merge changes from develop branch

src/json.cpp

@@ -133,6 +133,12 @@ std::string to_json_string(const value& val)
     return j.dump();
 }
 
+std::string to_pretty_json_string(const value& val, std::size_t indent)
+{
+    json j = val;
+    return j.dump(indent);
+}
+
 migraphx::value from_json_string(const char* str, std::size_t size)
 {
     json j = json::parse(str, str + size);

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

@@ -32,9 +32,20 @@ struct onnx_parser
         instruction_ref add_bias(const std::vector<instruction_ref>& args,
                                  instruction_ref curr_ins,
                                  uint64_t axis) const;
+
         instruction_ref add_broadcastable_binary_op(const std::string& op_name,
                                                     instruction_ref arg0,
                                                     instruction_ref arg1) const;
+
+        instruction_ref add_common_op(const std::string& op_name,
+                                      std::vector<instruction_ref> inputs) const;
+
+        template <class... Ts>
+        instruction_ref add_common_op(const std::string& op_name, Ts... xs) const
+        {
+            return add_common_op(op_name, {xs...});
+        }
+
         instruction_ref add_instruction(const operation& op,
                                         const std::vector<instruction_ref>& args) const;
 

src/onnx/onnx_parser.cpp

@@ -98,7 +98,13 @@ instruction_ref onnx_parser::node_info::add_broadcastable_binary_op(const std::s
                                                                     instruction_ref arg0,
                                                                     instruction_ref arg1) const
 {
-    return add_common_op(*mod, make_op(op_name), {arg0, arg1});
+    return this->add_common_op(op_name, arg0, arg1);
+}
+
+instruction_ref onnx_parser::node_info::add_common_op(const std::string& op_name,
+                                                      std::vector<instruction_ref> inputs) const
+{
+    return migraphx::add_common_op(*mod, make_op(op_name), std::move(inputs));
 }
 
 instruction_ref

src/onnx/padding.cpp

@@ -3,6 +3,7 @@
 #include <migraphx/pad_calc.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/make_op.hpp>
+#include <migraphx/op/common.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -94,7 +95,7 @@ void tune_padding_size(const value& v,
                        std::vector<int64_t>& s_start)
 {
     // maxpooling or count_include_pad is 1, no change is required.
-    if(v.at("mode").to<std::string>() == "max" or count_include_pad == 1)
+    if(v.at("mode").to<op::pooling_mode>() == op::pooling_mode::max or count_include_pad == 1)
     {
         return;
     }

src/onnx/parse_celu.cpp

+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/onnx/checks.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_celu : op_parser<parse_celu>
+{
+    std::vector<op_desc> operators() const { return {{"Celu"}}; }
+
+    instruction_ref parse(const op_desc&,
+                          const onnx_parser&,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        float alpha = 1.0;
+        if(contains(info.attributes, "alpha"))
+        {
+            alpha = info.attributes.at("alpha").f();
+        }
+        if(float_equal(alpha, 0.0f))
+        {
+            MIGRAPHX_THROW("CELU: alpha is zero (division by zero)");
+        }
+
+        auto input_lens = args[0]->get_shape().lens();
+        auto input_type = args[0]->get_shape().type();
+        if(input_type != migraphx::shape::float_type)
+        {
+            MIGRAPHX_THROW("CELU: input tensor not float type");
+        }
+        auto zero_lit = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}),
+            info.add_literal(migraphx::literal{migraphx::shape{input_type}, {0.}}));
+        auto one_lit = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}),
+            info.add_literal(migraphx::literal{migraphx::shape{input_type}, {1.}}));
+        auto alpha_lit = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}),
+            info.add_literal(migraphx::literal{migraphx::shape{input_type}, {alpha}}));
+        auto linear_part = info.add_instruction(migraphx::make_op("max"), zero_lit, args[0]);
+        auto divi        = info.add_instruction(migraphx::make_op("div"), args[0], alpha_lit);
+        auto expo        = info.add_instruction(migraphx::make_op("exp"), divi);
+        auto sub         = info.add_instruction(migraphx::make_op("sub"), expo, one_lit);
+        auto mul         = info.add_instruction(migraphx::make_op("mul"), alpha_lit, sub);
+        auto exp_part    = info.add_instruction(migraphx::make_op("min"), zero_lit, mul);
+        return info.add_instruction(migraphx::make_op("add"), linear_part, exp_part);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_clip.cpp

@@ -16,7 +16,6 @@ struct parse_clip : op_parser<parse_clip>
                           onnx_parser::node_info info,
                           std::vector<instruction_ref> args) const
     {
-        auto input_lens = args[0]->get_shape().lens();
         instruction_ref min_arg;
         instruction_ref max_arg;
         bool min_used = false;
@@ -45,29 +44,17 @@ struct parse_clip : op_parser<parse_clip>
             max_used      = true;
         }
 
-        if(min_used)
-        {
-            min_arg = info.add_instruction(make_op("multibroadcast", {{"out_lens", input_lens}}),
-                                           min_arg);
-        }
-
-        if(max_used)
-        {
-            max_arg = info.add_instruction(make_op("multibroadcast", {{"out_lens", input_lens}}),
-                                           max_arg);
-        }
-
         if(min_used and max_used)
         {
-            return info.add_instruction(make_op("clip"), args[0], min_arg, max_arg);
+            return info.add_common_op("clip", args[0], min_arg, max_arg);
         }
         else if(max_used)
         {
-            return info.add_instruction(make_op("min"), args[0], max_arg);
+            return info.add_broadcastable_binary_op("min", args[0], max_arg);
         }
         else if(min_used)
         {
-            return info.add_instruction(make_op("max"), args[0], min_arg);
+            return info.add_broadcastable_binary_op("max", args[0], min_arg);
         }
         else
         {

src/onnx/parse_eyelike.cpp

+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/onnx/checks.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_eyelike : op_parser<parse_eyelike>
+{
+    std::vector<op_desc> operators() const { return {{"EyeLike"}}; }
+
+    instruction_ref parse(const op_desc&,
+                          const onnx_parser&,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        auto input_shape = args[0]->get_shape();
+        auto input_lens  = input_shape.lens();
+        if(input_lens.size() != 2)
+        {
+            MIGRAPHX_THROW("EYELIKE: tensor input not of rank 2");
+        }
+        std::ptrdiff_t num_rows = input_lens.front();
+        std::ptrdiff_t num_cols = input_lens.back();
+
+        shape::type_t output_type = args[0]->get_shape().type();
+        if(contains(info.attributes, "dtype"))
+        {
+            output_type = get_type(info.attributes.at("dtype").i());
+        }
+
+        std::ptrdiff_t k = 0;
+        if(contains(info.attributes, "k"))
+        {
+            k = info.attributes.at("k").i();
+        }
+        if(k >= 0)
+        {
+            if(k >= num_cols)
+            {
+                std::ostringstream oss;
+                oss << "EYELIKE: positive k out of bounds, k = " << k << " num_cols = " << num_cols;
+                MIGRAPHX_THROW(oss.str());
+            }
+        }
+        else
+        {
+            if(std::abs(k) >= num_rows)
+            {
+                std::ostringstream oss;
+                oss << "EYELIKE: negative k out of bounds, k = " << k << " num_rows = " << num_cols;
+                MIGRAPHX_THROW(oss.str());
+            }
+        }
+
+        std::vector<char> eyelike_mat(num_rows * num_cols, 0);
+        for(std::ptrdiff_t i = 0; i < num_rows; ++i)
+        {
+            auto idx = i + k;
+            if(idx < num_cols and idx >= 0)
+                eyelike_mat[(num_cols + 1) * i + k] = char{1};
+        }
+        return info.add_literal(
+            migraphx::literal{migraphx::shape{output_type, input_lens}, eyelike_mat});
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_lpnormalization.cpp

+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/onnx/checks.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+//!  Parser for LpNormalization ONNX operator.
+/*!
+  Normalizes a tensor by the L1 or L2 norms along a given axis.
+  Norms that evaluate to 0 are changed to 1 to prevent division by zero.
+*/
+struct parse_lpnormalization : op_parser<parse_lpnormalization>
+{
+    std::vector<op_desc> operators() const { return {{"LpNormalization"}}; }
+
+    instruction_ref parse(const op_desc&,
+                          const onnx_parser&,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        int p = 2;
+        if(contains(info.attributes, "p"))
+        {
+            p = info.attributes.at("p").i();
+        }
+        if(p != 1 and p != 2)
+        {
+            MIGRAPHX_THROW("LPNORMALIZATION: only L1 and L2 norm supported");
+        }
+        auto input              = args.front();
+        auto input_shape        = input->get_shape();
+        const auto& input_lens  = input_shape.lens();
+        auto input_type         = input_shape.type();
+        std::ptrdiff_t num_axes = input_lens.size();
+        std::ptrdiff_t axis     = -1;
+        if(contains(info.attributes, "axis"))
+        {
+            axis = info.attributes.at("axis").i();
+            if(axis < -num_axes or axis >= num_axes)
+            {
+                // handled in normalize_attributes but throwing here might be clearer
+                MIGRAPHX_THROW("LPNORMALIZATION: selected axis out of bounds");
+            }
+        }
+        migraphx::instruction_ref p_val;
+        if(p == 1)
+        {
+            p_val = info.add_instruction(migraphx::make_op("abs"), input);
+        }
+        else
+        {
+            p_val = info.add_instruction(migraphx::make_op("mul"), input, input);
+        }
+
+        // need to check for zeros from lp norm to prevent division by zero
+        // change them to 1 for the element-wise division
+        auto norms =
+            info.add_instruction(migraphx::make_op("reduce_sum", {{"axes", {axis}}}), p_val);
+        if(p == 2)
+        {
+            norms = info.add_instruction(migraphx::make_op("sqrt"), norms);
+        }
+        // broadcast back to initial shape, negative axis option doesn't work with unidirectional
+        norms = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}), norms);
+        auto zero_mb = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}),
+            info.add_literal(migraphx::literal{migraphx::shape{input_type}, {0.}}));
+        auto one_mb = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", input_lens}}),
+            info.add_literal(migraphx::literal{migraphx::shape{input_type}, {1.}}));
+        auto is_zero = info.add_instruction(migraphx::make_op("equal"), norms, zero_mb);
+        auto norms_zeros_to_one =
+            info.add_instruction(migraphx::make_op("where"), is_zero, one_mb, norms);
+        return info.add_instruction(migraphx::make_op("div"), input, norms_zeros_to_one);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_pooling.cpp

@@ -2,6 +2,7 @@
 #include <migraphx/onnx/checks.hpp>
 #include <migraphx/onnx/padding.hpp>
 #include <migraphx/op/pad.hpp>
+#include <migraphx/op/pooling.hpp>
 #include <migraphx/instruction.hpp>
 #include <migraphx/ranges.hpp>
 #include <migraphx/stringutils.hpp>
@@ -27,7 +28,13 @@ struct parse_pooling : op_parser<parse_pooling>
                           std::vector<instruction_ref> args) const
     {
         std::string mode = opd.op_name;
-        operation op     = make_op("pooling", {{"mode", mode}});
+        if(mode != "max" && mode != "average")
+        {
+            MIGRAPHX_THROW("onnx pooling mode must be \"max\" or \"average\"");
+        }
+        operation op = make_op(
+            "pooling",
+            {{"mode", mode == "average" ? op::pooling_mode::average : op::pooling_mode::max}});
         value values = op.to_value();
         auto l0      = args[0];
         auto in_lens = l0->get_shape().lens();
@@ -72,6 +79,7 @@ struct parse_pooling : op_parser<parse_pooling>
 
         std::vector<int64_t> paddings;
         float pad_val = ((mode == "max") ? std::numeric_limits<float>::lowest() : 0.0f);
+
         if(contains(info.attributes, "pads"))
         {
             values["padding"].clear();

src/onnx/parse_roialign.cpp

+#include <migraphx/op/common.hpp>
 #include <migraphx/onnx/op_parser.hpp>
 #include <migraphx/onnx/checks.hpp>
 #include <migraphx/ranges.hpp>
@@ -28,10 +29,14 @@ struct parse_roialign : op_parser<parse_roialign>
                            "\": invalid value!");
         }
 
-        std::string mode = "avg";
+        migraphx::op::pooling_mode rmode(migraphx::op::pooling_mode::average);
         if(contains(info.attributes, "mode"))
         {
-            mode = info.attributes.at("mode").s();
+            // read mode; default is "avg"
+            if(info.attributes.at("mode").s() == "max")
+            {
+                rmode = migraphx::op::pooling_mode::max;
+            }
         }
 
         int64_t output_height = 1;
@@ -57,10 +62,9 @@ struct parse_roialign : op_parser<parse_roialign>
         {
             spatial_scale = info.attributes.at("spatial_scale").f();
         }
-
         return info.add_instruction(make_op("roialign",
                                             {{"coordinate_transformation_mode", coord_trans_mode},
-                                             {"mode", mode},
+                                             {"mode", rmode},
                                              {"output_height", output_height},
                                              {"output_width", output_width},
                                              {"sampling_ratio", sampling_ratio},

src/onnx/parse_size.cpp

+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/onnx/checks.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_size : op_parser<parse_size>
+{
+    std::vector<op_desc> operators() const { return {{"Size"}}; }
+
+    instruction_ref parse(const op_desc&,
+                          const onnx_parser&,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        return info.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::int64_type},
+                                                  {args[0]->get_shape().elements()}});
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/op_enums.cpp

+//
+//     Supporting functions for enum values used in operator parameters.
+//     These values are declared as "enum class" and should include << streaming operators
+//     to be able to write their values in human-readable format so users can
+//     save and edit model files.
+//
+#include <sstream>
+#include <migraphx/op/common.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+std::ostream& operator<<(std::ostream& os, pooling_mode v)
+{
+    // the strings for the enum are the same as the values used for onnx parsing
+    // but this enum is not onnx-specific:  strings must be converted when parsing tf
+    static const std::vector<std::string> pooling_mode_str = {"average", "max"};
+    os << pooling_mode_str[static_cast<std::underlying_type<pooling_mode>::type>(v)];
+    return os;
+}
+std::ostream& operator<<(std::ostream& os, rnn_direction v)
+{
+    static const std::vector<std::string> rnn_direction_str = {
+        "forward", "reverse", "bidirectional"};
+    os << rnn_direction_str[static_cast<std::underlying_type<rnn_direction>::type>(v)];
+    return os;
+}
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/program.cpp

@@ -353,17 +353,20 @@ std::vector<argument> program::eval(parameter_map params) const
 
     if(trace_level > 0)
     {
-        std::unordered_map<instruction_ref, std::string> ins_names;
+        std::unordered_map<instruction_ref, std::string> ins_out;
         // get instruction names
-        this->print(ins_names, [](auto, auto) {});
+        this->print([&](auto x, auto ins_names) {
+            std::stringstream ss;
+            instruction::print(ss, x, ins_names);
+            ins_out[x] = ss.str();
+        });
+
         return generic_eval(*this,
                             ctx,
                             std::move(params),
                             with_check_context([&](auto& ins, auto f, auto&& check_context) {
                                 ctx.finish();
-                                std::cout << "Run instruction: ";
-                                this->debug_print(ins, ins_names);
-                                std::cout << std::endl;
+                                std::cout << "Run instruction: " << ins_out.at(ins) << std::endl;
                                 timer t{};
                                 auto result = check_context(f);
                                 double t1   = t.record<milliseconds>();
@@ -742,6 +745,14 @@ void program::print(
     }
 }
 
+void program::print(
+    const std::function<void(instruction_ref ins,
+                             std::unordered_map<instruction_ref, std::string>)>& print_func) const
+{
+    std::unordered_map<instruction_ref, std::string> names;
+    this->print(names, print_func);
+}
+
 void program::print_graph(std::ostream& os, bool brief) const
 {
     const auto* mm = this->get_main_module();

src/py/migraphx_py.cpp

@@ -211,12 +211,21 @@ migraphx::shape to_shape(const py::buffer_info& info)
 MIGRAPHX_PYBIND11_MODULE(migraphx, m)
 {
     py::class_<migraphx::shape>(m, "shape")
-        .def(py::init<>())
+        .def(py::init([](py::kwargs kwargs) {
+            auto v    = migraphx::to_value(kwargs);
+            auto t    = migraphx::shape::parse_type(v.get("type", std::string{"float"}));
+            auto lens = v.get<std::size_t>("lens", {1});
+            if(v.contains("strides"))
+                return migraphx::shape(t, lens, v.at("strides").to_vector<std::size_t>());
+            else
+                return migraphx::shape(t, lens);
+        }))
         .def("type", &migraphx::shape::type)
         .def("lens", &migraphx::shape::lens)
         .def("strides", &migraphx::shape::strides)
         .def("elements", &migraphx::shape::elements)
         .def("bytes", &migraphx::shape::bytes)
+        .def("type_string", &migraphx::shape::type_string)
         .def("type_size", &migraphx::shape::type_size)
         .def("packed", &migraphx::shape::packed)
         .def("transposed", &migraphx::shape::transposed)

src/rewrite_pooling.cpp

@@ -38,7 +38,7 @@ void rewrite_pooling::apply(module& prog) const
         instruction_ref pooling{};
 
         // average pooling
-        if(op.mode == "average")
+        if(op.mode == op::pooling_mode::average)
         {
             pooling =
                 prog.insert_instruction(ins, make_op("reduce_mean", {{"axes", {1}}}), reshape);

src/rewrite_rnn.cpp

@@ -1426,14 +1426,5 @@ instruction_ref rewrite_rnn::pad_hidden_states(module& prog,
     return hs_padded;
 }
 
-namespace op {
-std::ostream& operator<<(std::ostream& os, rnn_direction v)
-{
-    std::vector<std::string> rnn_direction_str = {"forward", "reverse", "bidirectional"};
-    os << rnn_direction_str[static_cast<std::underlying_type<rnn_direction>::type>(v)];
-    return os;
-}
-} // namespace op
-
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/cpu/lowering.cpp

@@ -460,10 +460,10 @@ struct cpu_apply
         if(has_op("dnnl::pooling") and ins->get_shape().type() == shape::type_t::float_type and
            not v["ceil_mode"].to<bool>())
             return replace(ins, make_op("dnnl::pooling", op.to_value()));
-        std::string mode = v["mode"].to<std::string>();
-        if(mode == "max")
+        op::pooling_mode mode = v["mode"].to<op::pooling_mode>();
+        if(mode == op::pooling_mode::max)
             return replace(ins, make_op("cpu::pooling_max", v));
-        else if(mode == "average")
+        else if(mode == op::pooling_mode::average)
             return replace(ins, make_op("cpu::pooling_average", v));
         return ins;
     }

src/targets/cpu/pooling.cpp

@@ -129,7 +129,8 @@ struct dnnl_pooling : dnnl_extend_op<dnnl_pooling, dnnl::pooling_forward, op::po
 
     dnnl::pooling_forward::desc get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const
     {
-        auto algo  = op.mode == "max" ? dnnl::algorithm::pooling_max : dnnl::algorithm::pooling_avg;
+        auto algo = op.mode == op::pooling_mode::max ? dnnl::algorithm::pooling_max
+                                                     : dnnl::algorithm::pooling_avg;
         auto kdims = op.kdims();
         std::vector<size_t> padding_l(op.padding.begin(), op.padding.begin() + kdims);
         std::vector<size_t> padding_r(op.padding.begin() + kdims, op.padding.end());
@@ -145,5 +146,6 @@ struct dnnl_pooling : dnnl_extend_op<dnnl_pooling, dnnl::pooling_forward, op::po
 };
 
 } // namespace cpu
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/compile_hip.cpp

@@ -241,7 +241,8 @@ std::string enum_params(std::size_t count, std::string param)
 std::size_t compute_global(std::size_t n, std::size_t local)
 {
     std::size_t groups = (n + local - 1) / local;
-    std::size_t nglobal = std::min<std::size_t>(256, groups) * local;
+    // max possible number of blocks is set to 1B (1,073,741,824)
+    std::size_t nglobal = std::min<std::size_t>(1073741824, groups) * local;
     return nglobal;
 }
 

src/targets/gpu/compile_roialign.cpp

@@ -59,8 +59,8 @@ operation compile_roialign(context&, const std::vector<shape>& io_shapes, const
 
     // pooling_mode
     assert(val.contains("mode"));
-    auto mode           = val.at("mode").to<std::string>();
-    bool is_avg_pooling = (mode == "avg");
+    auto mode           = val.at("mode").to<migraphx::op::pooling_mode>();
+    bool is_avg_pooling = (mode == migraphx::op::pooling_mode::average);
     options.params += " -DIS_AVG_POOLING=" + std::to_string(static_cast<int>(is_avg_pooling));
 
     // coord_trans_mode