Merge branch 'master' into rearhart/plaidml-tolerances

python/ngraph/utils/types.py

@@ -37,6 +37,7 @@ NodeInput = Union[Node, NumericData]
 
 ngraph_to_numpy_types_map = [
     (NgraphType.boolean, np.bool),
+    (NgraphType.f16, np.float16),
     (NgraphType.f32, np.float32),
     (NgraphType.f64, np.float64),
     (NgraphType.i8, np.int8),

python/pyngraph/types/element_type.cpp

@@ -28,6 +28,7 @@ void regclass_pyngraph_Type(py::module m)
     py::class_<ngraph::element::Type, std::shared_ptr<ngraph::element::Type>> type(m, "Type");
     type.doc() = "ngraph.impl.Type wraps ngraph::element::Type";
     type.attr("boolean") = ngraph::element::boolean;
+    type.attr("f16") = ngraph::element::f16;
     type.attr("f32") = ngraph::element::f32;
     type.attr("f64") = ngraph::element::f64;
     type.attr("i8") = ngraph::element::i8;

src/ngraph/frontend/onnx_import/CMakeLists.txt

@@ -85,6 +85,8 @@ add_library(onnx_import STATIC
         op/equal.hpp
         op/erf.hpp
         op/exp.hpp
+        op/eye_like.cpp
+        op/eye_like.hpp
         op/flatten.cpp
         op/flatten.hpp
         op/floor.hpp
@@ -191,6 +193,7 @@ add_library(onnx_import STATIC
         op/xor.hpp
         ops_bridge.cpp
         ops_bridge.hpp
+        utils/common.cpp
         utils/common.hpp
         utils/convpool.cpp
         utils/convpool.hpp

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

@@ -52,6 +52,8 @@ namespace ngraph
             const std::string& output(int index) const;
             std::size_t get_outputs_size() const;
 
+            bool has_attribute(const std::string& name) const;
+
             template <typename T>
             T get_attribute_value(const std::string& name, T default_value) const;
 
@@ -87,6 +89,15 @@ namespace ngraph
         }
 
         std::size_t Node::Impl::get_outputs_size() const { return m_output_names.size(); }
+        bool Node::Impl::has_attribute(const std::string& name) const
+        {
+            auto it = std::find_if(
+                std::begin(m_attributes), std::end(m_attributes), [&](const Attribute& attribute) {
+                    return attribute.get_name() == name;
+                });
+            return it != std::end(m_attributes);
+        }
+
         template <typename T>
         T Node::Impl::get_attribute_value(const std::string& name, T default_value) const
         {
@@ -185,6 +196,11 @@ namespace ngraph
 
         const std::string& Node::output(int index) const { return m_pimpl->output(index); }
         std::size_t Node::get_outputs_size() const { return m_pimpl->get_outputs_size(); }
+        bool Node::has_attribute(const std::string& name) const
+        {
+            return m_pimpl->has_attribute(name);
+        }
+
         template <>
         float Node::get_attribute_value(const std::string& name, float default_value) const
         {

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

@@ -78,6 +78,8 @@ namespace ngraph
             const std::string& output(int index) const;
             std::size_t get_outputs_size() const;
 
+            bool has_attribute(const std::string& name) const;
+
             template <typename T>
             T get_attribute_value(const std::string& name, T default_value) const;
 

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

@@ -24,6 +24,7 @@
 #include "ngraph/type/element_type.hpp"
 #include "node.hpp"
 #include "tensor.hpp"
+#include "utils/common.hpp"
 #include "weight.hpp"
 
 namespace ngraph
@@ -41,17 +42,8 @@ namespace ngraph
                     {
                     }
                 };
-                struct unsupported_element_type : ngraph_error
-                {
-                    explicit unsupported_element_type(TensorProto_DataType type)
-                        : ngraph_error{"unsupported value info element type: " +
-                                       onnx::TensorProto_DataType_Name(
-                                           static_cast<onnx::TensorProto_DataType>(type))}
-                    {
-                    }
-                };
-            }
-        }
+            } // namespace value_info
+        }     // namespace error
 
         class ValueInfo
         {
@@ -83,24 +75,8 @@ namespace ngraph
                 {
                     throw error::value_info::unspecified_element_type{};
                 }
-                switch (m_value_info_proto->type().tensor_type().elem_type())
-                {
-                case onnx::TensorProto_DataType::TensorProto_DataType_BOOL: return element::boolean;
-                case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT:
-                case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT16: return element::f32;
-                case onnx::TensorProto_DataType::TensorProto_DataType_DOUBLE: return element::f64;
-                case onnx::TensorProto_DataType::TensorProto_DataType_INT8: return element::i8;
-                case onnx::TensorProto_DataType::TensorProto_DataType_INT16: return element::i16;
-                case onnx::TensorProto_DataType::TensorProto_DataType_INT32: return element::i32;
-                case onnx::TensorProto_DataType::TensorProto_DataType_INT64: return element::i64;
-                case onnx::TensorProto_DataType::TensorProto_DataType_UINT8: return element::u8;
-                case onnx::TensorProto_DataType::TensorProto_DataType_UINT16: return element::u16;
-                case onnx::TensorProto_DataType::TensorProto_DataType_UINT32: return element::u32;
-                case onnx::TensorProto_DataType::TensorProto_DataType_UINT64: return element::u64;
-                default:
-                    throw error::value_info::unsupported_element_type{
-                        m_value_info_proto->type().tensor_type().elem_type()};
-                }
+                return common::get_ngraph_element_type(
+                    m_value_info_proto->type().tensor_type().elem_type());
             }
 
             std::shared_ptr<ngraph::Node>

src/ngraph/frontend/onnx_import/op/cast.cpp

@@ -13,14 +13,13 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 //*****************************************************************************
-
 #include <memory>
-#include <onnx-ml.pb.h>
 
 #include "cast.hpp"
 #include "exceptions.hpp"
 #include "ngraph/op/convert.hpp"
 #include "ngraph/type/element_type.hpp"
+#include "utils/common.hpp"
 
 namespace ngraph
 {
@@ -34,25 +33,7 @@ namespace ngraph
                 {
                     auto data = node.get_ng_inputs().at(0);
                     int64_t target_type = node.get_attribute_value<int64_t>("to");
-                    element::Type elem_type;
-
-                    switch (target_type)
-                    {
-                    case onnx::TensorProto_DataType_BOOL: elem_type = element::boolean; break;
-                    case onnx::TensorProto_DataType_DOUBLE: elem_type = element::f64; break;
-                    case onnx::TensorProto_DataType_FLOAT16:
-                    case onnx::TensorProto_DataType_FLOAT: elem_type = element::f32; break;
-                    case onnx::TensorProto_DataType_INT8: elem_type = element::i8; break;
-                    case onnx::TensorProto_DataType_INT16: elem_type = element::i16; break;
-                    case onnx::TensorProto_DataType_INT32: elem_type = element::i32; break;
-                    case onnx::TensorProto_DataType_INT64: elem_type = element::i64; break;
-                    case onnx::TensorProto_DataType_UINT8: elem_type = element::u8; break;
-                    case onnx::TensorProto_DataType_UINT16: elem_type = element::u16; break;
-                    case onnx::TensorProto_DataType_UINT32: elem_type = element::u32; break;
-                    case onnx::TensorProto_DataType_UINT64: elem_type = element::u64; break;
-                    case onnx::TensorProto_DataType_UNDEFINED: elem_type = element::dynamic; break;
-                    default: ASSERT_IS_SUPPORTED(node, false) << "unsupported type";
-                    }
+                    element::Type elem_type = common::get_ngraph_element_type(target_type);
 
                     return {std::make_shared<ngraph::op::Convert>(data, elem_type)};
                 }

src/ngraph/frontend/onnx_import/op/eye_like.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 "eye_like.hpp"
+#include "exceptions.hpp"
+#include "ngraph/frontend/onnx_import/utils/common.hpp"
+
+namespace ngraph
+{
+    namespace onnx_import
+    {
+        namespace op
+        {
+            namespace set_1
+            {
+                NodeVector eye_like(const Node& node)
+                {
+                    const auto input = node.get_ng_inputs().at(0);
+                    const auto& input_shape = input->get_shape();
+
+                    std::int64_t dtype;
+                    element::Type target_type;
+
+                    std::int64_t shift = node.get_attribute_value<std::int64_t>("k", 0);
+                    if (node.has_attribute("dtype"))
+                    {
+                        dtype = node.get_attribute_value<std::int64_t>("dtype");
+                        target_type = common::get_ngraph_element_type(dtype);
+                    }
+                    else
+                    {
+                        target_type = input->get_element_type();
+                    }
+
+                    ASSERT_VALID_ARGUMENT(node, input_shape.size() == 2)
+                        << "The provided shape rank: " << input_shape.size()
+                        << " is unsupported, only 2D shapes are supported";
+
+                    std::shared_ptr<ngraph::Node> eye_like_matrix =
+                        common::shifted_square_identity(input_shape, target_type, shift);
+
+                    return {eye_like_matrix};
+                }
+
+            } // namespace set_1
+
+        } // namespace op
+
+    } // namespace onnx_import
+
+} // namespace ngraph

src/ngraph/frontend/onnx_import/op/eye_like.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 "core/node.hpp"
+#include "ngraph/node.hpp"
+
+namespace ngraph
+{
+    namespace onnx_import
+    {
+        namespace op
+        {
+            namespace set_1
+            {
+                NodeVector eye_like(const Node& node);
+            } // namespace set_1
+
+        } //namespace op
+
+    } // namespace onnx_import
+
+} // namespace ngraph

src/ngraph/frontend/onnx_import/ops_bridge.cpp

@@ -53,6 +53,7 @@
 #include "op/equal.hpp"
 #include "op/erf.hpp"
 #include "op/exp.hpp"
+#include "op/eye_like.hpp"
 #include "op/flatten.hpp"
 #include "op/floor.hpp"
 #include "op/gather.hpp"
@@ -260,6 +261,7 @@ namespace ngraph
             REGISTER_OPERATOR("Equal", 1, equal);
             REGISTER_OPERATOR("Erf", 1, erf);
             REGISTER_OPERATOR("Exp", 1, exp);
+            REGISTER_OPERATOR("EyeLike", 1, eye_like);
             REGISTER_OPERATOR("Flatten", 1, flatten);
             REGISTER_OPERATOR("Floor", 1, floor);
             REGISTER_OPERATOR("Gather", 1, gather);

src/ngraph/frontend/onnx_import/utils/common.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 <onnx-ml.pb.h> // onnx types
+
+#include "common.hpp"
+
+namespace ngraph
+{
+    namespace onnx_import
+    {
+        namespace common
+        {
+            const ngraph::element::Type& get_ngraph_element_type(int64_t onnx_type)
+            {
+                switch (onnx_type)
+                {
+                case onnx::TensorProto_DataType_BOOL: return element::boolean;
+                case onnx::TensorProto_DataType_DOUBLE: return element::f64;
+                case onnx::TensorProto_DataType_FLOAT16: return element::f16;
+                case onnx::TensorProto_DataType_FLOAT: return element::f32;
+                case onnx::TensorProto_DataType_INT8: return element::i8;
+                case onnx::TensorProto_DataType_INT16: return element::i16;
+                case onnx::TensorProto_DataType_INT32: return element::i32;
+                case onnx::TensorProto_DataType_INT64: return element::i64;
+                case onnx::TensorProto_DataType_UINT8: return element::u8;
+                case onnx::TensorProto_DataType_UINT16: return element::u16;
+                case onnx::TensorProto_DataType_UINT32: return element::u32;
+                case onnx::TensorProto_DataType_UINT64: return element::u64;
+                case onnx::TensorProto_DataType_UNDEFINED: return element::dynamic;
+                }
+                throw ngraph_error("unsupported element type: " +
+                                   onnx::TensorProto_DataType_Name(
+                                       static_cast<onnx::TensorProto_DataType>(onnx_type)));
+            }
+
+        } // namespace  common
+    }     // namespace onnx_import
+} // namespace ngraph

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

@@ -19,6 +19,7 @@
 #include <algorithm>   // std::generate
 #include <cmath>       // std::floor, std::min
 #include <cstddef>     // std::size_t
+#include <cstdint>     // std::int64_t
 #include <iterator>    // std::begin, std::end
 #include <memory>      // std::shared_ptr, std::make_shared
 #include <type_traits> // std::enable_if
@@ -27,6 +28,7 @@
 #include "ngraph/op/constant.hpp"
 #include "ngraph/op/util/broadcasting.hpp"
 #include "ngraph/shape.hpp"
+#include "ngraph/type/element_type.hpp"
 
 namespace ngraph
 {
@@ -34,6 +36,8 @@ namespace ngraph
     {
         namespace common
         {
+            const ngraph::element::Type& get_ngraph_element_type(std::int64_t onnx_type);
+
             /// \brief      Return a monotonic sequence.
             ///
             /// \note       Limitations: this function may not work for very large integer values
@@ -86,6 +90,43 @@ namespace ngraph
                 }
             }
 
+            /// \brief Creates a shifted square identity matrix.
+            /// \note Shifting in the context of this operator means that
+            ///       the matrix can be created with elements equal to 1 not only in the main diagonal.
+            ///       Shifting adds an offset and moves the diagonal up or down
+            ///
+            /// \param[in] output_shape Shape of the resulting matrix.
+            /// \param[in] output_type Element type of the resulting matrix.
+            /// \param[in] shift Shifting of diagonal.
+            ///
+            /// \return A Constant node representing shifted identity matrix.
+            template <typename T = double>
+            std::shared_ptr<ngraph::op::Constant>
+                shifted_square_identity(const Shape output_shape,
+                                        const element::Type& output_type,
+                                        const std::int64_t shift)
+            {
+                std::vector<T> identity_matrix(shape_size(output_shape), T{0});
+                std::int64_t rows = output_shape[0];
+                std::int64_t cols = output_shape[1];
+                for (std::int64_t row = 0; row < rows; ++row)
+                {
+                    const std::int64_t diagonal_element_idx = (row * cols) + row + shift;
+                    if (row + shift < 0)
+                    {
+                        continue;
+                    }
+                    else if (row + shift >= cols)
+                    {
+                        break;
+                    }
+                    identity_matrix.at(diagonal_element_idx) = T{1};
+                }
+
+                return std::make_shared<ngraph::op::Constant>(
+                    output_type, output_shape, identity_matrix);
+            }
+
             /// \brief Creates a square identity matrix.
             ///
             /// \param[in] n Order of the resulting matrix.
@@ -95,16 +136,9 @@ namespace ngraph
             std::shared_ptr<ngraph::op::Constant> square_identity(const size_t n,
                                                                   const element::Type& type)
             {
-                std::vector<T> identity_matrix(n * n, T{0});
-
-                for (size_t row = 0; row < n; ++row)
-                {
-                    const size_t diagonal_element = (n * row) + row;
-                    identity_matrix.at(diagonal_element) = T{1};
-                }
-
-                return std::make_shared<ngraph::op::Constant>(type, Shape{{n, n}}, identity_matrix);
+                return shifted_square_identity(Shape{n, n}, type, 0);
             }
+
         } // namespace  common
     }     // namespace onnx_import
 } // namespace ngraph

src/ngraph/node.hpp

@@ -216,6 +216,7 @@ namespace ngraph
         virtual bool is_op() const { return false; }
         virtual bool is_commutative() { return false; }
         virtual bool is_dynamic() const;
+        virtual bool has_state() const { return false; }
         size_t get_instance_id() const { return m_instance_id; }
         friend std::ostream& operator<<(std::ostream&, const Node&);
         virtual std::ostream& write_short_description(std::ostream&) const;

src/ngraph/op/experimental/generate_mask.hpp

@@ -72,6 +72,8 @@ namespace ngraph
             /// \brief Returns the seed value supplied to a random generator
             uint64_t get_seed() const { return m_seed; }
             bool get_use_seed() const { return m_use_seed; }
+            /// GenerateMask has state.
+            bool has_state() const override { return true; }
         protected:
             virtual void generate_adjoints(autodiff::Adjoints& adjoints,
                                            const NodeVector& deltas) override

src/ngraph/runtime/cpu/cpu_external_function.cpp

@@ -924,7 +924,8 @@ using namespace ngraph::runtime;
             // Always enable nodes computing output tensors or nodes whose outputs might get
             // overwritten due to inplace kernels
             // TODO (jbobba) - Do we need to handle cacheability
-            if (computes_result(node.get()) || possibly_overwritten(node.get()))
+            if (computes_result(node.get()) || possibly_overwritten(node.get()) ||
+                node->has_state())
             {
                 writer << " || 1";
             }
@@ -1423,7 +1424,7 @@ void runtime::cpu::CPU_ExternalFunction::build(ngraph::pass::PassConfig& pass_co
         bool disable_caching =
             (reuse_memory &&
              !cacheable) // Check cacheability only if we are reusing intermediate tensors
-            || computes_result(node.get()) || possibly_overwritten(node.get());
+            || computes_result(node.get()) || possibly_overwritten(node.get()) || node->has_state();
 
         vector<reference_wrapper<bool>> in_stale, out_stale;
         for (const auto& name : in_names)

test/models/onnx/eye_like.prototxt

+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "x"
+    output: "y"
+    op_type: "EyeLike"
+      attribute {
+      name: "k"
+      i: -1
+      type: INT
+    }
+  }
+  name: "hardmax_graph"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 9
+}

test/onnx/onnx_import.in.cpp

@@ -1482,3 +1482,46 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_shrink_int)
 
     test_case.run();
 }
+
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_eye_like)
+{
+    const auto eye_like_fn = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/eye_like.prototxt"));
+
+    auto test_case = ngraph::test::NgraphTestCase(eye_like_fn, "${BACKEND_NAME}");
+    test_case.add_input<float>({
+        0.f,
+        0.f,
+        0.f,
+        0.f,
+
+        0.f,
+        0.f,
+        0.f,
+        0.f,
+
+        0.f,
+        0.f,
+        0.f,
+        0.f,
+    });
+    test_case.add_expected_output<float>(Shape{3, 4},
+                                         {
+                                             0.f,
+                                             0.f,
+                                             0.f,
+                                             0.f,
+
+                                             1.f,
+                                             0.f,
+                                             0.f,
+                                             0.f,
+
+                                             0.f,
+                                             1.f,
+                                             0.f,
+                                             0.f,
+                                         });
+
+    test_case.run();
+}