CPU: Implement op::Slice

src/ngraph/runtime/cpu/emitter.cpp

@@ -28,6 +28,7 @@
 #include "ngraph/ops/get_tuple_element.hpp"
 #include "ngraph/ops/reduce.hpp"
 #include "ngraph/ops/reshape.hpp"
+#include "ngraph/ops/slice.hpp"
 #include "ngraph/runtime/tensor_view_info.hpp"
 #include "ngraph/runtime/cpu/call_frame.hpp"
 #include "ngraph/runtime/cpu/emitter.hpp"
@@ -1326,3 +1327,87 @@ void Emitter::EMITTER_DECL(EmitSign)
                    EIGEN_VECTOR_FORMAT(inputs[0].get_layout<DenseTensorViewLayout>()->get_size()) ").sign();\n"
           "    }\n";
 }
+
+void Emitter::EMITTER_DECL(EmitSlice)
+{
+    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");
+        }
+    }
+
+    auto arg_type = slice->get_arguments().at(0)->get_value_type();
+    auto arg_tensor_view_type = dynamic_pointer_cast<const TensorViewType>(arg_type);
+    assert(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)
+    {
+        TU +=
+            "    {\n"
+            "        call_frame->get_parameterized_tensor_view<" +
+            element_type_names[TI(arg_element_type)] + ">(" +
+            to_string(outputs.at(0).get_index()) +
+            ")->get_vector() =\n"
+            "        call_frame->get_parameterized_tensor_view<" +
+            element_type_names[TI(arg_element_type)] + ">(" +
+            to_string(inputs.at(0).get_index()) +
+            ")->get_vector();\n"
+            "    }\n";
+    }
+    else if (arg_rank == 1)
+    {
+        TU +=
+            "    {\n"
+            "        auto arg0 = call_frame->get_tensor_view_data<" + element_type_names[TI(arg_element_type)] +
+            ">(" + to_string(inputs[0].get_index()) + ");\n"
+            "        auto out  = call_frame->get_tensor_view_data<" + element_type_names[TI(arg_element_type)] +
+            ">(" + to_string(outputs[0].get_index()) + ");\n"
+            "        EigenVector<" + element_type_names[TI(arg_element_type)] +
+            ">(out, " EIGEN_VECTOR_FORMAT(outputs[0].get_layout<DenseTensorViewLayout>()->get_size()) ") =\n"
+            "        EigenVector<" + element_type_names[TI(arg_element_type)] +
+            ">(arg0, " EIGEN_VECTOR_FORMAT(inputs[0].get_layout<DenseTensorViewLayout>()->get_size()) ").segment(\n"
+            "        " + to_string(lower_bounds[0]) + ", " + to_string(upper_bounds[0] - lower_bounds[0]) + ");\n"
+            "    }\n";
+    }
+    else if (arg_rank == 2)
+    {
+        auto arg0_layout = inputs[0].get_layout<DenseTensorViewLayout>();
+        auto out_layout = outputs[0].get_layout<DenseTensorViewLayout>();
+
+        TU +=
+            "    {\n"
+            "        auto arg0 = call_frame->get_tensor_view_data<" +
+            element_type_names[TI(arg_element_type)] + ">(" + to_string(inputs[0].get_index()) +
+            ");\n"
+            "        auto out  = call_frame->get_tensor_view_data<" +
+            element_type_names[TI(arg_element_type)] + ">(" + to_string(outputs[0].get_index()) +
+            ");\n"
+            "        EigenMatrix<" +
+            element_type_names[TI(arg_element_type)] + ">(out, " +
+            EIGEN_MATRIX_FORMAT(out_layout->get_shape(), out_layout->get_strides()) +
+            ") = \n"
+            "        EigenMatrix<" +
+            element_type_names[TI(arg_element_type)] + ">(arg0, " +
+            EIGEN_MATRIX_FORMAT(arg0_layout->get_shape(), arg0_layout->get_strides()) +
+            ").block(" + to_string(lower_bounds[0]) + ", " + to_string(lower_bounds[1]) + ",\n"
+            "        " + to_string(upper_bounds[0] - lower_bounds[0]) + ",\n"
+            "        " + to_string(upper_bounds[1] - lower_bounds[1])+ ");\n"
+            "    }\n";
+    }
+    // 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");
+    }
+}

src/ngraph/runtime/cpu/emitter.hpp

@@ -81,6 +81,7 @@ namespace ngraph
                 void EMITTER_DECL(EmitFunctionCall);
                 void EMITTER_DECL(EmitReduce);
                 void EMITTER_DECL(EmitSign);
+                void EMITTER_DECL(EmitSlice);
             };
         }
     }

src/ngraph/runtime/cpu/external_function.cpp

@@ -51,6 +51,7 @@
 #include "ngraph/ops/reshape.hpp"
 #include "ngraph/ops/select.hpp"
 #include "ngraph/ops/sign.hpp"
+#include "ngraph/ops/slice.hpp"
 #include "ngraph/ops/subtract.hpp"
 #include "ngraph/ops/tuple.hpp"
 #include "ngraph/pass/assign_layout.hpp"
@@ -115,6 +116,7 @@ static const OpMap dispatcher{
     {TI(ngraph::op::FunctionCall), &Emitter::EmitFunctionCall},
     {TI(ngraph::op::Reduce), &Emitter::EmitReduce},
     {TI(ngraph::op::Sign), &Emitter::EmitSign},
+    {TI(ngraph::op::Slice), &Emitter::EmitSlice},
 };
 
 #undef TI