manual merge

src/load_save.cpp

@@ -21,6 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
+#include <migraphx/instruction.hpp>
 #include <migraphx/load_save.hpp>
 #include <migraphx/file_buffer.hpp>
 #include <migraphx/json.hpp>
@@ -60,9 +61,29 @@ void save(const program& p, const std::string& filename, const file_options& opt
 {
     write_buffer(filename, save_buffer(p, options));
 }
+
+// MIOpen doesn't support serializing fusion plans with Find-2.0 APIs
+void print_miopen_warning(const program& p)
+{
+    auto mods = p.get_modules();
+    if(std::any_of(mods.begin(), mods.end(), [](const auto* m) {
+           return std::any_of(m->begin(), m->end(), [](const instruction& i) {
+               return i.name() == "gpu::miopen_fusion";
+           });
+       }))
+    {
+        std::cout << "[WARNING]: Program has miopen_fusion instructions for which tuned solutions "
+                     "are not stored inside serialized MIGraphX program. Consider serializing with "
+                     "MIGRAPHX_DISABLE_MIOPEN_FUSION=1 flag set."
+                  << std::endl;
+        ;
+    }
+}
+
 std::vector<char> save_buffer(const program& p, const file_options& options)
 {
     value v = p.to_value();
+    print_miopen_warning(p);
     std::vector<char> buffer;
     if(options.format == "msgpack")
     {

src/memory_coloring.cpp

@@ -23,9 +23,9 @@
  */
 #include <migraphx/memory_coloring.hpp>
 #include <migraphx/module.hpp>
-#include <migraphx/operators.hpp>
 #include <migraphx/instruction.hpp>
 #include <migraphx/iterator_for.hpp>
+#include <migraphx/make_op.hpp>
 #include <migraphx/functional.hpp>
 #include <migraphx/algorithm.hpp>
 #include <migraphx/ranges.hpp>
@@ -382,7 +382,8 @@ void memory_coloring::apply(module& m) const
         auto s             = ins->get_shape();
         std::size_t offset = seg.first * alignment;
         assert(offset < n);
-        m.replace_instruction(ins, op::load{s, offset}, mem);
+        m.replace_instruction(
+            ins, make_op("load", {{"shape", to_value(s)}, {"offset", offset}}), mem);
     }
 
     // Replace zero allocation
@@ -391,7 +392,8 @@ void memory_coloring::apply(module& m) const
         if(ins->name() != allocation_op)
             continue;
         assert(ins->get_shape().bytes() == 0);
-        m.replace_instruction(ins, op::load{ins->get_shape(), 0}, mem);
+        m.replace_instruction(
+            ins, make_op("load", {{"shape", to_value(ins->get_shape())}, {"offset", 0}}), mem);
     }
 
     // Remove scratch parameter if its not used

src/module.cpp

@@ -873,12 +873,11 @@ module::print_py(std::ostream& os,
             if(ins->name() == "@literal")
             {
                 os << mname << ".add_literal(";
-                bool use_abs = false;
-                ins->get_literal().visit([&](auto v) {
-                    use_abs = std::none_of(v.begin(), v.end(), [](auto x) { return x < 0; });
-                });
+                const bool use_abs = false;
                 // Disable abs for now
-                use_abs = false;
+                // ins->get_literal().visit([&](auto v) {
+                //     use_abs = std::none_of(v.begin(), v.end(), [](auto x) { return x < 0; });
+                // });
                 if(use_abs)
                     os << "migraphx.abs_literal(";
                 os << "migraphx.generate_argument(";

src/msgpack.cpp

@@ -25,6 +25,33 @@
 #include <migraphx/serialize.hpp>
 #include <msgpack.hpp>
 
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+// Leave an extra byte for error checking
+constexpr std::size_t msgpack_size_limit = std::numeric_limits<uint32_t>::max() - 1;
+
+template <class Range>
+std::size_t msgpack_chunk_size(const Range& r)
+{
+    return 1 + (r.size() - 1) / msgpack_size_limit;
+}
+
+template <class Iterator, class F>
+void msgpack_chunk_for_each(Iterator start, Iterator last, F f)
+{
+    while(std::distance(start, last) > msgpack_size_limit)
+    {
+        auto next = std::next(start, msgpack_size_limit);
+        f(start, next);
+        start = next;
+    }
+    f(start, last);
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
 namespace msgpack {
 MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
 {
@@ -63,16 +90,31 @@ MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
                 break;
             }
             case msgpack::type::BIN: {
+                // For backwards compatibility
                 v = migraphx::value::binary{o.via.bin.ptr, o.via.bin.size};
                 break;
             }
             case msgpack::type::ARRAY: {
-                migraphx::value r = migraphx::value::array{};
-                std::for_each(
-                    o.via.array.ptr,
-                    o.via.array.ptr + o.via.array.size,
-                    [&](const msgpack::object& so) { r.push_back(so.as<migraphx::value>()); });
-                v = r;
+                if(o.via.array.size != 0 and o.via.array.ptr->type == msgpack::type::BIN)
+                {
+                    auto bin = migraphx::value::binary{};
+                    std::for_each(
+                        o.via.array.ptr,
+                        o.via.array.ptr + o.via.array.size,
+                        [&](const msgpack::object& so) {
+                            bin.insert(bin.end(), so.via.bin.ptr, so.via.bin.ptr + so.via.bin.size);
+                        });
+                    v = bin;
+                }
+                else
+                {
+                    migraphx::value r = migraphx::value::array{};
+                    std::for_each(
+                        o.via.array.ptr,
+                        o.via.array.ptr + o.via.array.size,
+                        [&](const msgpack::object& so) { r.push_back(so.as<migraphx::value>()); });
+                    v = r;
+                }
                 break;
             }
             case msgpack::type::MAP: {
@@ -102,8 +144,12 @@ MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
         {
             const auto* data = reinterpret_cast<const char*>(x.data());
             auto size        = x.size();
-            o.pack_bin(size);
-            o.pack_bin_body(data, size);
+            o.pack_array(migraphx::msgpack_chunk_size(x));
+            migraphx::msgpack_chunk_for_each(
+                data, data + size, [&](const char* start, const char* last) {
+                    o.pack_bin(last - start);
+                    o.pack_bin_body(start, last - start);
+                });
             return o;
         }
     };
@@ -129,6 +175,8 @@ MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
                 o.pack_array(0);
                 return;
             }
+            if(v.size() > migraphx::msgpack_size_limit)
+                MIGRAPHX_THROW("Size is too large for msgpack");
             if(not v.front().get_key().empty())
             {
                 o.pack_map(v.size());

src/normalize_attributes.cpp

@@ -26,7 +26,7 @@
 #include <migraphx/normalize_attributes.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
-
+#include <migraphx/op/common.hpp>
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
@@ -49,6 +49,10 @@ auto tune_attribute(const std::vector<int64_t>& vec,
                     Message m)
 {
     std::vector<int64_t> result(vec);
+    if(result.empty())
+    {
+        return result;
+    };
     int64_t n_rank                                 = input_shape.ndim();
     std::vector<op::normalize_attribute> vec_attrs = val.to_vector<op::normalize_attribute>();
     if(contains(vec_attrs, op::normalize_attribute::use_output))
@@ -188,20 +192,27 @@ bool normalize_attributes(operation& op, const shape& input_shape)
     auto val   = op.to_value();
     if(attrs.contains("normalize_padding"))
     {
-        auto padding       = val.at(attrs.at("normalize_padding").to<std::string>());
-        auto padding_size  = padding.size();
-        auto padding_start = 2;
-
-        if(padding_size == 2 * (input_shape.ndim() - padding_start))
-            tuned = true;
-        else if(padding_size != (input_shape.ndim() - padding_start))
-            MIGRAPHX_THROW("inconsistent padding size");
-        else
+        bool use_auto_padding =
+            (val.contains("padding_mode") and
+             (val.at("padding_mode").to<int>() != migraphx::op::padding_mode_t::default_));
+        if(not use_auto_padding)
         {
-            auto result    = tune_pad_attribute(padding);
-            val["padding"] = result;
-            op.from_value(val);
-            tuned = true;
+            auto padding       = val.at(attrs.at("normalize_padding").to<std::string>());
+            auto padding_size  = padding.size();
+            auto padding_start = 2;
+            if(padding_size == 2 * (input_shape.ndim() - padding_start))
+                tuned = true;
+            else if(padding_size != (input_shape.ndim() - padding_start))
+            {
+                MIGRAPHX_THROW("normalize_attributes: inconsistent padding vector size ");
+            }
+            else
+            {
+                auto result    = tune_pad_attribute(padding);
+                val["padding"] = result;
+                op.from_value(val);
+                tuned = true;
+            }
         }
     }
     if(not attrs.contains("normalize_axes"))
@@ -251,5 +262,22 @@ bool normalize_attributes(operation& op, const shape& input_shape)
     return tuned;
 }
 
+std::vector<int64_t> normalize_axes(const std::vector<int64_t>& axes,
+                                    const shape& input_shape,
+                                    const value& attr_val,
+                                    const std::string& prefix)
+{
+    return tune_attribute(axes, {}, attr_val, input_shape, [&] { return prefix; });
+}
+
+std::vector<int64_t> normalize_indices(const std::vector<int64_t>& indices,
+                                       const std::vector<int64_t>& axes,
+                                       const shape& input_shape,
+                                       const value& attr_val,
+                                       const std::string& prefix)
+{
+    return tune_attribute(indices, axes, attr_val, input_shape, [&] { return prefix; });
+}
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/onnx/include/migraphx/onnx/onnx_parser.hpp

@@ -117,6 +117,7 @@ struct onnx_parser
     parse_graph(module* mod, const onnx::GraphProto& graph, bool inlining = false);
     literal parse_value(const onnx::AttributeProto& attr) const;
     literal parse_tensor(const onnx::TensorProto& t) const;
+    shape parse_type(const onnx::TypeProto& t) const;
     shape parse_type(const onnx::TypeProto& t, const std::vector<std::size_t>& input_dims) const;
 };
 

src/onnx/onnx_parser.cpp

@@ -357,10 +357,9 @@ parse_inputs(const onnx_parser& parser,
             }
 
             shape s;
-            std::vector<std::size_t> dims;
             if(parser.map_input_dims.count(name) > 0)
             {
-                dims = parser.map_input_dims.at(name);
+                std::vector<std::size_t> dims = parser.map_input_dims.at(name);
                 s    = parser.parse_type(input.type(), dims);
             }
             else if(parser.map_dyn_input_dims.count(name) > 0)
@@ -370,7 +369,7 @@ parse_inputs(const onnx_parser& parser,
             }
             else
             {
-                s = parser.parse_type(input.type(), dims);
+                s = parser.parse_type(input.type());
             }
             mod_insts[name] = mod->add_parameter(name, s);
         }
@@ -553,14 +552,9 @@ literal onnx_parser::parse_tensor(const onnx::TensorProto& t) const
     }
     MIGRAPHX_THROW("PARSE_TENSOR: Invalid tensor type");
 }
-shape onnx_parser::parse_type(const onnx::TypeProto& t,
-                              const std::vector<std::size_t>& input_dims) const
+shape onnx_parser::parse_type(const onnx::TypeProto& t) const
 {
     shape::type_t shape_type = get_type(t.tensor_type().elem_type());
-    if(not input_dims.empty())
-    {
-        return {shape_type, input_dims};
-    }
 
     std::vector<shape::dynamic_dimension> dynamic_dims;
     auto&& tensor_dims = t.tensor_type().shape().dim();
@@ -590,6 +584,15 @@ shape onnx_parser::parse_type(const onnx::TypeProto& t,
     return shape_from_dyn_dims(shape_type, dynamic_dims);
 }
 
+shape onnx_parser::parse_type(const onnx::TypeProto& t,
+                              const std::vector<std::size_t>& input_dims) const
+{
+    shape::type_t shape_type = get_type(t.tensor_type().elem_type());
+    if(input_dims.empty())
+        return {shape_type};
+    return {shape_type, input_dims};
+}
+
 shape::type_t get_type(int dtype)
 {
     switch(dtype)

src/onnx/parse_castlike.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/instruction.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_castlike : op_parser<parse_castlike>
+{
+    std::vector<op_desc> operators() const { return {{"CastLike"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& /*parser*/,
+                          const onnx_parser::node_info& info,
+                          const std::vector<instruction_ref>& args) const
+    {
+        if(not(args.size() == 2))
+        {
+            MIGRAPHX_THROW("PARSE_CASTLIKE: CastLike must have exactly 2 inputs!");
+        }
+        shape::type_t target_type = args[1]->get_shape().type();
+        return info.add_instruction(make_op("convert", {{"target_type", target_type}}), args[0]);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_constant.cpp

@@ -25,6 +25,7 @@
 #include <migraphx/ranges.hpp>
 #include <migraphx/literal.hpp>
 #include <migraphx/make_op.hpp>
+#include <migraphx/stringutils.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -39,16 +40,38 @@ struct parse_constant : op_parser<parse_constant>
                           onnx_parser::node_info info,
                           const std::vector<instruction_ref>& /*args*/) const
     {
-        literal v = parser.parse_value(info.attributes.at("value"));
+        static const std::vector<std::string> attributes = {
+            "value", "value_float", "value_floats", "value_int", "value_ints"};
+
+        std::vector<std::string> present_attributes;
+        std::copy_if(attributes.begin(),
+                     attributes.end(),
+                     std::back_inserter(present_attributes),
+                     [&](const std::string& a) { return contains(info.attributes, a); });
+
+        if(present_attributes.empty())
+        {
+            MIGRAPHX_THROW("Constant node does not contain any supported attribute");
+        }
+
+        if(present_attributes.size() > 1)
+        {
+            MIGRAPHX_THROW("Constant contains multiple attributes: " +
+                           join_strings(std::move(present_attributes), ", "));
+        }
+
+        // cppcheck-suppress accessMoved
+        auto&& attr = info.attributes[present_attributes[0]];
+        literal v   = parser.parse_value(attr);
+
         // return empty literal
         if(v.get_shape().elements() == 0)
         {
             return info.add_literal(literal{v.get_shape().type()});
         }
 
-        auto dim_size = info.attributes.at("value").t().dims_size();
         // if dim_size is 0, it is a scalar
-        if(dim_size == 0)
+        if(attr.has_t() and attr.t().dims_size() == 0)
         {
             migraphx::shape scalar_shape{v.get_shape().type()};
             return info.add_literal(migraphx::literal{scalar_shape, v.data()});

src/onnx/parse_constant_of_shape.cpp

@@ -49,15 +49,14 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
             {
                 MIGRAPHX_THROW("ConstantOfShape: attribute value can contain only 1 elements!");
             }
+            // convert to a scalar literal
+            l_val = literal(shape{l_val.get_shape().type(), {1}, {0}}, l_val.data());
         }
         else
         {
             l_val = literal({shape::float_type, {1}, {0}}, {0.0f});
         }
 
-        // input is empty, output is a scalar
-        auto type = l_val.get_shape().type();
-
         if(args.empty())
         {
             MIGRAPHX_THROW("ConstantOfShape : must have 1 input!");
@@ -65,30 +64,39 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
         else
         {
             migraphx::shape s;
-            // empty input tensor, output is a scalar
-            if(args[0]->get_shape().elements() == 0)
+            // input is empty, output is a scalar
+            auto type = l_val.get_shape().type();
+            migraphx::argument input = args[0]->eval();
+            if(not input.empty())
             {
-                s = migraphx::shape{type, {1}, {0}};
+                // empty input tensor, output is a scalar
+                if(args[0]->get_shape().elements() == 0)
+                {
+                    s = migraphx::shape{type, {1}, {0}};
+                }
+                else
+                {
+                    std::vector<std::size_t> dims;
+                    input.visit([&](auto ia) { dims.assign(ia.begin(), ia.end()); });
+                    s = migraphx::shape{type, dims};
+                }
+                literal l_out{};
+                l_val.visit([&](auto val) {
+                    using val_type = std::remove_cv_t<typename decltype(val)::value_type>;
+                    // l_val contains only one element
+                    std::vector<val_type> out_vec(s.elements(), val.front());
+                    l_out = literal(s, out_vec);
+                });
+                return info.add_literal(l_out);
             }
+            // has variable input (dynamic shape buffer)
             else
             {
-                migraphx::argument in = args[0]->eval();
-                check_arg_empty(in, "ConstantOfShape: dynamic shape is not supported");
-
-                std::vector<std::size_t> dims;
-                in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
-                s = migraphx::shape{type, dims};
+                auto dv_lit = info.add_literal(l_val);
+                auto alloc_ins =
+                    info.add_instruction(make_op("allocate", {{"buf_type", type}}), args[0]);
+                return info.add_instruction(make_op("fill"), dv_lit, alloc_ins);
             }
-
-            literal l_out{};
-            l_val.visit([&](auto val) {
-                using val_type = std::remove_cv_t<typename decltype(val)::value_type>;
-                // l_val contains only one element
-                std::vector<val_type> out_vec(s.elements(), val.front());
-                l_out = literal(s, out_vec);
-            });
-
-            return info.add_literal(l_out);
         }
     }
 };

src/onnx/parse_pooling.cpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -151,26 +151,6 @@ struct parse_pooling : op_parser<parse_pooling>
                 kdims, paddings.size() / 2, "PARSE_POOLING: inconsistent explicit paddings");
         }
 
-        if(contains(info.attributes, "auto_pad"))
-        {
-            if(in_shape.dynamic())
-            {
-                MIGRAPHX_THROW(
-                    "PARSE_POOLING: Auto padding pooling with dynamic input shape not supported");
-            }
-            else
-            {
-                values["padding"].clear();
-                // return paddings could be empty, then setting to 0 for no padding
-                cal_auto_padding_size(info,
-                                      values,
-                                      values["lengths"].to_vector<std::size_t>(),
-                                      {1, 1},
-                                      in_shape.lens(),
-                                      paddings);
-            }
-        }
-
         if(paddings.size() != 2 * kdims)
         {
             paddings.resize(kdims * 2);
@@ -192,6 +172,36 @@ struct parse_pooling : op_parser<parse_pooling>
         // used to calculate the supposed output shape
         std::vector<int64_t> orig_padding = paddings;
 
+        // TODO:  add parsing for dilations
+        if(contains(info.attributes, "auto_pad") and
+           to_upper(info.attributes["auto_pad"].s()) != "NOTSET")
+        {
+            auto auto_pad = to_upper(info.attributes["auto_pad"].s());
+            // don't use the given padding sizes, if any
+            // values["padding"].clear();
+            if(in_shape.dynamic())
+            {
+                // set padding_mode to trigger auto padding at runtime
+                bool is_same_upper     = (auto_pad.find("SAME_UPPER") != std::string::npos);
+                values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
+                                                       : to_value(op::padding_mode_t::same_lower);
+            }
+            else
+            {
+                // Calculate auto padding
+                // dilations (argument 4) not supported; default to all 1's
+                cal_auto_padding_size(info,
+                                      values,
+                                      values["lengths"].to_vector<std::size_t>(),
+                                      std::vector<size_t>(in_shape.ndim() - 2, 1),
+                                      in_shape.lens(),
+                                      paddings);
+                values["padding"] = paddings;
+                // default padding_mode indicates that padding sizes are not calculated dynamically
+                values["padding_mode"] = migraphx::op::padding_mode_t::default_;
+            }
+        }
+
         std::vector<int64_t> slice_start;
         std::vector<int64_t> slice_end;
         tune_padding_size(values, paddings, count_include_pad, slice_start);
@@ -208,8 +218,9 @@ struct parse_pooling : op_parser<parse_pooling>
             orig_padding.insert(orig_padding.begin(), 2, 0);
             op::pad pad{orig_padding, 0.0f};
             shape padded_shape = pad.compute_shape({l0->get_shape()});
-            auto out_lens      = make_op("pooling", values).compute_shape({padded_shape}).lens();
 
+            // make an op just to get its output shape
+            auto out_lens = make_op("pooling", values).compute_shape({padded_shape}).lens();
             // compute slice_end information
             slice_end.resize(slice_start.size());
             std::transform(out_lens.begin() + 2,

src/onnx/parse_randomuniform_ops.cpp

@@ -96,7 +96,7 @@ struct parse_randomuniform_ops : op_parser<parse_randomuniform_ops>
         if(contains(info.attributes, "seed"))
             gen.seed(info.attributes.at("seed").f());
 
-        std::uniform_real_distribution<> d(high, low);
+        std::uniform_real_distribution<> d(low, high);
         std::vector<double> rand_vals(out_shape.elements());
         std::generate(rand_vals.begin(), rand_vals.end(), [&]() { return d(gen); });
 

src/onnx/parse_resize.cpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -97,22 +97,19 @@ const auto& get_original_idx_op(const std::string& mode)
 static std::vector<int>
 calc_neighbor_points(const std::vector<std::vector<std::vector<std::size_t>>>& vvv_ind,
                      int i_dim,
-                     const std::vector<std::vector<std::size_t>>& vec_dims,
+                     std::vector<std::vector<std::size_t>> vec_dims,
                      const shape& in_s)
 {
     if(i_dim == vvv_ind.size())
     {
-        std::vector<int> vec_ind;
-        vec_ind.resize(vec_dims.size());
+        std::vector<int> vec_ind(vec_dims.size());
         std::transform(vec_dims.begin(), vec_dims.end(), vec_ind.begin(), [&](auto idx) {
             return static_cast<int>(in_s.index(idx));
         });
-
         return vec_ind;
     }
 
-    const auto& vv_ind = vvv_ind[i_dim];
-    const auto& vv_lo  = vv_ind.at(0);
+    const auto& vv_lo = vvv_ind[i_dim][0];
     std::vector<std::vector<std::size_t>> vec_dims1;
     for(std::size_t start = 0; start < vec_dims.size(); start += vv_lo.size())
     {
@@ -126,8 +123,8 @@ calc_neighbor_points(const std::vector<std::vector<std::vector<std::size_t>>>& v
                        });
     }
 
-    const auto& vv_hi = vv_ind.at(1);
-    for(std::size_t start = 0; start < vec_dims.size(); start += vv_lo.size())
+    const auto& vv_hi = vvv_ind[i_dim][1];
+    for(std::size_t start = 0; start < vec_dims.size(); start += vv_hi.size())
     {
         std::transform(vv_hi.begin(),
                        vv_hi.end(),
@@ -138,8 +135,8 @@ calc_neighbor_points(const std::vector<std::vector<std::vector<std::size_t>>>& v
                            return dim;
                        });
     }
-
-    return calc_neighbor_points(vvv_ind, i_dim + 1, vec_dims1, in_s);
+    vec_dims.clear();
+    return calc_neighbor_points(vvv_ind, i_dim + 1, std::move(vec_dims1), in_s);
 }
 
 static std::string get_coord_trans_mode(const onnx_parser::attribute_map& attr)
@@ -240,7 +237,7 @@ struct parse_resize : op_parser<parse_resize>
                 auto arg_out_s = arg->eval();
                 check_arg_empty(arg_out_s,
                                 "PARSE_" + opd.op_name + ": dynamic output size is not supported!");
-                arg_out_s.visit([&](auto ol) { out_lens.assign(ol.begin(), ol.end()); });
+                arg_out_s.visit([&](const auto& ol) { out_lens.assign(ol.begin(), ol.end()); });
 
                 if(out_lens.size() != in_lens.size())
                 {
@@ -267,7 +264,7 @@ struct parse_resize : op_parser<parse_resize>
                                     "PARSE_" + opd.op_name +
                                         ": dynamic input scale is not supported!");
 
-                    arg_scale.visit([&](auto v) { vec_scale.assign(v.begin(), v.end()); });
+                    arg_scale.visit([&](const auto& v) { vec_scale.assign(v.begin(), v.end()); });
                     if(in_lens.size() != vec_scale.size())
                     {
                         MIGRAPHX_THROW("PARSE_" + opd.op_name +
@@ -300,15 +297,15 @@ struct parse_resize : op_parser<parse_resize>
 
             // map out_idx to in_idx
             auto nearest_op = get_nearest_op(nearest_mode);
-            shape_for_each(out_s, [&](auto idx) {
-                auto in_idx = idx;
+            shape_for_each(out_s, [&](const auto& out_idx_v, size_t out_idx) {
+                std::vector<size_t> in_idx(out_idx_v.size());
                 for(auto ii = 0; ii < in_lens.size(); ++ii)
                 {
-                    auto idx_val = idx_op(in_lens[ii], out_lens[ii], idx[ii], vec_scale[ii]);
+                    auto idx_val = idx_op(in_lens[ii], out_lens[ii], out_idx_v[ii], vec_scale[ii]);
                     in_idx[ii]   = nearest_op(in_lens[ii], idx_val);
                 }
 
-                ind[out_s.index(idx)] = static_cast<int64_t>(in_s.index(in_idx));
+                ind[out_idx] = static_cast<int64_t>(in_s.index(in_idx));
             });
 
             shape ind_s{shape::int32_type, out_lens};
@@ -327,20 +324,18 @@ struct parse_resize : op_parser<parse_resize>
             std::vector<std::vector<std::vector<std::size_t>>> vvv_ind(n_dim, vv_ind);
             std::vector<std::vector<float>> delta(n_dim, std::vector<float>(out_elements));
 
-            shape_for_each(out_s, [&](auto idx) {
-                auto in_idx  = idx;
-                auto out_idx = out_s.index(idx);
+            shape_for_each(out_s, [&](const auto& out_idx_v, size_t out_idx) {
                 for(auto ii = 0; ii < in_lens.size(); ++ii)
                 {
-                    auto idx_val = idx_op(in_lens[ii], out_lens[ii], idx[ii], vec_scale[ii]);
+                    auto idx_val = idx_op(in_lens[ii], out_lens[ii], out_idx_v[ii], vec_scale[ii]);
                     vvv_ind[ii][0][out_idx] = nearest_floor(in_lens[ii], idx_val);
                     vvv_ind[ii][1][out_idx] = nearest_ceil(in_lens[ii], idx_val);
                     delta[ii][out_idx]      = idx_val - vvv_ind[ii][0][out_idx];
                 }
             });
 
-            std::vector<std::vector<std::size_t>> vec_dims(out_elements);
-            auto ind      = calc_neighbor_points(vvv_ind, 0, vec_dims, in_s);
+            auto ind = calc_neighbor_points(
+                vvv_ind, 0, std::vector<std::vector<std::size_t>>(out_elements), in_s);
             auto ind_lens = out_lens;
             ind_lens[0] *= (std::size_t{1} << n_dim);
             shape ind_s{shape::int32_type, ind_lens};

src/onnx/parse_roialign.cpp

@@ -37,15 +37,18 @@ struct parse_roialign : op_parser<parse_roialign>
     std::vector<op_desc> operators() const { return {{"RoiAlign"}}; }
 
     instruction_ref parse(const op_desc& /*opd*/,
-                          const onnx_parser& /*parser*/,
+                          const onnx_parser& parser,
                           onnx_parser::node_info info,
                           const std::vector<instruction_ref>& args) const
     {
-        std::string coord_trans_mode = "half_pixel";
-        if(contains(info.attributes, "coordinate_transformation_mode"))
+        std::string coord_trans_mode =
+            parser.opset_version >= 16 ? "half_pixel" : "output_half_pixel";
+
+        if(const auto* a = "coordinate_transformation_mode"; contains(info.attributes, a))
         {
-            coord_trans_mode = info.attributes.at("coordinate_transformation_mode").s();
+            coord_trans_mode = info.attributes.at(a).s();
         }
+
         if(not contains({"half_pixel", "output_half_pixel"}, coord_trans_mode))
         {
             MIGRAPHX_THROW("coordinate_transformation_mode \"" + coord_trans_mode +

src/onnx/parse_slice.cpp

@@ -34,16 +34,65 @@ namespace onnx {
 
 struct parse_slice : op_parser<parse_slice>
 {
+
     std::vector<op_desc> operators() const { return {{"Slice"}}; }
 
+    struct slice_desc
+    {
+        op::slice op;
+        std::vector<instruction_ref> op_args;
+        std::vector<int64_t> steps;
+        std::vector<int64_t> raxes;
+
+        void always_insert(instruction_ref arg) { op_args.insert(op_args.begin(), arg); }
+
+        std::vector<int64_t> insert(instruction_ref arg)
+        {
+            std::vector<int64_t> result;
+            migraphx::argument arg_value = arg->eval();
+            if(arg_value.empty())
+            {
+                op_args.insert(op_args.begin(), arg);
+            }
+            else
+            {
+                arg_value.visit([&](auto s) { result.assign(s.begin(), s.end()); });
+            }
+            return result;
+        }
+    };
+
     instruction_ref parse(const op_desc& /*opd*/,
                           const onnx_parser& parser,
-                          onnx_parser::node_info info,
-                          std::vector<instruction_ref> args) const
+                          const onnx_parser::node_info& info,
+                          const std::vector<instruction_ref>& args) const
     {
-        op::slice op;
+        auto sd  = construct_slice_desc(parser, info, args);
+        auto ins = info.add_instruction(sd.op, sd.op_args);
+        if(not sd.raxes.empty())
+        {
+            ins = info.add_instruction(make_op("reverse", {{"axes", sd.raxes}}), ins);
+        }
+        // If any steps are other than default 1, add a "steps" op
+        if(std::any_of(sd.steps.begin(), sd.steps.end(), [](auto s) { return std::abs(s) != 1; }))
+        {
+            std::vector<int64_t> nsteps;
+            std::transform(sd.steps.begin(),
+                           sd.steps.end(),
+                           std::back_inserter(nsteps),
+                           [](auto s) { return std::abs(s); });
+            return ins = info.add_instruction(
+                       make_op("step", {{"axes", sd.op.axes}, {"steps", nsteps}}), ins);
+        }
+        else
+            return ins;
+    }
 
-        std::vector<int64_t> steps;
+    slice_desc construct_slice_desc(const onnx_parser& parser,
+                                    onnx_parser::node_info info,
+                                    std::vector<instruction_ref> args) const
+    {
+        slice_desc sd;
 
         // slice can have up to 5 inputs, we first check the 5th one
         // to decide whether MIGRAPHX can handle this slice.
@@ -51,89 +100,73 @@ struct parse_slice : op_parser<parse_slice>
         {
             migraphx::argument step_arg = args.back()->eval();
             check_arg_empty(step_arg, "PARSE_SLICE: cannot handle variable steps for slice");
-            step_arg.visit([&](auto s) { steps.assign(s.begin(), s.end()); });
+            step_arg.visit([&](auto s) { sd.steps.assign(s.begin(), s.end()); });
         }
 
         if(args.size() >= 4)
         {
-            migraphx::argument axes_arg = args.at(3)->eval();
-            check_arg_empty(axes_arg, "PARSE_SLICE: cannot handle variable axes for slice");
-            axes_arg.visit([&](auto s) { op.axes.assign(s.begin(), s.end()); });
+            sd.op.axes = sd.insert(args.at(3));
         }
         else if(contains(info.attributes, "axes"))
         {
             literal s = parser.parse_value(info.attributes.at("axes"));
-            s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
+            s.visit([&](auto v) { copy(v, std::back_inserter(sd.op.axes)); });
         }
 
         if(args.size() >= 3)
         {
-            migraphx::argument end_arg = args.at(2)->eval();
-            check_arg_empty(end_arg, "PARSE_SLICE: cannot handle variable ends for slice");
-            end_arg.visit([&](auto s) { op.ends.assign(s.begin(), s.end()); });
+            sd.op.ends = sd.insert(args.at(2));
         }
         else if(contains(info.attributes, "ends"))
         {
             literal s = parser.parse_value(info.attributes.at("ends"));
-            s.visit([&](auto v) { copy(v, std::back_inserter(op.ends)); });
+            s.visit([&](auto v) { copy(v, std::back_inserter(sd.op.ends)); });
         }
 
         if(args.size() >= 2)
         {
-            migraphx::argument start_arg = args.at(1)->eval();
-            check_arg_empty(start_arg, "PARSE_SLICE: cannot handle variable starts for slice");
-            start_arg.visit([&](auto s) { op.starts.assign(s.begin(), s.end()); });
+            sd.op.starts = sd.insert(args.at(1));
         }
         else if(contains(info.attributes, "starts"))
         {
             literal s = parser.parse_value(info.attributes.at("starts"));
-            s.visit([&](auto v) { copy(v, std::back_inserter(op.starts)); });
+            s.visit([&](auto v) { copy(v, std::back_inserter(sd.op.starts)); });
         }
 
+        // data input argument
+        sd.always_insert(args.at(0));
+
         // If axes arg is not given, the default is all of them.
-        if(op.axes.empty())
+        if(sd.op.axes.empty() and sd.op_args.size() < 3)
         {
             std::vector<int64_t> axes(args[0]->get_shape().ndim());
             std::iota(axes.begin(), axes.end(), int64_t{0});
-            op.axes = axes;
+            sd.op.axes = axes;
         }
 
-        std::vector<int64_t> raxes;
+        if(not sd.steps.empty())
+        {
+            if(sd.op.starts.empty() or sd.op.ends.empty())
+                MIGRAPHX_THROW("PARSE_SLICE: steps and variable starts and ends is not supported");
+            if(sd.op.axes.empty())
+                MIGRAPHX_THROW("PARSE_SLICE: steps and variable axes is not supported");
+        }
 
-        assert(steps.empty() or steps.size() == op.axes.size());
-        assert(op.axes.size() == op.starts.size());
-        assert(op.axes.size() == op.ends.size());
+        assert(sd.steps.empty() or sd.steps.size() == sd.op.axes.size());
 
         // If any axes have negative step, prepare to add a "reverse" op
-        for(auto i : range(steps.size()))
+        for(auto i : range(sd.steps.size()))
         {
-            if(steps[i] >= 0)
+            if(sd.steps[i] >= 0)
                 continue;
-            op.starts[i] += 1;
-            if(op.starts[i] == 0)
-                op.starts[i] = INT_MAX;
-            op.ends[i] += 1;
-            raxes.push_back(op.axes[i]);
-            std::swap(op.starts[i], op.ends[i]);
-        }
-
-        auto ins = info.add_instruction(op, args[0]);
-        if(not raxes.empty())
-        {
-            ins = info.add_instruction(make_op("reverse", {{"axes", raxes}}), ins);
+            sd.op.starts[i] += 1;
+            if(sd.op.starts[i] == 0)
+                sd.op.starts[i] = INT_MAX;
+            sd.op.ends[i] += 1;
+            sd.raxes.push_back(sd.op.axes[i]);
+            std::swap(sd.op.starts[i], sd.op.ends[i]);
         }
-        // If any steps are other than default 1, add a "steps" op
-        if(std::any_of(steps.begin(), steps.end(), [](auto s) { return std::abs(s) != 1; }))
-        {
-            std::vector<int64_t> nsteps;
-            std::transform(steps.begin(), steps.end(), std::back_inserter(nsteps), [](auto s) {
-                return std::abs(s);
-            });
-            return ins = info.add_instruction(
-                       make_op("step", {{"axes", op.axes}, {"steps", nsteps}}), ins);
-        }
-        else
-            return ins;
+        return sd;
     }
 };
 

src/optimize_module.cpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -36,8 +36,12 @@ void optimize_module::apply(module_pass_manager& mpm) const
 {
     for(int i = 0; i < 2; i++)
     {
-        mpm.run_pass(simplify_reshapes{});
-        mpm.run_pass(simplify_algebra{});
+        // loop to further optimize after initial transformations
+        for(int j = 0; j < 2; j++)
+        {
+            mpm.run_pass(simplify_reshapes{});
+            mpm.run_pass(simplify_algebra{});
+        }
         mpm.run_pass(eliminate_common_subexpression{});
         mpm.run_pass(dead_code_elimination{});
         mpm.run_pass(propagate_constant{});

src/pad_calc.cpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -52,6 +52,11 @@ void calculate_padding(int64_t idx,
     }
 }
 
+/**
+ * Given the input array dimensions; kernel (wei_lens); strides; and dilations,
+ * calculate the padding value in each dimension.
+ *
+ */
 std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
                                            const std::vector<std::size_t>& wei_lens,
                                            const std::vector<std::size_t>& strides,
@@ -60,6 +65,7 @@ std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input
 {
     std::vector<std::size_t> padding;
     assert(input_lens.size() >= 3);
+    assert(input_lens.size() == wei_lens.size());
     std::size_t num_spatial_dims = input_lens.size() - 2;
     padding.resize(2 * num_spatial_dims);
     for(std::size_t i = 0; i < num_spatial_dims; i++)
@@ -88,6 +94,11 @@ std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input
     return padding;
 }
 
+/**
+ *   Calculate the correct output shape for a convolution with
+ *   a given input size and other parameters.
+ *
+ */
 shape compute_padded_shape(const shape& input,
                            const shape& weights,
                            const std::vector<std::size_t>& padding,
@@ -111,5 +122,33 @@ shape compute_padded_shape(const shape& input,
     return input.with_lens(output_lens);
 }
 
+/**
+ *   Calculate the correct output shape for a pooling with
+ *   a given input size and other parameters.  This uses
+ *   the same formula for pooling that compute_padded_shape() uses
+ *   for convolutions, but takes slightly different inputs.
+ *
+ */
+shape compute_padded_pool_shape(const shape& input,
+                                const shape& kernel,
+                                const std::vector<std::size_t>& padding,
+                                const std::vector<std::size_t>& stride,
+                                const std::vector<std::size_t>& dilation)
+{
+    const size_t num_spatial_dims = input.lens().size() - 2;
+
+    std::vector<size_t> output_lens{input.lens()[0], input.lens()[1]};
+    // calculate the output shape of the pooling: ((W - K + 2P) / S) + 1
+    for(size_t i = 0; i < num_spatial_dims; ++i)
+    {
+        auto padding_factor = padding[i] + padding[i + num_spatial_dims];
+        output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(
+            1,
+            (input.lens()[i + 2] - (1 + dilation[i] * (kernel.lens()[i] - 1)) + padding_factor) /
+                    stride[i] +
+                1)));
+    }
+    return input.with_lens(output_lens);
+}
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/program.cpp

@@ -223,7 +223,7 @@ void program::compile(const std::vector<target>& targets, std::vector<compile_op
     // Gather all the target roots
     std::unordered_multimap<std::size_t, module_ref> roots;
     auto mods = this->get_modules();
-    for(auto* mod : mods)
+    for(const auto* mod : mods)
     {
         for(const auto& ins : *mod)
         {
@@ -347,7 +347,7 @@ void program::finalize()
 template <class T>
 std::string classify(T x)
 {
-    switch(std::fpclassify(x))
+    switch(std::fpclassify(static_cast<double>(x)))
     {
     case FP_INFINITE: return "inf";
     case FP_NAN: return "nan";
@@ -548,7 +548,7 @@ std::vector<argument> program::eval(parameter_map params, execution_environment
             ins_out[x] = ss.str();
         });
         ret = generic_eval(*this, contexts, std::move(params), [&](instruction_ref ins, auto f) {
-            auto& ctx = contexts[ins->get_target_id()];
+            const auto& ctx = contexts[ins->get_target_id()];
             ctx.finish();
             std::cout << "Run instruction: " << ins_out.at(ins) << std::endl;
             timer t{};
@@ -624,7 +624,7 @@ std::string get_migraphx_version()
 program file version is for the data structure or format of the MXR file. Version should be bumped
 if any changes occur to the format of the MXR file.
 */
-const int program_file_version = 6;
+const int program_file_version = 7;
 
 value program::to_value() const
 {
@@ -728,7 +728,7 @@ static void mod_from_val(module_ref mod,
                                std::back_inserter(module_inputs),
                                [&](const value& i) { return map_mods.at(i.to<std::string>()); });
 
-                for(auto& smod : module_inputs)
+                for(const auto& smod : module_inputs)
                 {
                     mod_from_val(smod, v, instructions, map_mods);
                 }
@@ -1186,7 +1186,7 @@ void program::remove_unused_modules()
     std::vector<module*> unused;
     generic_get_unused_modules(
         impl->modules, generic_get_modules(this->get_main_module()), std::back_inserter(unused));
-    for(auto* m : unused)
+    for(const auto* m : unused)
         this->remove_module(m->name());
 }
 

src/py/CMakeLists.txt

@@ -24,6 +24,7 @@
 
 option(MIGRAPHX_ENABLE_PYTHON "Enable python bindings" ON)
 add_library(migraphx_py py_loader.cpp)
+migraphx_generate_export_header(migraphx_py)
 target_include_directories(migraphx_py PRIVATE include)
 target_link_libraries(migraphx_py PUBLIC migraphx)
 rocm_install_targets(TARGETS migraphx_py INCLUDE include)