IntelGPU backend: Double datatype workaround implemented (#2435)

src/ngraph/runtime/intelgpu/intelgpu_backend.cpp

@@ -1332,7 +1332,8 @@ shared_ptr<runtime::Executable>
 
             arguments_check(op, 5, 1);
 
-            if (get_input_shape(op, 2).size() != 4)
+            if ((get_input_shape(op, 2).size() != 4) ||
+                (get_input_type(op) != ngraph::element::f32))
             {
                 do_batch_norm_operation(topology,
                                         get_output_name(op),
@@ -1364,7 +1365,8 @@ shared_ptr<runtime::Executable>
                 static_pointer_cast<op::BatchNormTraining>(op);
             const double eps = bnorm->get_eps_value();
 
-            if (get_input_shape(op, 2).size() != 4)
+            if ((get_input_shape(op, 2).size() != 4) ||
+                (get_input_type(op) != ngraph::element::f32))
             {
                 string mean_name;
                 string variance_name;

src/ngraph/runtime/intelgpu/intelgpu_layout.cpp

@@ -54,32 +54,19 @@ bool runtime::intelgpu::IntelGPULayout::
 cldnn::data_types
     runtime::intelgpu::IntelGPULayout::get_cldnn_type(const element::Type& element_type)
 {
-    if ((element_type == ngraph::element::i8) || (element_type == ngraph::element::boolean))
+    switch (element_type.get_type_enum())
     {
-        return cldnn::data_types::i8;
-    }
-    else if (element_type == ngraph::element::u8)
-    {
-        return cldnn::data_types::u8;
-    }
-    else if (element_type == ngraph::element::i32)
-    {
-        return cldnn::data_types::i32;
-    }
-    else if (element_type == ngraph::element::i64)
-    {
-        return cldnn::data_types::i64;
-    }
-    else if (element_type == ngraph::element::f32)
-    {
-        return cldnn::data_types::f32;
-    }
-    else
-    {
-        ostringstream os;
-        os << "IntelGPULayout::get_cldnn_type: Unknown type " << element_type;
-        throw invalid_argument(os.str());
+    case element::Type_t::i8:
+    case element::Type_t::boolean: return cldnn::data_types::i8;
+    case element::Type_t::u8: return cldnn::data_types::u8;
+    case element::Type_t::i32: return cldnn::data_types::i32;
+    case element::Type_t::i64: return cldnn::data_types::i64;
+    case element::Type_t::f32: return cldnn::data_types::f32;
     }
+
+    ostringstream os;
+    os << "IntelGPULayout::get_cldnn_type: Unknown type " << element_type;
+    throw invalid_argument(os.str());
 }
 
 cldnn::tensor runtime::intelgpu::IntelGPULayout::create_cldnn_tensor(const Shape& element_shape)
@@ -131,13 +118,27 @@ cldnn::layout runtime::intelgpu::IntelGPULayout::create_cldnn_layout(
     const cldnn::tensor tensor = create_cldnn_tensor(element_shape);
     cldnn::data_types data_type;
 
-    if ((element_type == ngraph::element::i16) || (element_type == ngraph::element::u16))
+    switch (element_type.get_type_enum())
+    {
+    case element::Type_t::i16:
+    case element::Type_t::u16:
     {
         data_type = cldnn::data_types::f16;
+        break;
     }
-    else
+    case element::Type_t::u32:
     {
-        data_type = get_cldnn_type(element_type);
+        data_type = cldnn::data_types::i32;
+        break;
+    }
+    case element::Type_t::u64:
+    case element::Type_t::f64:
+    {
+        data_type = cldnn::data_types::i64;
+        break;
+    }
+    default: { data_type = get_cldnn_type(element_type);
+    }
     }
 
     return cldnn::layout(data_type, format, tensor);

src/ngraph/runtime/intelgpu/unit_test.manifest

 all_2x2x3_eliminate_dims_0_1
-argmin_trivial_in_double
 avg_pool_2d_2channel_2image_padded_only_above_do_not_include_in_computation
 avg_pool_2d_2channel_2image_padded_only_above_include_in_computation
 avg_pool_3d_uneven_strided_padded
@@ -30,10 +29,7 @@ embedding_lookup_10x1_arbitrary
 embedding_lookup_10x1_arbitrary_index_type_int
 embedding_lookup_4x5_reverse
 generate_mask
-max_3d_to_scalar_double
 max_pool_3d
-numeric_double_inf
-numeric_double_nan
 quantize
 quantize_axes
 quantize_clamp_int32
@@ -69,9 +65,6 @@ shape_of_matrix
 shape_of_scalar
 shape_of_vector
 softmax_axis_3d_double
-sum_stable_acc_double
-sum_stable_simple_double
-sum_trivial_in_double
 topk_1d_max_all
 topk_1d_max_one
 topk_1d_max_partial