Merge branch 'master' into master

src/ngraph/runtime/intelgpu/intelgpu_op_broadcast.cpp

@@ -191,13 +191,16 @@ void runtime::intelgpu::do_max_min_operation(cldnn::topology& topology,
 {
     const string function_name = "min_max_" + output_name;
     const size_t input_size = shape_size<Shape>(input_shape);
-    const string& init_value = is_min ? "INFINITY" : "-INFINITY";
+    const string& init_value = get_opencl_type_min_max_value(output_type, !is_min);
     const string& operation = is_min ? " < " : " > ";
     codegen::CodeWriter writer;
 
-    writer << "__kernel void " << function_name << "(const __global float input"
-           << array_dims(input_shape) << ", __global float output" << array_dims(output_shape)
-           << ")\n";
+    runtime::intelgpu::gen_func_def(writer,
+                                    function_name,
+                                    {get_opencl_type_name(output_type)},
+                                    {input_shape},
+                                    get_opencl_type_name(output_type),
+                                    output_shape);
 
     writer.block_begin();
     {
@@ -231,11 +234,11 @@ void runtime::intelgpu::do_max_min_operation(cldnn::topology& topology,
                 ++var_idx;
             }
 
-            writer << "if (input" << access_dims(input_shape) << operation << "output"
+            writer << "if (input0" << access_dims(input_shape) << operation << "output"
                    << access_dims(input_shape, "i", axis) << ")\n";
             writer.block_begin();
             {
-                writer << "output" << access_dims(input_shape, "i", axis) << " = input"
+                writer << "output" << access_dims(input_shape, "i", axis) << " = input0"
                        << access_dims(input_shape) << ";\n";
             }
             writer.block_end();

src/ngraph/runtime/intelgpu/intelgpu_op_custom_kernels.cpp

@@ -28,34 +28,38 @@ using namespace ngraph;
 
 string runtime::intelgpu::get_opencl_type_name(const element::Type& ngraph_type)
 {
-    if (ngraph_type == ngraph::element::i64)
+    switch (ngraph_type.get_type_enum())
     {
-        return "long";
+    case element::Type_t::i64: return "long";
+    case element::Type_t::i32: return "int";
+    case element::Type_t::i16: return "short";
+    case element::Type_t::u16: return "ushort";
+    case element::Type_t::i8: return "char";
+    case element::Type_t::u8: return "uchar";
     }
-    else if (ngraph_type == ngraph::element::i32)
-    {
-        return "int";
-    }
-    else if (ngraph_type == ngraph::element::i16)
-    {
-        return "short";
-    }
-    else if (ngraph_type == ngraph::element::u16)
-    {
-        return "ushort";
-    }
-    else if (ngraph_type == ngraph::element::i8)
-    {
-        return "char";
-    }
-    else if (ngraph_type == ngraph::element::u8)
-    {
-        return "uchar";
-    }
-    else
+
+    return ngraph_type.c_type_string();
+}
+
+string runtime::intelgpu::get_opencl_type_min_max_value(const element::Type& ngraph_type,
+                                                        bool is_min)
+{
+    switch (ngraph_type.get_type_enum())
     {
-        return ngraph_type.c_type_string();
+    case element::Type_t::f32: return is_min ? "-INFINITY" : "INFINITY";
+    case element::Type_t::f64: return is_min ? "-INFINITY" : "INFINITY";
+    case element::Type_t::i64: return is_min ? "LONG_MIN" : "LONG_MAX";
+    case element::Type_t::u64: return is_min ? "0" : "ULONG_MAX";
+    case element::Type_t::i32: return is_min ? "INT_MIN" : "INT_MAX";
+    case element::Type_t::u32: return is_min ? "0" : "UINT_MAX";
+    case element::Type_t::i16: return is_min ? "SHRT_MIN" : "SHRT_MAX";
+    case element::Type_t::u16: return is_min ? "0" : "USHRT_MAX";
+    case element::Type_t::i8: return is_min ? "CHAR_MIN" : "CHAR_MAX";
+    case element::Type_t::u8: return is_min ? "0" : "UCHAR_MAX";
     }
+
+    throw ngraph_error("Unsupported type '" + ngraph_type.c_type_string() +
+                       "' in runtime::intelgpu::get_opencl_type_min_max_value()");
 }
 
 vector<cldnn_arg> runtime::intelgpu::get_kernel_args(size_t input, size_t output)

src/ngraph/runtime/intelgpu/intelgpu_op_custom_kernels.hpp

@@ -197,6 +197,8 @@ namespace ngraph
 
             // Helper functions used in cldnn::custom_gpu_primitive kernels
             std::string get_opencl_type_name(const element::Type& ngraph_type);
+            std::string get_opencl_type_min_max_value(const element::Type& ngraph_type,
+                                                      bool is_min);
             std::vector<cldnn_arg> get_kernel_args(size_t input, size_t output);
             std::string array_dims(const Shape& dimentions, const AxisSet& axis = {});
             std::string access_dims(const Shape& dimentions,

src/ngraph/runtime/intelgpu/unit_test.manifest

+all_2x2x3_eliminate_dims_0_1
+argmax_4D_axis_3_i64_in_i32
+argmin_trivial_in_double
+argmin_trivial_in_i32
 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
-backwards_batch_norm_three_outputs
 backwards_batch_norm_training
 backwards_dot_scalar_tensor
-backwards_dot_tensor3_tensor3
 backwards_dot_tensor_scalar
 backwards_dot_tensor_vector
-backwards_exp
 backwards_maxpool_n2_c1_hw5_3x3_str2_max
 backwards_maxpool_n4_c1_hw4_2x2_max
 backwards_replace_slice
 backwards_reverse_sequence_n3_c2_h3
 backwards_reverse_sequence_n4d2c3h2w2
 backwards_slice
-backwards_tanh
+batch_norm_bprop_n4c3h2w2
 batch_norm_inference_0eps_f64
 batch_norm_inference_f64
 batch_norm_training_0eps_f64
-batch_norm_one_output
-batch_norm_three_outputs
-batch_norm_bprop_n4c3h2w2
 dequantize
 dequantize_axes
 dequantize_dynamic_offset
@@ -30,10 +28,11 @@ dequantize_int8
 dequantize_int8_zero_offset
 dequantize_zero_offset
 divide_by_zero_int32
-embedding_lookup_4x5_reverse
 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
@@ -74,6 +73,9 @@ shape_of_scalar
 shape_of_vector
 softmax_axis_3d_double
 sum_stable_acc
+sum_stable_acc_double
+sum_stable_simple_double
+sum_trivial_in_double
 topk_1d_max_all
 topk_1d_max_one
 topk_1d_max_partial
@@ -127,21 +129,3 @@ zero_sized_sqrt
 zero_sized_subtract
 zero_sized_tan
 zero_sized_tanh
-shape_of_scalar
-shape_of_vector
-shape_of_matrix
-shape_of_5d
-sum_stable_acc
-sum_stable_acc_double
-sum_stable_simple_double
-sum_trivial_in_double
-max_matrix_rows_zero_int32
-max_to_scalar_int8
-min_to_scalar_int8
-max_3d_to_scalar_double
-argmin_trivial_in_i32
-argmax_4D_axis_3_i64_in_i32
-argmin_trivial_in_double
-all_2x2x3_eliminate_dim_1
-all_2x2x3_eliminate_dim_2
-all_2x2x3_eliminate_dims_0_1