6 @@
 #pragma once
 
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 #include "ngraph/runtime/cpu/op/rnn_utils.hpp"
 #include "ngraph/util.hpp"
 
@@ -28,7 +27,6 @@ namespace ngraph
         class Lstm : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             // INPUTS:

src/ngraph/runtime/cpu/op/matmul_bias.hpp

@@ -27,7 +27,6 @@ namespace ngraph
         class MatmulBias : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             CPU_BACKEND_API MatmulBias(const Output<Node>& W,

src/ngraph/runtime/cpu/op/max_pool_with_indices.hpp

@@ -32,7 +32,6 @@ namespace ngraph
         class MaxPoolWithIndices : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             CPU_BACKEND_API MaxPoolWithIndices(const Output<Node>& arg,
@@ -68,7 +67,6 @@ namespace ngraph
         class MaxPoolWithIndicesBackprop : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             CPU_BACKEND_API MaxPoolWithIndicesBackprop(const Output<Node>& arg_forward,

src/ngraph/runtime/cpu/op/quantized_matmul.hpp

@@ -19,7 +19,6 @@
 #include <utility>
 
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 
 namespace ngraph
 {
@@ -28,7 +27,6 @@ namespace ngraph
         class QuantizedMatmul : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             QuantizedMatmul(const Output<Node>& data,

src/ngraph/runtime/cpu/op/rnn.hpp

@@ -48,7 +48,6 @@ namespace ngraph
         class Rnn : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             CPU_BACKEND_API Rnn(const Output<Node>& src_layer,

src/ngraph/runtime/cpu/op/sigmoid_mul.hpp

@@ -30,7 +30,6 @@ namespace ngraph
         class SigmoidMultiply : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             /// Defines valid function types
@@ -69,7 +68,6 @@ namespace ngraph
         class SigmoidMultiplyBackprop : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             typedef SigmoidMultiply::FunctionType FunctionType;

src/ngraph/runtime/cpu/op/update_slice.hpp

@@ -18,7 +18,6 @@
 
 #include "ngraph/coordinate.hpp"
 #include "ngraph/op/op.hpp"
-#include "ngraph/runtime/cpu/cpu_backend_visibility.h"
 #include "ngraph/strides.hpp"
 
 namespace ngraph
@@ -51,7 +50,6 @@ namespace ngraph
         class UpdateSlice : public Op
         {
         public:
-            CPU_BACKEND_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             /// \brief Constructs a tensor slice update operation.

src/ngraph/runtime/plaidml/plaidml_ops_convolution.hpp

@@ -39,7 +39,6 @@ namespace ngraph
 class ngraph::runtime::plaidml::op::Convolution final : public ngraph::op::Op
 {
 public:
-    NGRAPH_API
     static const std::string type_name;
     const std::string& description() const override { return type_name; }
     Convolution(std::shared_ptr<ngraph::op::Convolution> src,
@@ -66,7 +65,6 @@ private:
 class ngraph::runtime::plaidml::op::ConvolutionBackpropData final : public ngraph::op::Op
 {
 public:
-    NGRAPH_API
     static const std::string type_name;
     const std::string& description() const override { return type_name; }
     ConvolutionBackpropData(std::shared_ptr<ngraph::op::ConvolutionBackpropData> src,
@@ -93,7 +91,6 @@ private:
 class ngraph::runtime::plaidml::op::ConvolutionBackpropFilters final : public ngraph::op::Op
 {
 public:
-    NGRAPH_API
     static const std::string type_name;
     const std::string& description() const override { return type_name; }
     ConvolutionBackpropFilters(std::shared_ptr<ngraph::op::ConvolutionBackpropFilters> src,

src/ngraph/runtime/plaidml/plaidml_ops_implicit_broadcast.hpp

@@ -40,7 +40,6 @@ namespace ngraph
 class ngraph::runtime::plaidml::op::ImplicitBroadcast final : public ngraph::op::Op
 {
 public:
-    NGRAPH_API
     static const std::string type_name;
     const std::string& description() const override { return type_name; }
     ImplicitBroadcast(const Output<Node>& input, const Shape& shape);

src/ngraph/runtime/plaidml/plaidml_ops_replicate.hpp

@@ -39,7 +39,6 @@ namespace ngraph
 class ngraph::runtime::plaidml::op::Replicate final : public ngraph::op::Op
 {
 public:
-    NGRAPH_API
     static const std::string type_name;
     const std::string& description() const override { return type_name; }
     Replicate(const Output<Node>& arg, std::size_t replication_axis, std::size_t replication_count);

src/ngraph/runtime/plaidml/plaidml_ops_winograd.hpp

@@ -38,7 +38,6 @@ namespace ngraph
 class ngraph::runtime::plaidml::op::Winograd final : public ngraph::op::Op
 {
 public:
-    NGRAPH_API
     static const std::string type_name;
     const std::string& description() const override { return type_name; }
     Winograd(std::shared_ptr<Convolution> conv, const OutputVector& args);

src/ngraph/runtime/reference/max.hpp

@@ -36,7 +36,7 @@ namespace ngraph
                      const AxisSet& reduction_axes)
             {
                 T minval = std::numeric_limits<T>::has_infinity
-                               ? -std::numeric_limits<T>::infinity()
+                               ? T(-std::numeric_limits<T>::infinity())
                                : std::numeric_limits<T>::min();
 
                 CoordinateTransform output_transform(out_shape);

test/backend/fused_op.in.cpp

@@ -1540,8 +1540,7 @@ NGRAPH_TEST(${BACKEND_NAME}, group_conv_transpose)
             -0.0270785f,  -0.00680824f, -0.06650258f, 0.08004665f,  0.07918708f,  -0.0724144f,
             0.06256775f,  -0.17838378f, -0.18863615f, 0.20064656f,  0.133717f,    -0.06876295f,
             -0.06398046f, -0.00864975f, 0.19289537f,  -0.01490572f, -0.13673618f, 0.01949645f});
-    test_case.set_tolerance(3);
-    test_case.run();
+    test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, group_conv_transpose_output_shape)

test/constant_folding.cpp

@@ -434,6 +434,110 @@ TEST(constant_folding, const_sum)
     ASSERT_EQ(values_expected, values_out);
 }
 
+TEST(constant_folding, const_max)
+{
+    Shape input_shape{3, 3};
+
+    vector<int32_t> values_in{1, 2, 3, 4, 5, 6, 7, 8, 9};
+    auto constant = op::Constant::create(element::i32, input_shape, values_in);
+    auto convert = make_shared<op::Max>(constant, AxisSet{1});
+    auto f = make_shared<Function>(convert, ParameterVector{});
+
+    pass::Manager pass_manager;
+    pass_manager.register_pass<pass::ConstantFolding>();
+    pass_manager.run_passes(f);
+
+    ASSERT_EQ(count_ops_of_type<op::Max>(f), 0);
+    ASSERT_EQ(count_ops_of_type<op::Constant>(f), 1);
+
+    auto new_const =
+        std::dynamic_pointer_cast<op::Constant>(f->get_results().at(0)->get_argument(0));
+    ASSERT_TRUE(new_const);
+    auto values_out = new_const->get_vector<int32_t>();
+
+    vector<int32_t> values_expected{3, 6, 9};
+
+    ASSERT_EQ(values_expected, values_out);
+}
+
+TEST(constant_folding, const_min)
+{
+    Shape input_shape{3, 3};
+
+    vector<int32_t> values_in{1, 2, 3, 4, 5, 6, 7, 8, 9};
+    auto constant = op::Constant::create(element::i32, input_shape, values_in);
+    auto convert = make_shared<op::Min>(constant, AxisSet{1});
+    auto f = make_shared<Function>(convert, ParameterVector{});
+
+    pass::Manager pass_manager;
+    pass_manager.register_pass<pass::ConstantFolding>();
+    pass_manager.run_passes(f);
+
+    ASSERT_EQ(count_ops_of_type<op::Min>(f), 0);
+    ASSERT_EQ(count_ops_of_type<op::Constant>(f), 1);
+
+    auto new_const =
+        std::dynamic_pointer_cast<op::Constant>(f->get_results().at(0)->get_argument(0));
+    ASSERT_TRUE(new_const);
+    auto values_out = new_const->get_vector<int32_t>();
+
+    vector<int32_t> values_expected{1, 4, 7};
+
+    ASSERT_EQ(values_expected, values_out);
+}
+
+TEST(constant_folding, const_all)
+{
+    Shape input_shape{3, 3};
+
+    vector<char> values_in{0, 1, 1, 0, 1, 0, 1, 1, 1};
+    auto constant = op::Constant::create(element::boolean, input_shape, values_in);
+    auto convert = make_shared<op::All>(constant, AxisSet{1});
+    auto f = make_shared<Function>(convert, ParameterVector{});
+
+    pass::Manager pass_manager;
+    pass_manager.register_pass<pass::ConstantFolding>();
+    pass_manager.run_passes(f);
+
+    ASSERT_EQ(count_ops_of_type<op::All>(f), 0);
+    ASSERT_EQ(count_ops_of_type<op::Constant>(f), 1);
+
+    auto new_const =
+        std::dynamic_pointer_cast<op::Constant>(f->get_results().at(0)->get_argument(0));
+    ASSERT_TRUE(new_const);
+    auto values_out = new_const->get_vector<char>();
+
+    vector<char> values_expected{0, 0, 1};
+
+    ASSERT_EQ(values_expected, values_out);
+}
+
+TEST(constant_folding, const_any)
+{
+    Shape input_shape{3, 3};
+
+    vector<char> values_in{1, 0, 0, 1, 0, 1, 0, 0, 0};
+    auto constant = op::Constant::create(element::boolean, input_shape, values_in);
+    auto convert = make_shared<op::Any>(constant, AxisSet{1});
+    auto f = make_shared<Function>(convert, ParameterVector{});
+
+    pass::Manager pass_manager;
+    pass_manager.register_pass<pass::ConstantFolding>();
+    pass_manager.run_passes(f);
+
+    ASSERT_EQ(count_ops_of_type<op::Any>(f), 0);
+    ASSERT_EQ(count_ops_of_type<op::Constant>(f), 1);
+
+    auto new_const =
+        std::dynamic_pointer_cast<op::Constant>(f->get_results().at(0)->get_argument(0));
+    ASSERT_TRUE(new_const);
+    auto values_out = new_const->get_vector<char>();
+
+    vector<char> values_expected{1, 1, 0};
+
+    ASSERT_EQ(values_expected, values_out);
+}
+
 TEST(constant_folding, const_concat)
 {
     auto constant0 =

test/onnx/onnx_import_rnn.in.cpp

@@ -104,8 +104,7 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_with_clip)
 
     // We have to enlarge tolerance bits to 3 - it's only one bit more than default value.
     // The discrepancies may occur at most on 7th decimal position.
-    test_case.set_tolerance(3);
-    test_case.run();
+    test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
 }
 
 NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_mixed_seq)
@@ -144,8 +143,7 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_mixed_seq)
 
     // We have to enlarge tolerance bits to 3 - it's only one bit more than default value.
     // The discrepancies may occur at most on 7th decimal position.
-    test_case.set_tolerance(3);
-    test_case.run();
+    test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
 }
 
 NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_hardsigmoid_activation)
@@ -201,8 +199,7 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_hardsigmoid_activation)
     test_case.add_expected_output<float>(Shape{1, 1, 2}, {0.19017234f, 0.00356848f});
 
     // The discrepancies occur at most at 18th mantissa bit - 8th decimal position.
-    test_case.set_tolerance(6);
-    test_case.run();
+    test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 4);
 }
 
 NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_large_batch_no_clip)
@@ -307,8 +304,7 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_bdir_short_input_seq)
     test_case.add_expected_output<float>(Shape{2, 1, 2},
                                          {-0.0251062f, 0.0561262f, -0.0318928f, 0.0762679f});
 
-    test_case.set_tolerance(DEFAULT_FLOAT_TOLERANCE_BITS + 3);
-    test_case.run();
+    test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 3);
 }
 
 NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_mixed_seq_reverse)
@@ -353,6 +349,5 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_mixed_seq_reverse)
         Shape{1, 2, 3},
         {0.52497941f, 0.54983425f, 0.5744428f, 1.34960834f, 1.54772296f, 1.65633056f});
 
-    test_case.set_tolerance(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
-    test_case.run();
+    test_case.run(DEFAULT_FLOAT_TOLERANCE_BITS + 1);
 }

test/onnx/onnxifi.cpp

@@ -17,7 +17,7 @@
 #include <cstring>
 
 #include <gtest/gtest.h>
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 
 #include "ngraph/runtime/backend_manager.hpp"
 

test/util/test_case.cpp

@@ -19,8 +19,9 @@
 #include "gtest/gtest.h"
 #include "ngraph/assertion.hpp"
 
-void ngraph::test::NgraphTestCase::run()
+void ngraph::test::NgraphTestCase::run(size_t tolerance_bits)
 {
+    m_tolerance_bits = tolerance_bits;
     const auto& function_results = m_function->get_results();
     NGRAPH_CHECK(m_expected_outputs.size() == function_results.size(),
                  "Expected number of outputs is different from the function's number of results.");
@@ -52,12 +53,6 @@ void ngraph::test::NgraphTestCase::run()
     }
 }
 
-ngraph::test::NgraphTestCase& ngraph::test::NgraphTestCase::set_tolerance(int tolerance_bits)
-{
-    m_tolerance_bits = tolerance_bits;
-    return *this;
-}
-
 ngraph::test::NgraphTestCase& ngraph::test::NgraphTestCase::dump_results(bool dump)
 {
     m_dump_results = dump;

test/util/test_case.hpp

@@ -38,8 +38,6 @@ namespace ngraph
             {
             }
 
-            NgraphTestCase& set_tolerance(int tolerance_bits);
-
             /// \brief Makes the test case print the expected and computed values to the console. This should only be used for debugging purposes.
             ///
             /// Just before the assertion is done, the current test case will gather expected and computed values,
@@ -130,7 +128,7 @@ namespace ngraph
                 add_expected_output(expected_shape, value);
             }
 
-            void run();
+            void run(size_t tolerance_bits = DEFAULT_FLOAT_TOLERANCE_BITS);
 
         private:
             template <typename T>