Merge

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_generic_op.cpp

@@ -10,6 +10,7 @@ struct parse_generic_op : op_parser<parse_generic_op>
 {
     std::vector<op_desc> operators() const
     {
+        // clang-format off
         return {{"Abs", "abs"},
                 {"Acos", "acos"},
                 {"Acosh", "acosh"},
@@ -27,7 +28,9 @@ struct parse_generic_op : op_parser<parse_generic_op>
                 {"Flatten", "flatten"},
                 {"Floor", "floor"},
                 {"Gather", "gather"},
+                {"GatherND", "gathernd"},
                 {"Identity", "identity"},
+                {"IsNaN", "isnan"},
                 {"LeakyRelu", "leaky_relu"},
                 {"Log", "log"},
                 {"LRN", "lrn"},
@@ -36,8 +39,6 @@ struct parse_generic_op : op_parser<parse_generic_op>
                 {"Reciprocal", "recip"},
                 {"Relu", "relu"},
                 {"Round", "round"},
-                {"Scatter", "scatter"},
-                {"ScatterElements", "scatter"},
                 {"Sigmoid", "sigmoid"},
                 {"Sign", "sign"},
                 {"Sin", "sin"},
@@ -46,6 +47,7 @@ struct parse_generic_op : op_parser<parse_generic_op>
                 {"Tan", "tan"},
                 {"Tanh", "tanh"},
                 {"Not", "not"}};
+        // clang-format on
     }
 
     bool needs_contiguous(const std::string& op_name) const

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_mean.cpp

@@ -2,6 +2,7 @@
 #include <migraphx/onnx/checks.hpp>
 #include <migraphx/instruction.hpp>
 #include <migraphx/make_op.hpp>
+#include <migraphx/ranges.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -9,6 +10,9 @@ namespace onnx {
 
 struct parse_mean : op_parser<parse_mean>
 {
+    const std::set<shape::type_t> float_types = {
+        shape::float_type, shape::half_type, shape::double_type};
+
     std::vector<op_desc> operators() const { return {{"Mean"}}; }
 
     /// Calculates the element-wise mean of n>=1 input tensors
@@ -24,14 +28,29 @@ struct parse_mean : op_parser<parse_mean>
         auto divisor = info.add_literal(
             migraphx::literal{migraphx::shape{args[0]->get_shape().type()}, {num_data}});
 
-        return std::accumulate(args.begin(), args.end(), args[0], [&](auto& mean, auto& data_i) {
-            // Pre-divide each tensor element-wise by n to reduce risk of overflow during summation
-            data_i = info.add_broadcastable_binary_op("div", data_i, divisor);
-
-            if(data_i != args[0])
-                return info.add_broadcastable_binary_op("add", mean, data_i);
-            return data_i;
+        if(contains(float_types, args[0]->get_shape().type()))
+        {
+            return std::accumulate(args.begin() + 1,
+                                   args.end(),
+                                   info.add_broadcastable_binary_op("div", args[0], divisor),
+                                   [&](auto mean, auto data_i) {
+                                       // Pre-divide each tensor element-wise by n to reduce risk of
+                                       // overflow during summation
+                                       auto div =
+                                           info.add_broadcastable_binary_op("div", data_i, divisor);
+                                       return info.add_broadcastable_binary_op("add", mean, div);
+                                   });
+        }
+        else
+        {
+            // Compute sum before division for integral types
+            auto sum = std::accumulate(
+                args.begin() + 1, args.end(), args[0], [&](auto accum, auto data_i) {
+                    return info.add_broadcastable_binary_op("add", accum, data_i);
                 });
+
+            return info.add_broadcastable_binary_op("div", sum, divisor);
+        }
     }
 };
 

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>
@@ -18,7 +19,9 @@ struct parse_pooling : op_parser<parse_pooling>
         return {{"AveragePool", "average"},
                 {"GlobalAveragePool", "average"},
                 {"GlobalMaxPool", "max"},
-                {"MaxPool", "max"}};
+                {"MaxPool", "max"},
+                {"LpPool", "lpnorm"},
+                {"GlobalLpPool", "lpnorm"}};
     }
 
     instruction_ref parse(const op_desc& opd,
@@ -26,8 +29,16 @@ struct parse_pooling : op_parser<parse_pooling>
                           onnx_parser::node_info info,
                           std::vector<instruction_ref> args) const
     {
+        const std::unordered_map<std::string, op::pooling_mode> mode_map = {
+            {"max", op::pooling_mode::max},
+            {"average", op::pooling_mode::average},
+            {"lpnorm", op::pooling_mode::lpnorm}};
         std::string mode = opd.op_name;
-        operation op     = make_op("pooling", {{"mode", mode}});
+        if(not contains(mode_map, mode))
+        {
+            MIGRAPHX_THROW("onnx pooling mode must be [\"max\", \"average\", \"lpnorm\"]");
+        }
+        operation op = make_op("pooling", {{"mode", mode_map.at(mode)}});
         value values = op.to_value();
         auto l0      = args[0];
         auto in_lens = l0->get_shape().lens();
@@ -67,11 +78,18 @@ struct parse_pooling : op_parser<parse_pooling>
                 kdims, values["lengths"].size(), "PARSE_POOLING: inconsistent lengths");
         }
 
+        // lp_order attribute
+        if(contains(info.attributes, "p"))
+        {
+            values["lp_order"] = info.attributes.at("p").i();
+        }
+
         // ensure pads availabe only when auto_pad is "NOT_SET"
         check_padding_mode(info, "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();
@@ -110,7 +128,7 @@ struct parse_pooling : op_parser<parse_pooling>
             std::fill_n(values["stride"].begin(), kdims, 1);
         }
         // used to calculate the supposed output shape
-        std::vector<int64_t> orig_padding(paddings.begin(), paddings.end());
+        std::vector<int64_t> orig_padding = paddings;
 
         std::vector<int64_t> slice_start;
         std::vector<int64_t> slice_end;

src/onnx/parse_reversesequence.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 ReverseSequence ONNX operator.
+/*!
+  Reverses the data along the time axis for the batches along the batch axis.
+  The sequence lengths can be given to reverse up to the given length for each batch, keeping the
+  rest of the sequence in the original order. Variable sequence_lens is not supported in this
+  version of MIGraphX. You can pass the sequence_lens either as a constant node or an attribute. The
+  batch axis and time axis must be [0, 1] and not the same.
+*/
+struct parse_reversesequence : op_parser<parse_reversesequence>
+{
+    std::vector<op_desc> operators() const { return {{"ReverseSequence"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& parser,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        int batch_axis = 1;
+        if(contains(info.attributes, "batch_axis"))
+        {
+            batch_axis = info.attributes.at("batch_axis").i();
+        }
+        if(batch_axis != 0 and batch_axis != 1)
+        {
+            MIGRAPHX_THROW("REVERSESEQUENCE: batch axis not 0 or 1");
+        }
+
+        int time_axis = 0;
+        if(contains(info.attributes, "time_axis"))
+        {
+            time_axis = info.attributes.at("time_axis").i();
+        }
+        if(time_axis != 0 and time_axis != 1)
+        {
+            MIGRAPHX_THROW("REVERSESEQUENCE: time axis not 0 or 1");
+        }
+
+        if(time_axis == batch_axis)
+        {
+            MIGRAPHX_THROW("REVERSESEQUENCE: time axis and batch axis are the same");
+        }
+
+        auto input      = args[0];
+        auto input_lens = input->get_shape().lens();
+        if(input_lens.size() < 2)
+        {
+            MIGRAPHX_THROW("REVERSESEQUENCE: input tensor must have rank >= 2");
+        }
+
+        std::vector<int64_t> sequence_lens;
+        if(args.size() == 2)
+        {
+            migraphx::argument seq_lens_arg = args.back()->eval();
+            check_arg_empty(seq_lens_arg, "REVERSESEQUENCE: cannot handle variable sequence_lens");
+            seq_lens_arg.visit([&](auto s) { sequence_lens.assign(s.begin(), s.end()); });
+        }
+        else if(contains(info.attributes, "sequence_lens"))
+        {
+            literal s = parser.parse_value(info.attributes.at("sequence_lens"));
+            s.visit([&](auto v) { sequence_lens.assign(v.begin(), v.end()); });
+        }
+        auto batch_size = input_lens[batch_axis];
+        auto time_size  = input_lens[time_axis];
+
+        // this condition may still work if sequence_len's shape was incorrect
+        if(sequence_lens.size() != batch_size)
+        {
+            MIGRAPHX_THROW("REVERSESEQUENCE: sequence_lens has incorrect shape");
+        }
+
+        instruction_ref ret;
+
+        auto add_slice = [&info, &input, batch_axis, time_axis](int b, int t_start, int t_end) {
+            return info.add_instruction(make_op("slice",
+                                                {{"axes", {batch_axis, time_axis}},
+                                                 {"starts", {b, t_start}},
+                                                 {"ends", {b + 1, t_end}}}),
+                                        input);
+        };
+
+        for(int b = 0; b < batch_size; ++b)
+        {
+            instruction_ref s0;
+            if(sequence_lens[b] > 1)
+            {
+                s0 = add_slice(b, 0, sequence_lens[b]);
+                s0 = info.add_instruction(make_op("reverse", {{"axes", {time_axis}}}), s0);
+
+                // if reversed less than whole batch, concat rest of batch
+                if(sequence_lens[b] < time_size)
+                {
+                    auto s1 = add_slice(b, sequence_lens[b], time_size);
+                    s0 = info.add_instruction(make_op("concat", {{"axis", time_axis}}), s0, s1);
+                }
+            }
+            else
+            { // cases where nothing changes
+                s0 = add_slice(b, 0, time_size);
+            }
+            if(b == 0)
+            {
+                ret = s0;
+            }
+            else
+            {
+                ret = info.add_instruction(make_op("concat", {{"axis", batch_axis}}), ret, s0);
+            }
+        }
+        return ret;
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

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_scatter.cpp

+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_scatter : op_parser<parse_scatter>
+{
+    std::vector<op_desc> operators() const { return {{"ScatterElements"}, {"Scatter"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& /*parser*/,
+                          const onnx_parser::node_info& info,
+                          const std::vector<instruction_ref>& args) const
+    {
+        operation op;
+
+        std::string op_name = "scatter_none";
+        int axis            = 0;
+
+        if(contains(info.attributes, "axis"))
+            axis = info.attributes.at("axis").i();
+        if(contains(info.attributes, "reduction"))
+        {
+            std::string reduction_att(info.attributes.at("reduction").s());
+            // check for a valid reduction attribute.  We have an operator for each one.
+            if(not contains({"none", "add", "mul"}, reduction_att))
+                MIGRAPHX_THROW("PARSE_SCATTER: unsupported reduction mode " + reduction_att);
+            // merge scatter with reduction attribute to specify which scatter operation.  Future
+            // reduction op names should follow this pattern and should also be added to the check
+            // above.
+            op_name = std::string("scatter_") + reduction_att;
+        }
+        op = migraphx::make_op(op_name, {{"axis", axis}});
+        return info.add_instruction(op, args);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_scatternd.cpp

+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_scatternd : op_parser<parse_scatternd>
+{
+    std::vector<op_desc> operators() const { return {{"ScatterND"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& /*parser*/,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref>& args) const
+    {
+        if(contains(info.attributes, "reduction"))
+        {
+            if(info.attributes.at("reduction").s() == "add")
+                return info.add_instruction(migraphx::make_op("scatternd_add"), args);
+            if(info.attributes.at("reduction").s() == "mul")
+                return info.add_instruction(migraphx::make_op("scatternd_mul"), args);
+        }
+
+        return info.add_instruction(migraphx::make_op("scatternd_none"), args);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

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/onnx/parse_squeeze.cpp

@@ -30,11 +30,11 @@ struct parse_squeeze : op_parser<parse_squeeze>
                           std::vector<instruction_ref> args) const
     {
         auto op = parser.load(opd.op_name, info);
-        std::vector<int64_t> axes;
         if(args.size() == 2)
         {
             auto arg_axes = args.at(1)->eval();
             check_arg_empty(arg_axes, "PARSE_" + opd.op_name + ": cannot handle variable axes!");
+            std::vector<int64_t> axes;
             arg_axes.visit([&](auto s) { axes.assign(s.begin(), s.end()); });
             op = assign_axes(op, axes);
         }

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", "lpnorm"};
+    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/opt/memory_coloring.cpp

@@ -4,11 +4,11 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-void memory_coloring::apply(module& p) const
+void memory_coloring::apply(module& m) const
 {
     if(!enabled(MIGRAPHX_DISABLE_MEMORY_COLORING{}))
     {
-        memory_coloring_impl opt(&p, allocation_op, verify);
+        memory_coloring_impl opt(&m, allocation_op, verify);
         opt.run();
     }
 }

src/process.cpp

@@ -20,7 +20,6 @@ int exec(const std::string& cmd, const std::function<void(const char*)>& std_out
     int ec = 0;
     if(enabled(MIGRAPHX_TRACE_CMD_EXECUTE{}))
         std::cout << cmd << std::endl;
-    std::array<char, 128> buffer;
     auto closer = [&](FILE* stream) {
         auto status = pclose(stream);
         ec          = WIFEXITED(status) ? 0 : WEXITSTATUS(status); // NOLINT
@@ -30,6 +29,7 @@ int exec(const std::string& cmd, const std::function<void(const char*)>& std_out
         std::unique_ptr<FILE, decltype(closer)> pipe(popen(cmd.c_str(), "r"), closer); // NOLINT
         if(!pipe)
             MIGRAPHX_THROW("popen() failed: " + cmd);
+        std::array<char, 128> buffer;
         while(fgets(buffer.data(), buffer.size(), pipe.get()) != nullptr)
             std_out(buffer.data());
     }

src/program.cpp

@@ -353,13 +353,20 @@ std::vector<argument> program::eval(parameter_map params) const
 
     if(trace_level > 0)
     {
+        std::unordered_map<instruction_ref, std::string> ins_out;
+        // get instruction names
+        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);
+                                std::cout << "Run instruction: " << ins_out.at(ins) << std::endl;
                                 timer t{};
                                 auto result = check_context(f);
                                 double t1   = t.record<milliseconds>();
@@ -742,6 +749,14 @@ 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();
@@ -816,7 +831,8 @@ void generic_get_unused_modules(Map& m, const std::vector<T*>& mods, OutputItera
     std::transform(mods.begin(), mods.end(), std::inserter(used, used.end()), [](auto&& mod) {
         return mod->name();
     });
-    transform_if(m.begin(),
+    transform_if(
+        m.begin(),
         m.end(),
         out,
         [&](auto&& pp) { return not contains(used, pp.first); },

src/propagate_constant.cpp

@@ -3,6 +3,7 @@
 #include <migraphx/matcher.hpp>
 #include <migraphx/literal.hpp>
 #include <migraphx/functional.hpp>
+#include <migraphx/par_for.hpp>
 #include <unordered_set>
 
 namespace migraphx {
@@ -20,33 +21,42 @@ bool skip_propogate(instruction_ref ins)
     return false;
 }
 
-void propagate_constant::apply(module& p) const
+bool is_const(instruction_ref ins) { return ins->can_eval() and not skip_propogate(ins); }
+
+void propagate_constant::apply(module& m) const
 {
-    for(auto i : iterator_for(p))
-    {
-        if(i->name() != "@literal")
-            continue;
-        if(i->outputs().empty())
-            continue;
-        fix([&](auto self, auto ins) {
-            std::unordered_set<instruction_ref> children(ins->outputs().begin(),
-                                                         ins->outputs().end());
-            for(auto child : children)
-            {
-                if(child->name() == "@literal" or skip_propogate(child))
+    std::unordered_set<instruction_ref> const_instrs;
+    auto last = std::prev(m.end());
+
+    // Find instructions that can be evaluated to a literal
+    for(auto i : iterator_for(m))
     {
-                    self(child);
+        if(is_const(i) and i != last)
             continue;
+
+        std::copy_if(
+            i->inputs().begin(),
+            i->inputs().end(),
+            std::inserter(const_instrs, const_instrs.begin()),
+            [&](const instruction_ref ins) { return is_const(ins) and ins->name() != "@literal"; });
     }
-                auto r = child->eval();
-                if(not r.empty())
+
+    // Compute literals in parallel
+    std::vector<instruction_ref> const_instrs_vec{const_instrs.begin(), const_instrs.end()};
+    std::vector<argument> literals(const_instrs_vec.size());
+    par_for(const_instrs_vec.size(), 1, [&](const auto i) {
+        literals[i] = const_instrs_vec[i]->eval();
+    });
+
+    // Replace instructions in m
+    for(size_t i = 0; i < const_instrs_vec.size(); i++)
     {
-                    assert(r.get_shape() == child->get_shape());
-                    auto l = p.add_literal(r.get_shape(), r.data());
-                    self(p.replace_instruction(child, l));
-                }
+        if(not literals[i].empty())
+        {
+            assert(literals[i].get_shape() == const_instrs_vec[i]->get_shape());
+            auto l = m.add_literal(literals[i].get_shape(), literals[i].data());
+            m.replace_instruction(const_instrs_vec[i], l);
         }
-        })(i);
     }
 }
 

src/py/migraphx_py.cpp

@@ -3,8 +3,11 @@
 #include <pybind11/stl.h>
 #include <pybind11/numpy.h>
 #include <migraphx/program.hpp>
+#include <migraphx/instruction_ref.hpp>
+#include <migraphx/operation.hpp>
 #include <migraphx/quantization.hpp>
 #include <migraphx/generate.hpp>
+#include <migraphx/instruction.hpp>
 #include <migraphx/ref/target.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/tf.hpp>
@@ -95,7 +98,6 @@ migraphx::value to_value(py::kwargs kwargs)
         auto&& val = arg.second;
         visit_py(val, [&](auto py_val) { v[key] = py_val; });
     }
-
     return v;
 }
 } // namespace migraphx
@@ -211,12 +213,21 @@ 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", "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)
@@ -247,13 +258,46 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
 
     py::class_<migraphx::target>(m, "target");
 
-    py::class_<migraphx::module>(m, "module")
+    py::class_<migraphx::instruction_ref>(m, "instruction_ref");
+
+    py::class_<migraphx::module, std::unique_ptr<migraphx::module, py::nodelete>>(m, "module")
         .def("print", [](const migraphx::module& mm) { std::cout << mm << std::endl; })
-        .def("__eq__", std::equal_to<migraphx::module>{})
-        .def("__ne__", std::not_equal_to<migraphx::module>{})
+        .def(
+            "add_instruction",
+            [](migraphx::module& mm,
+               const migraphx::operation& op,
+               std::vector<migraphx::instruction_ref>& args,
+               std::vector<migraphx::module*>& mod_args) {
+                return mm.add_instruction(op, args, mod_args);
+            },
+            py::arg("op"),
+            py::arg("args"),
+            py::arg("mod_args") = std::vector<migraphx::module*>{})
+        .def(
+            "add_literal",
+            [](migraphx::module& mm, py::buffer data) {
+                py::buffer_info info = data.request();
+                auto literal_shape   = to_shape(info);
+                return mm.add_literal(literal_shape, reinterpret_cast<char*>(info.ptr));
+            },
+            py::arg("data"))
+        .def(
+            "add_parameter",
+            [](migraphx::module& mm, const std::string& name, const migraphx::shape shape) {
+                return mm.add_parameter(name, shape);
+            },
+            py::arg("name"),
+            py::arg("shape"))
+        .def(
+            "add_return",
+            [](migraphx::module& mm, std::vector<migraphx::instruction_ref>& args) {
+                return mm.add_return(args);
+            },
+            py::arg("args"))
         .def("__repr__", [](const migraphx::module& mm) { return migraphx::to_string(mm); });
 
     py::class_<migraphx::program>(m, "program")
+        .def(py::init([]() { return migraphx::program(); }))
         .def("get_parameter_names", &migraphx::program::get_parameter_names)
         .def("get_parameter_shapes", &migraphx::program::get_parameter_shapes)
         .def("get_output_shapes", &migraphx::program::get_output_shapes)
@@ -268,11 +312,11 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
             py::arg("t"),
             py::arg("offload_copy") = true,
             py::arg("fast_math")    = true)
-        .def("get_main_module",
-             [](migraphx::program& p) {
-                 auto* mm = p.get_main_module();
-                 return *mm;
-             })
+        .def("get_main_module", [](const migraphx::program& p) { return p.get_main_module(); })
+        .def(
+            "create_module",
+            [](migraphx::program& p, const std::string& name) { return p.create_module(name); },
+            py::arg("name"))
         .def("run",
              [](migraphx::program& p, py::dict params) {
                  migraphx::parameter_map pm;
@@ -303,15 +347,15 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
 
         .def("name", &migraphx::operation::name);
 
-    m.def("parse_tf",
+    m.def(
+        "parse_tf",
         [](const std::string& filename,
            bool is_nhwc,
            unsigned int batch_size,
            std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
            std::vector<std::string> output_names) {
             return migraphx::parse_tf(
-                  filename,
-                  migraphx::tf_options{is_nhwc, batch_size, map_input_dims, output_names});
+                filename, migraphx::tf_options{is_nhwc, batch_size, map_input_dims, output_names});
         },
         "Parse tf protobuf (default format is nhwc)",
         py::arg("filename"),
@@ -320,7 +364,8 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
         py::arg("map_input_dims") = std::unordered_map<std::string, std::vector<std::size_t>>(),
         py::arg("output_names")   = std::vector<std::string>());
 
-    m.def("parse_onnx",
+    m.def(
+        "parse_onnx",
         [](const std::string& filename,
            unsigned int default_dim_value,
            std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
@@ -343,7 +388,8 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
         py::arg("print_program_on_error") = false,
         py::arg("max_loop_iterations")    = 10);
 
-    m.def("parse_onnx_buffer",
+    m.def(
+        "parse_onnx_buffer",
         [](const std::string& onnx_buffer,
            unsigned int default_dim_value,
            std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims,
@@ -363,7 +409,8 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
         py::arg("skip_unknown_operators") = false,
         py::arg("print_program_on_error") = false);
 
-    m.def("load",
+    m.def(
+        "load",
         [](const std::string& name, const std::string& format) {
             migraphx::file_options options;
             options.format = format;
@@ -373,7 +420,8 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
         py::arg("filename"),
         py::arg("format") = "msgpack");
 
-    m.def("save",
+    m.def(
+        "save",
         [](const migraphx::program& p, const std::string& name, const std::string& format) {
             migraphx::file_options options;
             options.format = format;
@@ -386,6 +434,7 @@ MIGRAPHX_PYBIND11_MODULE(migraphx, m)
 
     m.def("get_target", &migraphx::make_target);
     m.def("generate_argument", &migraphx::generate_argument, py::arg("s"), py::arg("seed") = 0);
+    m.def("fill_argument", &migraphx::fill_argument, py::arg("s"), py::arg("value"));
     m.def("quantize_fp16",
           &migraphx::quantize_fp16,
           py::arg("prog"),

src/reduce_dims.cpp

@@ -16,11 +16,9 @@ bool reduce_dim(std::vector<shape>& shapes, std::size_t n)
         auto bstride = s.strides()[n + 1];
         auto blen    = s.lens()[n + 1];
 
-        if(astride == bstride * blen)
-        {
+        if(astride == bstride * blen or alen == 1)
             new_lens.push_back(alen * blen);
     }
-    }
     if(new_lens.size() != shapes.size())
         return false;
     std::size_t i = 0;
@@ -37,12 +35,25 @@ bool reduce_dim(std::vector<shape>& shapes, std::size_t n)
     return true;
 }
 
-std::size_t reduce_dim_all(std::vector<shape>& shapes, std::size_t n)
+void reduce_dim1(std::vector<shape>& shapes)
 {
-    while(reduce_dim(shapes, n) and n < shapes.size())
+    if(std::any_of(shapes.begin(), shapes.end(), [&](const auto& s) {
+           return s.lens().size() < 2 or s.lens().back() != 1;
+       }))
+        return;
+    for(auto& s : shapes)
     {
+        auto lens    = s.lens();
+        auto strides = s.strides();
+        lens.pop_back();
+        strides.pop_back();
+        s = shape{s.type(), lens, strides};
     }
+}
 
+std::size_t reduce_dim_all(std::vector<shape>& shapes, std::size_t n)
+{
+    while(reduce_dim(shapes, n) and n < shapes.size()) {}
     return n + 1;
 }
 void reduce_dim_all(std::vector<shape>& shapes)
@@ -50,6 +61,7 @@ void reduce_dim_all(std::vector<shape>& shapes)
     std::size_t n = 0;
     while(n < shapes.front().lens().size() - 1)
         n = reduce_dim_all(shapes, n);
+    reduce_dim1(shapes);
 }
 
 std::vector<std::size_t> base_lens(const std::vector<shape>& shapes)

src/register_target.cpp

-#include <migraphx/register_target.hpp>
 #include <unordered_map>
+#include <migraphx/register_target.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -11,7 +11,17 @@ std::unordered_map<std::string, target>& target_map()
 }
 
 void register_target(const target& t) { target_map()[t.name()] = t; }
-target make_target(const std::string& name) { return target_map().at(name); }
+
+target make_target(const std::string& name)
+{
+    const auto it = target_map().find(name);
+    if(it == target_map().end())
+    {
+        MIGRAPHX_THROW("Requested target '" + name + "' is not enabled or not supported");
+    }
+    return it->second;
+}
+
 std::vector<std::string> get_targets()
 {
     std::vector<std::string> result;

src/rewrite_batchnorm.cpp

@@ -14,9 +14,9 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-void rewrite_batchnorm::apply(module& p) const
+void rewrite_batchnorm::apply(module& m) const
 {
-    for(auto ins : iterator_for(p))
+    for(auto ins : iterator_for(m))
     {
         if(ins->name() != "batch_norm_inference")
             continue;
@@ -46,13 +46,13 @@ void rewrite_batchnorm::apply(module& p) const
             });
 
         auto broadcast   = op::broadcast{1, ins->get_shape().lens()};
-        auto a_ins       = p.add_literal({a.get_shape(), a.data()});
-        auto a_broadcast = p.insert_instruction(ins, broadcast, a_ins);
-        auto mul   = p.insert_instruction(ins, make_op("mul"), ins->inputs().front(), a_broadcast);
-        auto b_ins = p.add_literal({b.get_shape(), b.data()});
-        auto b_broadcast = p.insert_instruction(ins, broadcast, b_ins);
-        auto add         = p.insert_instruction(ins, make_op("add"), mul, b_broadcast);
-        p.replace_instruction(ins, add);
+        auto a_ins       = m.add_literal({a.get_shape(), a.data()});
+        auto a_broadcast = m.insert_instruction(ins, broadcast, a_ins);
+        auto mul   = m.insert_instruction(ins, make_op("mul"), ins->inputs().front(), a_broadcast);
+        auto b_ins = m.add_literal({b.get_shape(), b.data()});
+        auto b_broadcast = m.insert_instruction(ins, broadcast, b_ins);
+        auto add         = m.insert_instruction(ins, make_op("add"), mul, b_broadcast);
+        m.replace_instruction(ins, add);
     }
 }