DropOut for INT (#2029)

src/ngraph/runtime/interpreter/int_backend.hpp

@@ -128,6 +128,7 @@
 #include "ngraph/runtime/reference/tanh.hpp"
 #include "ngraph/runtime/reference/topk.hpp"
 #include "ngraph/runtime/tensor.hpp"
+#include "ngraph/state/rng_state.hpp"
 
 #ifdef NGRAPH_DISTRIBUTED
 #include "ngraph/runtime/reference/allreduce.hpp"
@@ -175,6 +176,7 @@ private:
         bool m_performance_counters_enabled = false;
         std::unordered_map<const Node*, stopwatch> m_timer_map;
         std::vector<NodeWrapper> m_wrapped_nodes;
+        std::unordered_map<const Node*, std::unique_ptr<RNGState>> m_states;
         std::unique_ptr<AlignedBuffer> m_temporary_memory;
 
         void* get_temporary_pointer(size_t offset) { return m_temporary_memory->get_ptr(offset); }
@@ -332,8 +334,19 @@ private:
         }
         case OP_TYPEID::GenerateMask:
         {
-            throw ngraph_error(
-                "GenerateMask is an experimental op that's only supported on CPU backend");
+            if (instance.m_states.count(&node) == 0)
+            {
+                const op::GenerateMask* gm = static_cast<const op::GenerateMask*>(&node);
+                instance.m_states[&node] = std::unique_ptr<ngraph::RNGState>(
+                    ngraph::RNGState::create_rng_state(gm->get_seed(), gm->get_probability()));
+            }
+
+            bool training = static_cast<bool>(static_cast<const T*>(args[0])[0]);
+            auto state = instance.m_states.at(&node).get();
+            size_t element_count = shape_size(node.get_output_shape(0));
+            reference::generate_mask<T>(
+                reinterpret_cast<T*>(out[0]), element_count, state, training);
+            break;
         }
         case OP_TYPEID::GetOutputElement:
         {

src/ngraph/runtime/interpreter/unit_test.manifest

@@ -7,7 +7,6 @@ batchnorm_fprop_inference_b2c2h2w1
 batchnorm_fprop_bprop
 batchnorm_fprop_bprop_2step
 computation_reuse
-generate_mask
 topk_int64
 topk_3d_large_input_max
 topk_3d_large_input_min