CPU: MKLDNN Average Pooling

src/ngraph/runtime/cpu/cpu_emitter.cpp

@@ -2145,16 +2145,59 @@ void runtime::cpu::CPU_Emitter::EmitAvgPool(codegen::CodeWriter& writer,
     auto avg_pool = static_cast<const op::AvgPool*>(n);
 
     auto arg_shape = args[0].get_shape();
+    auto arg_rank = arg_shape.size();
+
     auto result_shape = out[0].get_shape();
 
-    writer << "kernel::avg_pool<" << out[0].get_type() << ">(" << args[0].get_name() << ",\n";
-    writer << "                 " << out[0].get_name() << ",\n";
-    writer << "                 {" << join(arg_shape) << "},\n";
-    writer << "                 {" << join(result_shape) << "},\n";
-    writer << "                 {" << join(avg_pool->get_window_shape()) << "},\n";
-    writer << "                 {" << join(avg_pool->get_window_movement_strides()) << "},\n";
-    writer << "                 {" << join(avg_pool->get_padding_below()) << "},\n";
-    writer << "                 {" << join(avg_pool->get_padding_above()) << "});\n";
+    // TODO(jmenon): Refactor into an MKLDNN Pooling emitter that handles
+    // all pooling variants
+
+    // TODO(jmenon): Optimize for 1D
+
+    // TODO(jmenon): Remove element type restriction
+    if (arg_rank == 4 && avg_pool->get_window_shape().size() == 2 &&
+        args[0].get_element_type() == element::f32)
+    {
+        const string& et = get_mkldnn_data_type(args[0].get_element_type().c_type_string());
+
+        writer << "{\n";
+        writer.indent++;
+
+        writer << "auto input_data_desc = memory::desc({" << join(arg_shape) << "}, " << et
+               << ", memory::format::nchw);\n";
+        writer << "auto result_desc = memory::desc({" << join(result_shape) << "}, " << et
+               << ", memory::format::nchw);\n";
+
+        writer << "auto input_data = memory({input_data_desc, cpu_engine}, " << args[0].get_name()
+               << ");\n";
+        writer << "auto result = memory({result_desc, cpu_engine}, " << out[0].get_name() << ");\n";
+
+        // TODO(jmenon): Use a workspace
+        writer << "auto avg_pooling = pooling_forward({"
+               << "{prop_kind::forward_inference, algorithm::pooling_avg, "
+               << "input_data_desc, result_desc, {" << join(avg_pool->get_window_movement_strides())
+               << "}, {" << join(avg_pool->get_window_shape()) << "}, "
+               << "{" << join(avg_pool->get_padding_below()) << "}, "
+               << "{" << join(avg_pool->get_padding_above()) << "}, "
+               << "padding_kind::zero}, cpu_engine}, "
+               << "input_data, result);\n";
+
+        writer << "auto s = stream(stream::kind::eager);\n"
+               << "s.submit({avg_pooling}).wait();\n";
+        writer.indent--;
+        writer << "}\n";
+    }
+    else
+    {
+        writer << "kernel::avg_pool<" << out[0].get_type() << ">(" << args[0].get_name() << ",\n";
+        writer << "                 " << out[0].get_name() << ",\n";
+        writer << "                 {" << join(arg_shape) << "},\n";
+        writer << "                 {" << join(result_shape) << "},\n";
+        writer << "                 {" << join(avg_pool->get_window_shape()) << "},\n";
+        writer << "                 {" << join(avg_pool->get_window_movement_strides()) << "},\n";
+        writer << "                 {" << join(avg_pool->get_padding_below()) << "},\n";
+        writer << "                 {" << join(avg_pool->get_padding_above()) << "});\n";
+    }
 }
 
 void runtime::cpu::CPU_Emitter::EmitPad(codegen::CodeWriter& writer,