add support for asymmetric padding and auto_pad (SAME_UPPER)

src/include/migraphx/operators.hpp

@@ -16,6 +16,13 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+enum padding_mode_t
+{
+    default_, // NOLINT
+    same,
+    valid
+};
+
 struct not_computable
 {
     argument compute(const shape&, const std::vector<argument>&) const
@@ -58,12 +65,7 @@ struct convolution
     std::array<std::size_t, 2> padding  = {{0, 0}};
     std::array<std::size_t, 2> stride   = {{1, 1}};
     std::array<std::size_t, 2> dilation = {{1, 1}};
-    enum padding_mode_t
-    {
-        default_, // NOLINT
-        same,
-        valid
-    };
+    
     padding_mode_t padding_mode = default_;
     int group                   = 1;
 
@@ -189,12 +191,14 @@ struct pooling
     std::array<std::size_t, 2> padding = {{0, 0}};
     std::array<std::size_t, 2> stride  = {{1, 1}};
     std::array<std::size_t, 2> lengths = {{1, 1}};
+    padding_mode_t padding_mode = default_;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
         return pack(f(self.mode, "mode"),
                     f(self.padding, "padding"),
+                    f(self.padding, "padding_mode"),
                     f(self.stride, "stride"),
                     f(self.lengths, "lengths"));
     }
@@ -211,7 +215,10 @@ struct pooling
         assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
         assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
 
-        return {t,
+        
+        if(padding_mode == default_)
+        {
+            return {t,
                 {
                     input.lens()[0],
                     input.lens()[1],
@@ -226,6 +233,40 @@ struct pooling
                                                   static_cast<float>(stride[1]))) +
                             1)),
                 }};
+        }
+        else if(padding_mode == same)
+        {
+            return {t,
+                    {input.lens()[0],
+                     input.lens()[1],
+                     static_cast<std::size_t>(
+                         std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
+                     static_cast<std::size_t>(
+                         std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
+        }
+        else if(padding_mode == valid)
+        {
+            return {t,
+                {
+                input.lens()[0],
+                    input.lens()[1],
+                    std::size_t(std::max<std::ptrdiff_t>(
+                        1,
+                        std::ptrdiff_t(std::floor((input.lens()[2] - lengths[0]) /
+                                                  static_cast<float>(stride[0]))) +
+                            1)),
+                    std::size_t(std::max<std::ptrdiff_t>(
+                        1,
+                        std::ptrdiff_t(std::floor((input.lens()[3] - lengths[1]) /
+                                                  static_cast<float>(stride[1]))) +
+                            1)),
+                }};
+        }
+        else
+        {
+            MIGRAPHX_THROW("Invalid padding mode");
+        }
+        
     }
 };
 

src/onnx/onnx.cpp

@@ -215,24 +215,30 @@ struct onnx_parser
     parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
     {
         op::convolution op;
+        auto l0 = args[0];
         if(contains(attributes, "pads"))
         {
             if(contains(attributes, "auto_pad"))
             {
                 MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
             }
-            std::vector<std::size_t> padding(4);
-            copy(attributes["pads"].ints(), padding.begin());
+            std::vector<std::int64_t> padding;
+            copy(attributes["pads"].ints(), std::back_inserter(padding));
             if(padding.size() != 4)
             {
                 MIGRAPHX_THROW("padding should have 4 values");
             }
             if(padding[0] != padding[2] || padding[1] != padding[3])
             {
-                MIGRAPHX_THROW("migraphx does not support asymetric padding");
+                // insert zeros for pad op (args[0] has 4 dims)
+                padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
+                l0 = prog.add_instruction(op::pad{padding}, l0);
             }
-            op.padding[0] = padding[0];
-            op.padding[1] = padding[1];
+            else
+            {
+                op.padding[0] = padding[0];
+                op.padding[1] = padding[1];
+            }   
         }
         if(contains(attributes, "strides"))
         {
@@ -252,7 +258,7 @@ struct onnx_parser
 
             if(s.find("SAME") != std::string::npos)
             {
-                op.padding_mode = op::convolution::same;
+                op.padding_mode = op::padding_mode_t::same;
             }
         }
         if(contains(attributes, "group"))
@@ -266,7 +272,7 @@ struct onnx_parser
             auto l2       = prog.add_instruction(op::broadcast{axis, l1->get_shape()}, args[2]);
             return prog.add_instruction(op::add{}, l1, l2);
         }
-        return prog.add_instruction(op, args);
+        return prog.add_instruction(op, l0, args[1]);
     }
 
     instruction_ref parse_pooling(const std::string& name,
@@ -274,6 +280,7 @@ struct onnx_parser
                                   std::vector<instruction_ref> args)
     {
         op::pooling op{ends_with(name, "MaxPool") ? "max" : "average"};
+        auto l0 = args[0];
         if(starts_with(name, "Global"))
         {
             auto lens  = args.front()->get_shape().lens();
@@ -281,18 +288,23 @@ struct onnx_parser
         }
         if(contains(attributes, "pads"))
         {
-            std::vector<std::size_t> padding(4);
-            copy(attributes["pads"].ints(), padding.begin());
+            std::vector<std::int64_t> padding;
+            copy(attributes["pads"].ints(), std::back_inserter(padding));
             if(padding.size() != 4)
             {
                 MIGRAPHX_THROW("padding should have 4 values");
             }
             if(padding[0] != padding[2] || padding[1] != padding[3])
             {
-                MIGRAPHX_THROW("migraphx does not support asymetric padding");
+                // insert zeros for pad op (args[0] has 4 dims)
+                padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
+                l0 = prog.add_instruction(op::pad{padding}, l0);
+            }
+            else
+            {
+                op.padding[0] = padding[0];
+                op.padding[1] = padding[1];
             }
-            op.padding[0] = padding[0];
-            op.padding[1] = padding[1];
         }
         if(contains(attributes, "strides"))
         {
@@ -305,13 +317,14 @@ struct onnx_parser
         if(contains(attributes, "auto_pad"))
         {
             auto s = attributes["auto_pad"].s();
-            if(to_upper(s) != "NOTSET")
+            if(s.find("SAME_UPPER") == std::string::npos)
             {
-                MIGRAPHX_THROW("auto_pad is not supported for pooling");
+                MIGRAPHX_THROW("auto_pad only supports SAME_UPPER for pooling");
             }
+            op.padding_mode = op::padding_mode_t::same;
         }
 
-        return prog.add_instruction(op, std::move(args));
+        return prog.add_instruction(op, l0);
     }
 
     instruction_ref