IntelGPUBackend: const, div, maxpool and max operations (#1234)

* IntelGPUBackend: const, div, maxpool and max operations

* IntelGPUBackend: negative, abs, relu, sqrt, tanh and substract operations

* Update intelgpu_backend.cpp

src/ngraph/runtime/intelgpu/intelgpu_backend.hpp

@@ -20,6 +20,7 @@
 #include <memory>
 
 #include <CPP/engine.hpp>
+#include <CPP/network.hpp>
 
 #include "ngraph/runtime/backend.hpp"
 

src/ngraph/runtime/intelgpu/intelgpu_layout.cpp

@@ -73,18 +73,39 @@ cldnn::data_types
     else
     {
         ostringstream os;
-        os << "IntelGPUTensorView::get_cldnn_type: Unknown type " << element_type;
+        os << "IntelGPULayout::get_cldnn_type: Unknown type " << element_type;
         throw std::invalid_argument(os.str());
     }
 }
 
+cldnn::tensor runtime::intelgpu::IntelGPULayout::create_cldnn_tensor(const Shape& element_shape)
+{
+    std::vector<size_t> idx(4, 1);
+    size_t index = 0;
+
+    for (auto i = element_shape.rbegin(); i != element_shape.rend() && index < 3; ++i, ++index)
+    {
+        idx.at(index) = *i;
+    }
+
+    if (element_shape.size() > 3)
+    {
+        idx.at(3) =
+            accumulate(element_shape.rbegin() + 3, element_shape.rend(), 1, multiplies<size_t>());
+    }
+
+    //Parameters for this ctor: batch, feature, spatial_x, spatial_y
+    const cldnn::tensor tns(idx.at(3), idx.at(2), idx.at(0), idx.at(1));
+
+    return tns;
+}
+
 cldnn::layout runtime::intelgpu::IntelGPULayout::create_cldnn_layout(
     const ngraph::element::Type& element_type, const Shape& element_shape)
 {
-    const size_t mem_size = shape_size(element_shape);
     const cldnn::data_types data_type = get_cldnn_type(element_type);
-    const cldnn::tensor tensor(1, mem_size, 1, 1);
-    const cldnn::format::type format = cldnn::format::yxfb;
+    const cldnn::format::type format = cldnn::format::bfyx;
+    const cldnn::tensor tensor = create_cldnn_tensor(element_shape);
 
     return cldnn::layout(data_type, format, tensor);
 }

src/ngraph/runtime/intelgpu/intelgpu_layout.hpp

@@ -17,6 +17,7 @@
 #pragma once
 
 #include <CPP/layout.hpp>
+#include <CPP/tensor.hpp>
 
 #include "ngraph/descriptor/layout/tensor_view_layout.hpp"
 
@@ -48,6 +49,7 @@ public:
     static cldnn::data_types get_cldnn_type(const ngraph::element::Type& element_type);
     static cldnn::layout create_cldnn_layout(const ngraph::element::Type& element_type,
                                              const Shape& element_shape);
+    static cldnn::tensor create_cldnn_tensor(const Shape& element_shape);
 
 private:
     Strides strides;

src/ngraph/runtime/intelgpu/intelgpu_tensor_view.cpp

@@ -41,7 +41,7 @@ runtime::intelgpu::IntelGPUTensorView::IntelGPUTensorView(const ngraph::element:
     if (nullptr != memory_pointer)
     {
         ocl_memory = make_shared<cldnn::memory>(
-            cldnn::memory::attach<void>(layout, memory_pointer, layout.get_linear_size()));
+            cldnn::memory::attach<void>(layout, memory_pointer, layout.bytes_count()));
     }
     else
     {

src/ngraph/runtime/intelgpu/intelgpu_tensor_view.hpp

@@ -17,11 +17,9 @@
 #pragma once
 
 #include <CPP/engine.hpp>
-#include <CPP/layout.hpp>
 #include <CPP/memory.hpp>
 
 #include "ngraph/runtime/tensor_view.hpp"
-#include "ngraph/type/element_type.hpp"
 
 namespace ngraph
 {
@@ -56,7 +54,5 @@ public:
 
     cldnn::memory* get_data_ptr() { return ocl_memory.get(); }
 private:
-    cldnn::data_types get_cldnn_type(const ngraph::element::Type& element_type) const;
-
     std::shared_ptr<cldnn::memory> ocl_memory;
 };

src/ngraph/runtime/intelgpu/unit_test.manifest

-ab
-abc
 abc_int64
 abs
 acos
@@ -18,11 +16,10 @@ avg_pool_2d_2channel_2image_padded_3x3_strided
 avg_pool_2d_2channel_2image_padded_3x3_strided_uneven
 avg_pool_2d_2channel_2image_padded_only_above
 avg_pool_2d_2channel_2image_padded_only_below
-backwards_abc
+avg_pool_3d
 backwards_abs
 backwards_acos
 backwards_add
-backwards_add_nested
 backwards_asin
 backwards_atan
 backwards_avgpool_n1_c1_hw2x2
@@ -56,18 +53,17 @@ backwards_maxpool_n2_c1_hw5_3x3_str2_max
 backwards_maxpool_n4c1h4w4_kh2kw2_sh1sw1
 backwards_maxpool_n4_c1_hw4_2x2_max
 backwards_minimum
-backwards_multiply
 backwards_negative
 backwards_parameter
 backwards_power
 backwards_relu
 backwards_replace_slice
-backwards_reshape
 backwards_reverse_3d_02
 backwards_reverse_sequence_n3_c2_h3
 backwards_reverse_sequence_n4d2c3h2w2
 backwards_select
 backwards_select_nested
+backwards_sigmoid
 backwards_sign
 backwards_sin
 backwards_sinh
@@ -152,9 +148,7 @@ convolution_4d_4items_strided_dilated_padded_same
 convolution_outlining
 cos
 cosh
-divide
 divide_adjoint_stability
-divide_by_zero_float32
 divide_by_zero_int32
 dot_0_0
 dot1d
@@ -176,8 +170,6 @@ equal
 exp
 floor
 function_call
-function_name
-fuse_max_with_constant_zero_input_as_relu
 greater
 greatereq
 kahan_sum_3d_to_vector
@@ -193,20 +185,16 @@ max_3d_to_matrix_least_sig
 max_3d_to_matrix_most_sig
 max_3d_to_scalar
 max_3d_to_vector
-maximum
 max_matrix_cols_zero
 max_matrix_columns
 max_matrix_rows
 max_matrix_rows_zero
 max_matrix_to_scalar_zero_by_zero
-max_pool_1d_1channel_1image
-max_pool_1d_1channel_2image
-max_pool_1d_2channel_2image
 max_pool_2d_1channel_1image_overpadded
 max_pool_2d_1channel_1image_padded
 max_pool_2d_1channel_1image_padded_negative_values
 max_pool_2d_1channel_1image_strided
-max_pool_2d_2channel_2image
+max_pool_3d
 max_to_scalar
 max_trivial
 max_trivial_5d
@@ -226,10 +214,10 @@ min_to_scalar
 min_trivial
 min_trivial_5d
 min_vector_zero
+mkldnn_layouts
 multiple_backends
 multiple_result
 negative
-node_name
 not
 notequal
 numeric_double_inf
@@ -257,7 +245,6 @@ pad_interior_1d
 pad_interior_exterior_1d
 pad_interior_exterior_2d
 pad_interior_exterior_4d_2x0x3x2
-parameter_as_output
 power
 product_3d_eliminate_zero_dim
 product_3d_to_matrix_least_sig
@@ -297,19 +284,7 @@ replace_slice_3d_strided_different_strides
 replace_slice_matrix
 replace_slice_scalar
 replace_slice_vector
-reshape_3d_transpose
-reshape_4d_transpose
 reshape_6d
-reshape_m2m_dim_change_transpose
-reshape_m2m_same
-reshape_m2m_transpose
-reshape_s2t
-reshape_t2s_012
-reshape_t2s_120
-reshape_t2v_012
-reshape_v2m_col
-reshape_v2m_row
-reshape_v2t_middle
 reverse_0d
 reverse_1d_0
 reverse_1d_nochange
@@ -328,12 +303,14 @@ reverse_3d_nochange
 reverse_sequence_n2c3h4w2
 reverse_sequence_n4c3h2w2
 reverse_sequence_n4d2c3h2w2
-scalar_constant_float32
 scalar_constant_int64
 select
 select_and_scatter_3d_without_overlap
 select_and_scatter_without_overlap
 select_and_scatter_with_overlap
+sigmoid_bprop_n1c1h4
+sigmoid_n1c1h2w2
+sigmoid_n1c1h4
 sign
 sin
 sinh
@@ -369,11 +346,8 @@ sum_trivial_5d
 sum_vector_zero
 tan
 tanh
-tensor_constant
-tensor_constant_float32
 tensor_constant_int64
 tensor_constant_with_op
-tensorview_custom_mem
 validate_call_input_type
 validate_call_output_type
 zero_sized_abs