Merge branch 'master' into krovatkin/min_gcc_ver

src/ngraph/CMakeLists.txt

@@ -38,6 +38,7 @@ set (SRC
     ops/reduce.cpp
     ops/reshape.cpp
     ops/select.cpp
+    ops/slice.cpp
     ops/tuple.cpp
     ops/unary_elementwise_arithmetic.cpp
     ops/unary_elementwise_builtin.cpp

src/ngraph/common.hpp

@@ -43,6 +43,9 @@ namespace ngraph
     /// @brief A set of axes, for example, reduction axes
     using AxisSet = std::set<size_t>;
 
+    /// @brief Coordinate in a tensor
+    using Coordinate = std::vector<size_t>;
+
     /// @brief Shape for a tensor
     using Shape = std::vector<size_t>;
 

src/ngraph/ngraph.hpp

@@ -79,6 +79,7 @@
 #include "ngraph/ops/remainder.hpp"
 #include "ngraph/ops/reshape.hpp"
 #include "ngraph/ops/select.hpp"
+#include "ngraph/ops/slice.hpp"
 #include "ngraph/ops/subtract.hpp"
 #include "ngraph/ops/tuple.hpp"
 #include "ngraph/runtime/backend.hpp"

src/ngraph/ops/slice.cpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// 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
+// ----------------------------------------------------------------------------
+
+#include "ngraph/ops/slice.hpp"
+
+using namespace std;
+using namespace ngraph::op;
+
+void Slice::propagate_types()
+{
+    if (m_arguments.size() != 1)
+    {
+        throw ngraph_error("Wrong number of arguments.");
+    }
+
+    auto arg_type = m_arguments.at(0)->get_value_type();
+    if (nullptr == arg_type)
+    {
+        throw ngraph_error("Argument to slice is missing type.");
+    }
+    auto arg_tensor_view_type = dynamic_pointer_cast<const TensorViewType>(arg_type);
+    if (nullptr == arg_tensor_view_type)
+    {
+        throw ngraph_error("Argument to slice is not a tensor view");
+    }
+    auto& arg_shape = arg_tensor_view_type->get_shape();
+
+    if (m_lower_bounds.size() != arg_shape.size())
+    {
+        throw ngraph_error(
+            "Number of lower bounds provided for slice does not match number of input axes");
+    }
+
+    if (m_upper_bounds.size() != arg_shape.size())
+    {
+        throw ngraph_error(
+            "Number of upper bounds provided for slice does not match number of input axes");
+    }
+
+    if (m_step.size() != arg_shape.size())
+    {
+        throw ngraph_error(
+            "Number of step axes provided for slice does not match number of input axes");
+    }
+
+    Shape result_shape;
+
+    for (size_t i = 0; i < arg_shape.size(); i++)
+    {
+        if (m_upper_bounds[i] > arg_shape[i])
+        {
+            throw ngraph_error("Upper bound for slice is out of range");
+        }
+
+        if (m_lower_bounds[i] > m_upper_bounds[i])
+        {
+            throw ngraph_error("Lower bound for slice is greater than upper bound");
+        }
+
+        if (0 == m_step[i])
+        {
+            throw ngraph_error("Step distance for slice is zero");
+        }
+
+        size_t result_axis_size = m_upper_bounds[i] - m_lower_bounds[i];
+        result_axis_size =
+            result_axis_size / m_step[i] + ((result_axis_size % m_step[i] == 0) ? 0 : 1);
+        result_shape.push_back(result_axis_size);
+    }
+
+    set_value_type_checked(
+        make_shared<TensorViewType>(arg_tensor_view_type->get_element_type(), result_shape));
+}

src/ngraph/ops/slice.hpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// 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
+// ----------------------------------------------------------------------------
+
+#pragma once
+
+#include "ngraph/ops/op.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        class Slice : public Builtin
+        {
+        public:
+            ///
+            /// @param arg The tensor view to be sliced.
+            /// @param lower_bounds The axiswise lower bounds of the slice.
+            /// @param upper_bounds The axiswise upper bounds of the slice (exclusive).
+            /// @param step The slicing step; for example, step of {n,m} means to take
+            ///             every nth row and everyth mth column of the input matrix.
+            ///
+            Slice(const std::shared_ptr<Node>& arg,
+                  const Coordinate& lower_bounds,
+                  const Coordinate& upper_bounds,
+                  const Shape& step)
+                : Builtin({arg})
+                , m_lower_bounds(lower_bounds)
+                , m_upper_bounds(upper_bounds)
+                , m_step(step)
+            {
+            }
+
+            Slice(const std::shared_ptr<Node>& arg,
+                  const Coordinate& lower_bounds,
+                  const Coordinate& upper_bounds)
+                : Builtin({arg})
+                , m_lower_bounds(lower_bounds)
+                , m_upper_bounds(upper_bounds)
+                , m_step(Shape(lower_bounds.size(), 1))
+            {
+            }
+
+            virtual std::string description() const override { return "Slice"; }
+            virtual void propagate_types() override;
+
+            const Coordinate& get_lower_bounds() const { return m_lower_bounds; }
+            const Coordinate& get_upper_bounds() const { return m_upper_bounds; }
+            const Shape& get_step() const { return m_step; }
+        protected:
+            const Coordinate m_lower_bounds;
+            const Coordinate m_upper_bounds;
+            const Shape m_step;
+        };
+    }
+}

src/ngraph/runtime/ngvm/eigen/matrix_slice.hpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// 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
+// ----------------------------------------------------------------------------
+
+#pragma once
+
+#include "ngraph/runtime/ngvm/call_frame.hpp"
+#include "ngraph/runtime/ngvm/eigen/utils.hpp"
+#include "ngraph/runtime/ngvm/instruction.hpp"
+#include "ngraph/runtime/tensor_view.hpp"
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace ngvm
+        {
+            namespace eigen
+            {
+                template <typename ET>
+                class MatrixSliceInstruction : public Instruction
+                {
+                public:
+                    MatrixSliceInstruction(const TensorViewInfo& arg,
+                                           const TensorViewInfo& out,
+                                           size_t lower_row,
+                                           size_t lower_col,
+                                           size_t upper_row,
+                                           size_t upper_col)
+                        : m_arg(arg)
+                        , m_out(out)
+                        , m_lower_row(lower_row)
+                        , m_lower_col(lower_col)
+                        , m_upper_row(upper_row)
+                        , m_upper_col(upper_col)
+                    {
+                    }
+
+                    virtual void execute(CallFrame& call_frame) const override
+                    {
+                        EigenMatrix<ET>(call_frame, m_out) = EigenMatrix<ET>(call_frame, m_arg)
+                                                                 .block(m_lower_row,
+                                                                        m_lower_col,
+                                                                        m_upper_row - m_lower_row,
+                                                                        m_upper_col - m_lower_col);
+                    }
+
+                protected:
+                    TensorViewInfo m_arg;
+                    TensorViewInfo m_out;
+                    size_t m_lower_row;
+                    size_t m_lower_col;
+                    size_t m_upper_row;
+                    size_t m_upper_col;
+                };
+            }
+        }
+    }
+}

src/ngraph/runtime/ngvm/eigen/vector_slice.hpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// 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
+// ----------------------------------------------------------------------------
+
+#pragma once
+
+#include "ngraph/runtime/ngvm/call_frame.hpp"
+#include "ngraph/runtime/ngvm/eigen/utils.hpp"
+#include "ngraph/runtime/ngvm/instruction.hpp"
+#include "ngraph/runtime/tensor_view.hpp"
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace ngvm
+        {
+            namespace eigen
+            {
+                template <typename ET>
+                class VectorSliceInstruction : public Instruction
+                {
+                public:
+                    VectorSliceInstruction(const TensorViewInfo& arg,
+                                           const TensorViewInfo& out,
+                                           size_t lower,
+                                           size_t upper)
+                        : m_arg(arg)
+                        , m_out(out)
+                        , m_lower(lower)
+                        , m_upper(upper)
+                    {
+                    }
+
+                    virtual void execute(CallFrame& call_frame) const override
+                    {
+                        EigenVector<ET>(call_frame, m_out) =
+                            EigenVector<ET>(call_frame, m_arg).segment(m_lower, m_upper - m_lower);
+                    }
+
+                protected:
+                    TensorViewInfo m_arg;
+                    TensorViewInfo m_out;
+                    size_t m_lower;
+                    size_t m_upper;
+                };
+            }
+        }
+    }
+}

src/ngraph/runtime/ngvm/external_function.cpp

@@ -47,6 +47,7 @@
 #include "ngraph/ops/reduce.hpp"
 #include "ngraph/ops/reshape.hpp"
 #include "ngraph/ops/select.hpp"
+#include "ngraph/ops/slice.hpp"
 #include "ngraph/ops/subtract.hpp"
 #include "ngraph/ops/tuple.hpp"
 #include "ngraph/pass/assign_tensors.hpp"
@@ -74,6 +75,7 @@
 #include "ngraph/runtime/ngvm/eigen/less_than.hpp"
 #include "ngraph/runtime/ngvm/eigen/log.hpp"
 #include "ngraph/runtime/ngvm/eigen/matrix_mult.hpp"
+#include "ngraph/runtime/ngvm/eigen/matrix_slice.hpp"
 #include "ngraph/runtime/ngvm/eigen/matrix_transpose.hpp"
 #include "ngraph/runtime/ngvm/eigen/matrix_vector_product.hpp"
 #include "ngraph/runtime/ngvm/eigen/maximum.hpp"
@@ -87,6 +89,7 @@
 #include "ngraph/runtime/ngvm/eigen/scalar_tensor_product.hpp"
 #include "ngraph/runtime/ngvm/eigen/select.hpp"
 #include "ngraph/runtime/ngvm/eigen/subtract.hpp"
+#include "ngraph/runtime/ngvm/eigen/vector_slice.hpp"
 #include "ngraph/runtime/ngvm/external_function.hpp"
 #include "ngraph/runtime/utils.hpp"
 
@@ -835,6 +838,67 @@ ExternalFunction::OpMap& ExternalFunction::get_op_map()
             }
         };
 
+        REGISTER_TO_OP_MAP(op::Slice)
+        {
+            auto slice = static_cast<const op::Slice*>(n);
+
+            for (auto d : slice->get_step())
+            {
+                if (1 != d)
+                {
+                    throw ngraph_error("Slice does not support non-unit step yet in the VM");
+                }
+            }
+
+            auto arg_type = slice->get_arguments().at(0)->get_value_type();
+            auto arg_tensor_view_type = dynamic_pointer_cast<const TensorViewType>(arg_type);
+            assert(nullptr != arg_tensor_view_type);
+            auto arg_shape = arg_tensor_view_type->get_shape();
+            auto arg_rank = arg_shape.size();
+            auto& arg_element_type = arg_tensor_view_type->get_element_type();
+
+            auto& lower_bounds = slice->get_lower_bounds();
+            auto& upper_bounds = slice->get_upper_bounds();
+
+            // Scalar slice is necessarily just a copy.
+            if (arg_rank == 0)
+            {
+                PUSH_POLYMORPHIC_INSTRUCTION(arg_element_type,
+                                             "Slice has unhandled element type",
+                                             runtime::ngvm::eigen::CopyInstruction,
+                                             in.at(0).get_index(),
+                                             out.at(0).get_index());
+            }
+            else if (arg_rank == 1)
+            {
+                PUSH_POLYMORPHIC_INSTRUCTION(arg_element_type,
+                                             "Slice has unhandled element type",
+                                             runtime::ngvm::eigen::VectorSliceInstruction,
+                                             in[0],
+                                             out[0],
+                                             lower_bounds[0],
+                                             upper_bounds[0]);
+            }
+            else if (arg_rank == 2)
+            {
+                PUSH_POLYMORPHIC_INSTRUCTION(arg_element_type,
+                                             "Slice has unhandled element type",
+                                             runtime::ngvm::eigen::MatrixSliceInstruction,
+                                             in[0],
+                                             out[0],
+                                             lower_bounds[0],
+                                             lower_bounds[1],
+                                             upper_bounds[0],
+                                             upper_bounds[1]);
+            }
+
+            // Other cases (reordering of axes for tensors with rank>2) are not handled yet.
+            else
+            {
+                throw ngraph_error("Slice is not implemented yet for tensors with rank>2 in VM");
+            }
+        };
+
         initialized = true;
     }
     return op_map;

test/execute.cpp

@@ -1621,7 +1621,7 @@ TEST(execute, reduce_matrix_to_scalar_zero_by_zero)
     ASSERT_EQ((vector<float>{99}), b->get_vector());
 }
 
-TEST(type_prop, reshape_t2v_012)
+TEST(execute, reshape_t2v_012)
 {
     auto shape_a = Shape{2, 2, 3};
     auto A = make_shared<op::Parameter>(
@@ -1645,7 +1645,7 @@ TEST(type_prop, reshape_t2v_012)
     ASSERT_EQ((vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}), result->get_vector());
 }
 
-TEST(type_prop, reshape_t2s_012)
+TEST(execute, reshape_t2s_012)
 {
     auto shape_a = Shape{1, 1, 1};
     auto A = make_shared<op::Parameter>(
@@ -1669,7 +1669,7 @@ TEST(type_prop, reshape_t2s_012)
     ASSERT_EQ((vector<float>{6}), result->get_vector());
 }
 
-TEST(type_prop, reshape_t2s_120)
+TEST(execute, reshape_t2s_120)
 {
     auto shape_a = Shape{1, 1, 1};
     auto A = make_shared<op::Parameter>(
@@ -1693,7 +1693,7 @@ TEST(type_prop, reshape_t2s_120)
     ASSERT_EQ((vector<float>{6}), result->get_vector());
 }
 
-TEST(type_prop, reshape_s2t)
+TEST(execute, reshape_s2t)
 {
     auto shape_a = Shape{};
     auto A = make_shared<op::Parameter>(
@@ -1717,7 +1717,7 @@ TEST(type_prop, reshape_s2t)
     ASSERT_EQ((vector<float>{42}), result->get_vector());
 }
 
-TEST(type_prop, reshape_v2m_col)
+TEST(execute, reshape_v2m_col)
 {
     auto shape_a = Shape{3};
     auto A = make_shared<op::Parameter>(
@@ -1741,7 +1741,7 @@ TEST(type_prop, reshape_v2m_col)
     ASSERT_EQ((vector<float>{1, 2, 3}), result->get_vector());
 }
 
-TEST(type_prop, reshape_v2m_row)
+TEST(execute, reshape_v2m_row)
 {
     auto shape_a = Shape{3};
     auto A = make_shared<op::Parameter>(
@@ -1765,7 +1765,7 @@ TEST(type_prop, reshape_v2m_row)
     ASSERT_EQ((vector<float>{1, 2, 3}), result->get_vector());
 }
 
-TEST(type_prop, reshape_v2t_middle)
+TEST(execute, reshape_v2t_middle)
 {
     auto shape_a = Shape{3};
     auto A = make_shared<op::Parameter>(
@@ -1789,7 +1789,7 @@ TEST(type_prop, reshape_v2t_middle)
     ASSERT_EQ((vector<float>{1, 2, 3}), result->get_vector());
 }
 
-TEST(type_prop, reshape_m2m_same)
+TEST(execute, reshape_m2m_same)
 {
     auto shape_a = Shape{3, 3};
     auto A = make_shared<op::Parameter>(
@@ -1813,7 +1813,7 @@ TEST(type_prop, reshape_m2m_same)
     ASSERT_EQ((vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9}), result->get_vector());
 }
 
-TEST(type_prop, reshape_m2m_transpose)
+TEST(execute, reshape_m2m_transpose)
 {
     auto shape_a = Shape{3, 3};
     auto A = make_shared<op::Parameter>(
@@ -1837,7 +1837,7 @@ TEST(type_prop, reshape_m2m_transpose)
     ASSERT_EQ((vector<float>{1, 4, 7, 2, 5, 8, 3, 6, 9}), result->get_vector());
 }
 
-TEST(type_prop, reshape_m2m_dim_change_transpose)
+TEST(execute, reshape_m2m_dim_change_transpose)
 {
     auto shape_a = Shape{3, 2};
     auto A = make_shared<op::Parameter>(
@@ -1883,3 +1883,75 @@ TEST(execute, exp)
         (vector<float>{expf(-4), expf(-3), expf(-2), expf(-1), expf(0), expf(1), expf(2), expf(3)}),
         result->get_vector());
 }
+
+TEST(execute, slice_scalar)
+{
+    auto shape_a = Shape{};
+    auto A = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), shape_a));
+    auto shape_r = Shape{};
+    auto rt = make_shared<TensorViewType>(element::Float32::element_type(), shape_r);
+    auto r = make_shared<op::Slice>(A, Coordinate{}, Coordinate{});
+    auto f = make_shared<Function>(r, rt, op::Parameters{A});
+
+    auto manager = runtime::Manager::get("NGVM");
+    auto external = manager->compile(f);
+    auto backend = manager->allocate_backend();
+    auto cf = backend->make_call_frame(external);
+
+    // Create some tensors for input/output
+    auto a = ngraph::runtime::make_tensor<element::Float32>(shape_a);
+    *a = vector<float>{312};
+    auto result = ngraph::runtime::make_tensor<element::Float32>(shape_r);
+
+    (*cf)({a}, {result});
+    ASSERT_EQ((vector<float>{312}), result->get_vector());
+}
+
+TEST(execute, slice_matrix)
+{
+    auto shape_a = Shape{4, 4};
+    auto A = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), shape_a));
+    auto shape_r = Shape{2, 3};
+    auto rt = make_shared<TensorViewType>(element::Float32::element_type(), shape_r);
+    auto r = make_shared<op::Slice>(A, Coordinate{0, 1}, Coordinate{3, 3});
+    auto f = make_shared<Function>(r, rt, op::Parameters{A});
+
+    auto manager = runtime::Manager::get("NGVM");
+    auto external = manager->compile(f);
+    auto backend = manager->allocate_backend();
+    auto cf = backend->make_call_frame(external);
+
+    // Create some tensors for input/output
+    auto a = ngraph::runtime::make_tensor<element::Float32>(shape_a);
+    *a = vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+    auto result = ngraph::runtime::make_tensor<element::Float32>(shape_r);
+
+    (*cf)({a}, {result});
+    ASSERT_EQ((vector<float>{2, 3, 6, 7, 10, 11}), result->get_vector());
+}
+
+TEST(execute, slice_vector)
+{
+    auto shape_a = Shape{16};
+    auto A = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), shape_a));
+    auto shape_r = Shape{12};
+    auto rt = make_shared<TensorViewType>(element::Float32::element_type(), shape_r);
+    auto r = make_shared<op::Slice>(A, Coordinate{2}, Coordinate{14});
+    auto f = make_shared<Function>(r, rt, op::Parameters{A});
+
+    auto manager = runtime::Manager::get("NGVM");
+    auto external = manager->compile(f);
+    auto backend = manager->allocate_backend();
+    auto cf = backend->make_call_frame(external);
+
+    // Create some tensors for input/output
+    auto a = ngraph::runtime::make_tensor<element::Float32>(shape_a);
+    *a = vector<float>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+    auto result = ngraph::runtime::make_tensor<element::Float32>(shape_r);
+
+    (*cf)({a}, {result});
+    ASSERT_EQ((vector<float>{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}), result->get_vector());
+}

test/type_prop.cpp

@@ -1235,3 +1235,285 @@ TEST(type_prop, reshape_deduce_wrong_output_shape)
         FAIL() << "Deduced type check failed for unexpected reason";
     }
 }
+
+TEST(type_prop, slice_deduce_vector)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{2}, Coordinate{5});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()), TensorViewType(element::Float32::element_type(), Shape{3}));
+}
+
+TEST(type_prop, slice_deduce_matrix)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{2, 1}, Coordinate{5, 7});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()),
+              TensorViewType(element::Float32::element_type(), Shape{3, 6}));
+}
+
+TEST(type_prop, slice_deduce_matrix_strided)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{2, 1}, Coordinate{5, 7}, Shape{3, 2});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()),
+              TensorViewType(element::Float32::element_type(), Shape{1, 3}));
+}
+
+TEST(type_prop, slice_deduce_matrix_strided_uneven)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{2, 1}, Coordinate{5, 7}, Shape{3, 4});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()),
+              TensorViewType(element::Float32::element_type(), Shape{1, 2}));
+}
+
+TEST(type_prop, slice_deduce_vector_edge)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0}, Coordinate{6});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()), TensorViewType(element::Float32::element_type(), Shape{6}));
+}
+
+TEST(type_prop, slice_deduce_matrix_edge)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 0}, Coordinate{6, 8});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()),
+              TensorViewType(element::Float32::element_type(), Shape{6, 8}));
+}
+
+TEST(type_prop, slice_deduce_matrix_zero_cols)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 0}, Coordinate{6, 0});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()),
+              TensorViewType(element::Float32::element_type(), Shape{6, 0}));
+}
+
+TEST(type_prop, slice_deduce_matrix_zero_zero)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 0}, Coordinate{0, 0});
+    sl->propagate_types();
+    ASSERT_EQ(*(sl->get_value_type()),
+              TensorViewType(element::Float32::element_type(), Shape{0, 0}));
+}
+
+TEST(type_prop, slice_deduce_vector_invalid_step)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0}, Coordinate{7}, Shape{1, 2});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Invalid slice step not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(
+            error.what(),
+            std::string(
+                "Number of step axes provided for slice does not match number of input axes"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_vector_edge_upper_oob)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0}, Coordinate{7});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Upper bound out of range not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(error.what(), std::string("Upper bound for slice is out of range"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_matrix_edge_upper_oob)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 0}, Coordinate{6, 9});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Upper bound out of range not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(error.what(), std::string("Upper bound for slice is out of range"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_vector_lower_above_upper)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{3}, Coordinate{2});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Lower bound above upper not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(error.what(), std::string("Lower bound for slice is greater than upper bound"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_matrix_lower_above_upper)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 5}, Coordinate{6, 4});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Lower bound above upper not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(error.what(), std::string("Lower bound for slice is greater than upper bound"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_matrix_lower_missing)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0}, Coordinate{5, 5});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Missing lower bound coordinate not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(
+            error.what(),
+            std::string(
+                "Number of lower bounds provided for slice does not match number of input axes"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_matrix_upper_missing)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 0}, Coordinate{5});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Missing upper bound coordinate not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(
+            error.what(),
+            std::string(
+                "Number of upper bounds provided for slice does not match number of input axes"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_matrix_lower_extra)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 0, 0}, Coordinate{5, 5});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Extra lower bound coordinate not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(
+            error.what(),
+            std::string(
+                "Number of lower bounds provided for slice does not match number of input axes"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, slice_deduce_matrix_upper_extra)
+{
+    auto param = make_shared<op::Parameter>(
+        make_shared<TensorViewType>(element::Float32::element_type(), Shape{6, 8}));
+    auto sl = make_shared<op::Slice>(param, Coordinate{0, 0}, Coordinate{5, 5, 5});
+    try
+    {
+        sl->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Extra upper bound coordinate not detected";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(
+            error.what(),
+            std::string(
+                "Number of upper bounds provided for slice does not match number of input axes"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}