Merge

src/include/migraphx/sqlite.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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.
+ */
+#ifndef MIGRAPHX_GUARD_MIGRAPHX_SQLITE_HPP
+#define MIGRAPHX_GUARD_MIGRAPHX_SQLITE_HPP
+
+#include <migraphx/config.hpp>
+#include <migraphx/filesystem.hpp>
+#include <memory>
+#include <unordered_map>
+#include <vector>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct sqlite_impl;
+
+struct sqlite
+{
+    sqlite() = default;
+    static sqlite read(const fs::path& p);
+    static sqlite write(const fs::path& p);
+    std::vector<std::unordered_map<std::string, std::string>> execute(const std::string& s);
+
+    private:
+    std::shared_ptr<sqlite_impl> impl;
+};
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_MIGRAPHX_SQLITE_HPP

src/include/migraphx/stringutils.hpp

@@ -174,27 +174,27 @@ inline std::string interpolate_string(const std::string& input,
 }
 
 template <class Iterator>
-inline std::string to_string_range(Iterator start, Iterator last)
+inline std::string to_string_range(Iterator start, Iterator last, const char* delim = ", ")
 {
     std::stringstream ss;
     if(start != last)
     {
         ss << *start;
-        std::for_each(std::next(start), last, [&](auto&& x) { ss << ", " << x; });
+        std::for_each(std::next(start), last, [&](auto&& x) { ss << delim << x; });
     }
     return ss.str();
 }
 
 template <class Range>
-inline std::string to_string_range(const Range& r)
+inline std::string to_string_range(const Range& r, const char* delim = ", ")
 {
-    return to_string_range(r.begin(), r.end());
+    return to_string_range(r.begin(), r.end(), delim);
 }
 
 template <class T>
-inline std::string to_string_range(const std::initializer_list<T>& r)
+inline std::string to_string_range(const std::initializer_list<T>& r, const char* delim = ", ")
 {
-    return to_string_range(r.begin(), r.end());
+    return to_string_range(r.begin(), r.end(), delim);
 }
 
 template <class T>

src/include/migraphx/support_metric.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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.
+ */
+#ifndef MIGRAPHX_GUARD_MIGRAPHX_SUPPORT_METRIC_HPP
+#define MIGRAPHX_GUARD_MIGRAPHX_SUPPORT_METRIC_HPP
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+enum class support_metric
+{
+    latency,
+    throughput
+};
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_MIGRAPHX_SUPPORT_METRIC_HPP

src/include/migraphx/target.hpp

@@ -37,6 +37,8 @@
 #include <migraphx/compile_options.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/rank.hpp>
+#include <migraphx/support_metric.hpp>
+#include <migraphx/instruction_ref.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -61,6 +63,13 @@ struct target
      * @return The context to be used during compilation and execution.
      */
     context get_context() const;
+    /**
+     * @brief Check how well an instruction is supported on a target with the given metric
+     * @param ins Instruction to check if it's supported
+     * @param metric Used to define how the return value should be interpreted
+     * @return The value based on the chosen metric. Negative numbers mean unsupported
+     */
+    float is_supported(T&, instruction_ref ins, support_metric m) const;
     /**
      * @brief copy an argument to the current target.
      *
@@ -105,6 +114,12 @@ argument copy_from_target(T&, const argument& arg)
     return arg;
 }
 
+template <class T>
+float target_is_supported(T&, instruction_ref, support_metric)
+{
+    return 0;
+}
+
 #ifdef TYPE_ERASED_DECLARATION
 
 // Type-erased interface for:
@@ -117,6 +132,8 @@ struct target
     //
     context get_context() const;
     // (optional)
+    float is_supported(instruction_ref ins, support_metric m) const;
+    // (optional)
     argument copy_to(const argument& input) const;
     // (optional)
     argument copy_from(const argument& input) const;
@@ -207,6 +224,12 @@ struct target
         return (*this).private_detail_te_get_handle().get_context();
     }
 
+    float is_supported(instruction_ref ins, support_metric m) const
+    {
+        assert((*this).private_detail_te_handle_mem_var);
+        return (*this).private_detail_te_get_handle().is_supported(ins, m);
+    }
+
     argument copy_to(const argument& input) const
     {
         assert((*this).private_detail_te_handle_mem_var);
@@ -242,11 +265,31 @@ struct target
         virtual std::vector<pass> get_passes(context& ctx,
                                              const compile_options& options) const = 0;
         virtual context get_context() const                                        = 0;
+        virtual float is_supported(instruction_ref ins, support_metric m) const    = 0;
         virtual argument copy_to(const argument& input) const                      = 0;
         virtual argument copy_from(const argument& input) const                    = 0;
         virtual argument allocate(const shape& s) const                            = 0;
     };
 
+    template <class T>
+    static auto private_detail_te_default_is_supported(char,
+                                                       T&& private_detail_te_self,
+                                                       instruction_ref ins,
+                                                       support_metric m)
+        -> decltype(private_detail_te_self.is_supported(ins, m))
+    {
+        return private_detail_te_self.is_supported(ins, m);
+    }
+
+    template <class T>
+    static float private_detail_te_default_is_supported(float,
+                                                        T&& private_detail_te_self,
+                                                        instruction_ref ins,
+                                                        support_metric m)
+    {
+        return target_is_supported(private_detail_te_self, ins, m);
+    }
+
     template <class T>
     static auto
     private_detail_te_default_copy_to(char, T&& private_detail_te_self, const argument& input)
@@ -329,6 +372,12 @@ struct target
 
         context get_context() const override { return private_detail_te_value.get_context(); }
 
+        float is_supported(instruction_ref ins, support_metric m) const override
+        {
+
+            return private_detail_te_default_is_supported(char(0), private_detail_te_value, ins, m);
+        }
+
         argument copy_to(const argument& input) const override
         {
 

src/include/migraphx/target_assignments.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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.
+ */
+#ifndef MIGRAPHX_GUARD_MIGRAPHX_ASSIGNMENT_HPP
+#define MIGRAPHX_GUARD_MIGRAPHX_ASSIGNMENT_HPP
+
+#include <unordered_map>
+
+#include <migraphx/instruction_ref.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct target_assignments
+{
+    void add_assignment(instruction_ref ins, const std::string& target);
+
+    auto begin() const { return assignments.cbegin(); }
+    auto end() const { return assignments.cend(); }
+
+    private:
+    std::unordered_map<instruction_ref, std::string> assignments;
+};
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_MIGRAPHX_ASSIGNMENT_HPP

src/insert_pad.cpp

@@ -40,6 +40,12 @@ static void update_op(const instruction_ref& input, const instruction_ref& ins,
     auto val        = op.to_value();
     auto op_padding = val.at("padding").to_vector<size_t>();
 
+    // skip if shape is dynamic
+    if(input->get_shape().dynamic())
+    {
+        return;
+    }
+
     auto kdims = input->get_shape().lens().size() - 2;
     if(std::equal(op_padding.begin(),
                   op_padding.begin() + kdims,

src/instruction.cpp

@@ -445,8 +445,8 @@ operation instruction::normalized_operator() const
     operation o = this->get_operator();
     if(this->need_normalization())
     {
-        auto lens = this->inputs().front()->get_shape().lens();
-        if(!normalize_attributes(o, lens))
+        auto s = this->inputs().front()->get_shape();
+        if(!normalize_attributes(o, s.max_lens()))
             return this->get_operator();
     }
     return o;

src/module.cpp

@@ -399,7 +399,8 @@ module::add_instructions(const std::vector<instruction_ref>& instructions,
 }
 
 std::vector<instruction_ref>
-module::add_instructions(module_ref m, std::unordered_map<instruction_ref, instruction_ref> map_ins)
+module::add_instructions(const_module_ref m,
+                         std::unordered_map<instruction_ref, instruction_ref> map_ins)
 {
     return this->insert_instructions(this->end(), m, std::move(map_ins));
 }
@@ -420,8 +421,10 @@ module::insert_instructions(instruction_ref ins,
     return insert_generic_instructions(*this, ins, instructions, std::move(map_ins));
 }
 
-std::vector<instruction_ref> module::insert_instructions(
-    instruction_ref ins, module_ref m, std::unordered_map<instruction_ref, instruction_ref> map_ins)
+std::vector<instruction_ref>
+module::insert_instructions(instruction_ref ins,
+                            const_module_ref m,
+                            std::unordered_map<instruction_ref, instruction_ref> map_ins)
 {
     return insert_generic_instructions(*this, ins, iterator_for(*m), std::move(map_ins));
 }
@@ -436,11 +439,7 @@ module::insert_instructions(instruction_ref ins,
     return insert_generic_instructions(*this, ins, iterator_for(r), std::move(map_ins));
 }
 
-instruction_ref module::add_literal(literal l)
-{
-    impl->emplace_front(std::move(l));
-    return impl->instructions.begin();
-}
+instruction_ref module::add_literal(literal l) { return insert_literal(begin(), std::move(l)); }
 
 instruction_ref module::add_outline(const shape& s)
 {
@@ -450,10 +449,7 @@ instruction_ref module::add_outline(const shape& s)
 
 instruction_ref module::add_parameter(std::string name, shape s)
 {
-    assert(get_parameter_shape(name) == shape{});
-    impl->push_front({builtin::param{std::move(name), impl->nparams}, std::move(s), {}});
-    impl->nparams++;
-    return impl->instructions.begin();
+    return insert_parameter(begin(), std::move(name), std::move(s));
 }
 
 instruction_ref module::add_return(std::vector<instruction_ref> args)
@@ -466,6 +462,20 @@ instruction_ref module::add_return(std::vector<instruction_ref> args)
     return result;
 }
 
+instruction_ref module::insert_literal(instruction_ref ins, literal l)
+{
+    impl->emplace(ins, std::move(l));
+    return std::prev(ins);
+}
+
+instruction_ref module::insert_parameter(instruction_ref ins, std::string name, shape s)
+{
+    assert(get_parameter_shape(name) == shape{});
+    impl->insert(ins, {builtin::param{std::move(name), impl->nparams}, std::move(s), {}});
+    impl->nparams++;
+    return std::prev(ins);
+}
+
 instruction_ref module::replace_return(std::vector<instruction_ref> args)
 {
     auto last = std::prev(this->end());

src/normalize_ops.cpp

@@ -43,9 +43,9 @@ void normalize_ops::apply(module& m) const
         if(inputs.empty())
             continue;
 
-        auto lens                    = inputs[0]->get_shape().lens();
+        auto s                       = inputs[0]->get_shape();
         migraphx::operation tuned_op = ins->get_operator();
-        if(normalize_attributes(tuned_op, lens))
+        if(normalize_attributes(tuned_op, s.max_lens()))
         {
             m.replace_instruction(ins, tuned_op, inputs);
             ins->set_normalized();

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

@@ -93,9 +93,10 @@ struct onnx_parser
         onnx_parser&, const node_info&, std::vector<instruction_ref>)>;
     node_map nodes;
     std::unordered_map<std::string, instruction_ref> instructions;
-    program prog                  = program();
-    std::size_t default_dim_value = 1;
+    program prog                                   = program();
+    shape::dynamic_dimension default_dyn_dim_value = {1, 1, 0};
     std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims;
+    std::unordered_map<std::string, std::vector<shape::dynamic_dimension>> map_dyn_input_dims;
     bool skip_unknown_operators = false;
     int64_t max_loop_iterations = 10;
     int64_t opset_version       = 13;
@@ -118,6 +119,7 @@ struct onnx_parser
 };
 
 shape::type_t get_type(int dtype);
+bool is_type_float(shape::type_t dtype);
 
 } // namespace onnx
 } // namespace MIGRAPHX_INLINE_NS

src/onnx/onnx.cpp

@@ -41,8 +41,25 @@ template <class... Ts>
 program parse_onnx_from(const onnx_options& options, Ts&&... xs)
 {
     onnx::onnx_parser parser;
-    parser.map_input_dims         = options.map_input_dims;
-    parser.default_dim_value      = options.default_dim_value;
+    parser.map_input_dims     = options.map_input_dims;
+    parser.map_dyn_input_dims = options.map_dyn_input_dims;
+    auto dim_val              = options.default_dim_value;
+    if(dim_val != 0)
+    {
+        if(options.default_dyn_dim_value != shape::dynamic_dimension{1, 1, 0})
+        {
+            MIGRAPHX_THROW("PARSE_ONNX_FROM: both default_dim_value and default_dyn_dim_value"
+                           "set to non-default value");
+        }
+        else
+        {
+            parser.default_dyn_dim_value = {dim_val, dim_val, 0};
+        }
+    }
+    else
+    {
+        parser.default_dyn_dim_value = options.default_dyn_dim_value;
+    }
     parser.skip_unknown_operators = options.skip_unknown_operators;
     parser.max_loop_iterations    = options.max_loop_iterations;
 

src/onnx/onnx_parser.cpp

@@ -28,16 +28,17 @@
 #include <migraphx/stringutils.hpp>
 #include <migraphx/ranges.hpp>
 #include <migraphx/instruction.hpp>
-#include <migraphx/pad_calc.hpp>
 #include <migraphx/common.hpp>
 #include <migraphx/type_traits.hpp>
 #include <migraphx/float_equal.hpp>
 #include <migraphx/file_buffer.hpp>
 #include <migraphx/filesystem.hpp>
 #include <migraphx/op/unknown.hpp>
+#include <migraphx/env.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
+
 namespace onnx {
 
 static onnx_parser::attribute_map get_attributes(const onnx::NodeProto& node)
@@ -58,7 +59,7 @@ create_literal(shape::type_t shape_type, const std::vector<size_t>& dims, const
         std::accumulate(dims.begin(), dims.end(), std::size_t(1), std::multiplies<std::size_t>());
     if(elem_num == 0)
     {
-        return {};
+        return literal{shape_type};
     }
 
     // in case of scalar constants in onnx file, use dims=1 to fill initializer data
@@ -75,7 +76,7 @@ static literal create_literal(shape::type_t shape_type, const std::vector<size_t
         std::accumulate(dims.begin(), dims.end(), std::size_t(1), std::multiplies<std::size_t>());
     if(elem_num == 0)
     {
-        return {};
+        return literal{shape_type};
     }
 
     // scalar input
@@ -255,6 +256,11 @@ int64_t onnx_parser::get_opset_version(const onnx::ModelProto& model)
 
 void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
 {
+    if(not map_input_dims.empty() and not map_dyn_input_dims.empty())
+    {
+        MIGRAPHX_THROW("PARSE_GRAPH: both map_input_dims and map_dyn_input_dims non-empty, only"
+                       "one should be used");
+    }
     std::unordered_map<std::string, instruction_ref> mod_insts;
     for(auto&& f : graph.initializer())
     {
@@ -268,7 +274,7 @@ void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
         // input not in initializer_data, so it is a real input
         if(!contains(mod_insts, name))
         {
-            // ONNX specification does not specify hwo to deal with the
+            // ONNX specification does not specify how to deal with the
             // scenario that a nested subgraph contains a parameter with the
             // name existed in its parent graph.
             // In the current implementation, MIGraphX throws an exception for that.
@@ -278,13 +284,22 @@ void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
                                "\" existing in parent graph!");
             }
 
+            shape s;
             std::vector<std::size_t> dims;
             if(map_input_dims.count(name) > 0)
             {
                 dims = map_input_dims.at(name);
+                s    = parse_type(input.type(), dims);
+            }
+            else if(map_dyn_input_dims.count(name) > 0)
+            {
+                shape::type_t shape_type = get_type(input.type().tensor_type().elem_type());
+                s                        = {shape_type, map_dyn_input_dims.at(name)};
+            }
+            else
+            {
+                s = parse_type(input.type(), dims);
             }
-
-            shape s         = parse_type(input.type(), dims);
             mod_insts[name] = mod->add_parameter(name, s);
         }
     }
@@ -439,30 +454,41 @@ shape onnx_parser::parse_type(const onnx::TypeProto& t,
         return {shape_type, input_dims};
     }
 
-    std::vector<std::size_t> dims;
+    std::vector<shape::dynamic_dimension> dynamic_dims;
     auto&& tensor_dims = t.tensor_type().shape().dim();
     std::transform(tensor_dims.begin(),
                    tensor_dims.end(),
-                   std::back_inserter(dims),
-                   [&](auto&& d) -> std::size_t {
+                   std::back_inserter(dynamic_dims),
+                   [&](auto&& d) -> shape::dynamic_dimension {
                        if(d.has_dim_value())
                        {
                            if(static_cast<int>(d.dim_value()) <= 0)
                            {
-                               return default_dim_value;
+                               return default_dyn_dim_value;
                            }
-                           return d.dim_value();
+                           std::size_t tmp = d.dim_value();
+                           return {tmp, tmp, 0};
                        }
                        else
                        {
-                           return default_dim_value;
+                           return default_dyn_dim_value;
                        }
                    });
 
-    if(dims.empty())
+    if(dynamic_dims.empty())
+    {
         return {shape_type};
-
-    return {shape_type, dims};
+    }
+    if(std::all_of(dynamic_dims.begin(), dynamic_dims.end(), [](auto dd) { return dd.is_fixed(); }))
+    {
+        std::vector<std::size_t> dims;
+        std::transform(dynamic_dims.begin(),
+                       dynamic_dims.end(),
+                       std::back_inserter(dims),
+                       [](auto d) { return d.max; });
+        return {shape_type, dims};
+    }
+    return {shape_type, dynamic_dims};
 }
 
 shape::type_t get_type(int dtype)
@@ -487,6 +513,16 @@ shape::type_t get_type(int dtype)
     }
 }
 
+bool is_type_float(shape::type_t dtype)
+{
+    bool r = false;
+    if(dtype == shape::float_type || dtype == shape::double_type || dtype == shape::half_type)
+    {
+        r = true;
+    }
+    return r;
+}
+
 } // namespace onnx
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/onnx/parse_constant.cpp

@@ -43,7 +43,7 @@ struct parse_constant : op_parser<parse_constant>
         // return empty literal
         if(v.get_shape().elements() == 0)
         {
-            return info.add_literal(literal{});
+            return info.add_literal(literal{v.get_shape().type()});
         }
 
         auto dim_size = info.attributes.at("value").t().dims_size();

src/onnx/parse_convolution.cpp

@@ -47,15 +47,17 @@ struct parse_convolution : op_parser<parse_convolution>
                           onnx_parser::node_info info,
                           std::vector<instruction_ref> args) const
     {
-        auto op      = make_op(opd.op_name);
-        auto values  = op.to_value();
-        auto l0      = args[0];
-        auto weights = args[1];
-        auto in_lens = l0->get_shape().lens();
+        auto op       = make_op(opd.op_name);
+        auto values   = op.to_value();
+        auto l0       = args[0];
+        auto weights  = args[1];
+        auto l0_shape = l0->get_shape();
+        auto w_shape  = weights->get_shape();
+        auto in_lens  = l0_shape.max_lens();
         assert(in_lens.size() > 2);
         auto kdims = in_lens.size() - 2;
 
-        // ensure pads availabe only when auto_pad is "NOT_SET"
+        // ensure pads available only when auto_pad is "NOT_SET"
         check_padding_mode(info, "CONV");
 
         if(contains(info.attributes, "strides"))
@@ -79,21 +81,65 @@ struct parse_convolution : op_parser<parse_convolution>
             copy(info.attributes["pads"].ints(), std::back_inserter(padding));
             check_attr_sizes(kdims, padding.size() / 2, "PARSE_CONV: inconsistent paddings");
         }
-
         if(contains(info.attributes, "auto_pad"))
         {
-            auto weight_lens = weights->get_shape().lens();
-            std::vector<std::size_t> k_lens(weight_lens.begin() + 2, weight_lens.end());
-            cal_auto_padding_size(info,
-                                  values,
-                                  k_lens,
-                                  values["dilation"].to_vector<std::size_t>(),
-                                  in_lens,
-                                  padding);
-            auto auto_pad = info.attributes["auto_pad"].s();
+            bool is_same_padding = false;
+            auto auto_pad        = info.attributes["auto_pad"].s();
             if(auto_pad.find("SAME") != std::string::npos)
             {
-                values["padding_mode"] = to_value(op::padding_mode_t::same);
+                is_same_padding = true;
+            }
+
+            // check if image shape is dynamic
+            bool image_shape_dynamic = false;
+            if(l0_shape.dynamic())
+            {
+                auto dyn_dims = l0_shape.dyn_dims();
+                std::for_each(dyn_dims.begin() + 2, dyn_dims.end(), [&](auto dyn_dim) {
+                    if(not dyn_dim.is_fixed())
+                    {
+                        image_shape_dynamic = true;
+                    }
+                });
+            }
+
+            // check if kernel shape is dynamic
+            bool kernel_shape_dynamic = false;
+            if(w_shape.dynamic())
+            {
+                auto dyn_dims = w_shape.dyn_dims();
+                std::for_each(dyn_dims.begin() + 2, dyn_dims.end(), [&](auto dyn_dim) {
+                    if(not dyn_dim.is_fixed())
+                    {
+                        kernel_shape_dynamic = true;
+                    }
+                });
+            }
+
+            if(is_same_padding)
+            {
+                if(image_shape_dynamic or kernel_shape_dynamic)
+                {
+                    // must calculate "same" padding with input shape data
+                    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);
+                    values["use_dynamic_same_auto_pad"] = true;
+                }
+                else
+                {
+                    values["padding_mode"] = to_value(op::padding_mode_t::same);
+                    // kernel shape will be fixed, so max_lens() == min_len() for kernel lengths
+                    auto weight_lens = weights->get_shape().max_lens();
+                    std::vector<std::size_t> k_lens(weight_lens.begin() + 2, weight_lens.end());
+                    cal_auto_padding_size(info,
+                                          values,
+                                          k_lens,
+                                          values["dilation"].to_vector<std::size_t>(),
+                                          in_lens,
+                                          padding);
+                }
             }
         }
         values["padding"] = std::vector<size_t>(padding.begin(), padding.end());

src/onnx/parse_if.cpp

@@ -47,7 +47,8 @@ struct parse_if : op_parser<parse_if>
 
         if(args.front()->get_shape().elements() != 1)
         {
-            MIGRAPHX_THROW("PARSE_IF: condition input can have only one element!");
+            MIGRAPHX_THROW("PARSE_IF: " + info.name +
+                           " condition input can have only one element!");
         }
 
         std::string then_name = info.name + "_if";
@@ -69,7 +70,8 @@ struct parse_if : op_parser<parse_if>
                           else_out_shapes.begin(),
                           else_out_shapes.end()))
         {
-            MIGRAPHX_THROW("PARSE_IF: then and else sub_grahps must have same output shapes!");
+            MIGRAPHX_THROW("PARSE_IF: " + info.name +
+                           " then and else sub_grahps must have same output shapes!");
         }
 
         auto if_ret = info.add_instruction(make_op("if"), args, {then_mdl, else_mdl});

src/onnx/parse_instancenorm.cpp

@@ -32,9 +32,12 @@ namespace onnx {
 
 struct parse_instancenorm : op_parser<parse_instancenorm>
 {
+    const std::set<shape::type_t> valid_types = {
+        shape::float_type, shape::half_type, shape::double_type};
+
     std::vector<op_desc> operators() const { return {{"InstanceNormalization"}}; }
 
-    instruction_ref parse(const op_desc& /*opd*/,
+    instruction_ref parse(const op_desc& opd,
                           const onnx_parser& parser,
                           onnx_parser::node_info info,
                           std::vector<instruction_ref> args) const
@@ -52,6 +55,11 @@ struct parse_instancenorm : op_parser<parse_instancenorm>
         auto scale = args[1];
         auto bias  = args[2];
         auto dims  = x->get_shape().lens();
+        auto dtype = x->get_shape().type();
+        if(not contains(valid_types, dtype))
+            MIGRAPHX_THROW(opd.op_name + ": invalid output type: " + std::to_string(dtype) +
+                           ". Valid types are 1 (float), 10 (half), and 11 (double).");
+
         auto ndims = dims.size();
         assert(ndims >= 2);
         auto kdims = ndims - 2;
@@ -65,7 +73,7 @@ struct parse_instancenorm : op_parser<parse_instancenorm>
         auto l0              = info.add_instruction(make_op("sqdiff"), x, mean_bcast);
         auto variance        = info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), l0);
         auto l1              = info.add_instruction(make_op("sub"), x, mean_bcast);
-        auto epsilon_literal = info.add_literal(epsilon);
+        auto epsilon_literal = info.add_literal(literal{shape{dtype}, {epsilon}});
         auto epsilon_bcast =
             info.add_instruction(make_op("multibroadcast", {{"out_lens", dims}}), epsilon_literal);
         auto variance_bcast =

src/onnx/parse_mod.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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/instruction.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_mod : op_parser<parse_mod>
+{
+    std::vector<op_desc> operators() const { return {{"Mod"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& parser,
+                          onnx_parser::node_info info,
+                          std::vector<instruction_ref> args) const
+    {
+        std::string mod = "mod";
+        if(is_type_float(args[0]->get_shape().type()) || is_type_float(args[1]->get_shape().type()))
+        {
+            if(!contains(info.attributes, "fmod"))
+            {
+                MIGRAPHX_THROW("Mod operator with float args and fmod=0 invalid");
+            }
+        }
+
+        if(contains(info.attributes, "fmod"))
+        {
+            if(parser.parse_value(info.attributes.at("fmod")).at<int>() == 1)
+            {
+                mod = "fmod";
+            }
+        }
+        return info.add_common_op(mod, args[0], args[1]);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/pad_calc.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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/pad_calc.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+void calculate_padding(int64_t idx,
+                       std::vector<int64_t>& pads,
+                       int64_t input_dim,
+                       int64_t stride,
+                       int64_t dilation,
+                       int64_t weight_dim,
+                       bool is_same_upper)
+{
+    int64_t output_dim     = (input_dim + stride - 1) / stride; // round up result
+    int64_t new_weight_dim = weight_dim + (weight_dim - 1) * (dilation - 1);
+    int64_t pad =
+        std::max(static_cast<int64_t>(0), (output_dim - 1) * stride + new_weight_dim - input_dim);
+    auto pad_ndims = pads.size() / 2;
+
+    if(is_same_upper)
+    {
+        pads[idx]             = pad / 2;
+        pads[idx + pad_ndims] = pad - pad / 2;
+    }
+    else
+    {
+        pads[idx + pad_ndims] = pad / 2;
+        pads[idx]             = pad - pad / 2;
+    }
+}
+
+std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
+                                           std::vector<std::size_t> k_lens,
+                                           std::vector<std::size_t> strides,
+                                           std::vector<std::size_t> dilations,
+                                           bool use_upper)
+{
+    std::vector<std::size_t> padding;
+    padding.resize(2 * k_lens.size());
+    for(size_t i = 0; i < padding.size() / 2; i++)
+    {
+        std::ptrdiff_t input_dim      = tensor_lens[i];
+        std::ptrdiff_t stride         = strides[i];
+        std::ptrdiff_t weight_dim     = k_lens[i];
+        std::ptrdiff_t dilation       = dilations[i];
+        std::ptrdiff_t output_dim     = (input_dim + stride - 1) / stride; // round up result
+        std::ptrdiff_t new_weight_dim = weight_dim + (weight_dim - 1) * (dilation - 1);
+        std::size_t pad               = std::max(static_cast<std::ptrdiff_t>(0),
+                                   (output_dim - 1) * stride + new_weight_dim - input_dim);
+        auto pad_ndims                = padding.size() / 2;
+
+        if(use_upper)
+        {
+            padding[i]             = pad / 2;
+            padding[i + pad_ndims] = pad - pad / 2;
+        }
+        else
+        {
+            padding[i + pad_ndims] = pad / 2;
+            padding[i]             = pad - pad / 2;
+        }
+    }
+    return padding;
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/program.cpp

@@ -159,6 +159,25 @@ instruction_ref program::validate() const
     return mm->validate();
 }
 
+target_assignments program::get_target_assignments(const std::vector<target>& targets,
+                                                   assignment_options options)
+{
+    const auto m = options.metric;
+
+    target_assignments p;
+
+    const auto* mod = get_main_module();
+    for(auto it : iterator_for(*mod))
+    {
+        auto t = std::max_element(
+            targets.begin(), targets.end(), [it, m](const target& lhs, const target& rhs) {
+                return lhs.is_supported(it, m) < rhs.is_supported(it, m);
+            });
+        p.add_assignment(it, t->name());
+    }
+    return p;
+}
+
 bool program::is_compiled() const { return not this->impl->target_name.empty(); }
 
 void program::compile(const target& t, compile_options options)
@@ -288,9 +307,12 @@ std::vector<argument> generic_eval(const module* mod,
                     if(not contains(params, param_name))
                         MIGRAPHX_THROW("Parameter not found: " + param_name);
                     auto param = params[param_name];
-                    if(param.get_shape() != ins->get_shape())
+                    // TODO: may want to check correct number of dimensions and/or was within bounds
+                    if(not ins->get_shape().dynamic() and param.get_shape() != ins->get_shape())
+                    {
                         MIGRAPHX_THROW("Incorrect shape {" + to_string(param.get_shape()) +
                                        "} for parameter: " + param_name);
+                    }
                     return param;
                 }));
         }
@@ -333,7 +355,10 @@ std::vector<argument> generic_eval(const module* mod,
                             }));
         }
         assert(results.find(ins) != results.end());
-        assert(results.at(ins).get_shape() == ins->get_shape());
+        if(not ins->get_shape().dynamic())
+        {
+            assert(results.at(ins).get_shape() == ins->get_shape());
+        }
     }
     return {results.at(std::prev(mod->end()))};
 }
@@ -504,12 +529,14 @@ static void mod_from_val(module_ref mod,
 
         if(name == "@param")
         {
-            output = mod->add_parameter(fields["parameter"].to<std::string>(),
-                                        migraphx::from_value<shape>(node.at("shape")));
+            output = mod->insert_parameter(mod->end(),
+                                           fields["parameter"].to<std::string>(),
+                                           migraphx::from_value<shape>(node.at("shape")));
         }
         else if(name == "@literal")
         {
-            output = mod->add_literal(migraphx::from_value<literal>(node.at("literal")));
+            output =
+                mod->insert_literal(mod->end(), migraphx::from_value<literal>(node.at("literal")));
         }
         else
         {
@@ -544,11 +571,11 @@ static void mod_from_val(module_ref mod,
             }
             else if(module_inputs.empty())
             {
-                output = mod->add_instruction(op, inputs);
+                output = mod->insert_instruction(mod->end(), op, inputs);
             }
             else
             {
-                output = mod->add_instruction(op, inputs, module_inputs);
+                output = mod->insert_instruction(mod->end(), op, inputs, module_inputs);
             }
         }
         output->set_normalized(normalized);
@@ -681,11 +708,13 @@ void program::perf_report(std::ostream& os,
     double overhead_percent       = overhead_time * 100.0 / total_time;
     double total_instruction_time = 0.0;
     std::unordered_map<std::string, double> op_times;
+    std::unordered_map<std::string, std::size_t> op_n;
     for(auto&& p : ins_vec)
     {
         double avg = common_average(p.second);
         op_times[perf_group(p.first->get_operator())] += avg;
         total_instruction_time += avg;
+        op_n[perf_group(p.first->get_operator())]++;
     }
     double calculate_overhead_time    = total_time - total_instruction_time;
     double calculate_overhead_percent = calculate_overhead_time * 100.0 / total_time;
@@ -706,18 +735,19 @@ void program::perf_report(std::ostream& os,
 
     os << std::endl;
     os << "Summary:" << std::endl;
-    std::vector<std::pair<double, std::string>> op_times_sorted;
-    std::transform(op_times.begin(),
-                   op_times.end(),
-                   std::back_inserter(op_times_sorted),
-                   [](auto p) { return std::make_pair(p.second, p.first); });
+    std::vector<std::tuple<double, std::size_t, std::string>> op_times_sorted;
+    std::transform(
+        op_times.begin(), op_times.end(), std::back_inserter(op_times_sorted), [&](auto p) {
+            auto&& name = p.first;
+            return std::make_tuple(p.second, op_n.at(name), name);
+        });
     std::sort(op_times_sorted.begin(), op_times_sorted.end(), std::greater<>{});
-    for(auto&& p : op_times_sorted)
+    for(auto&& [avg, nn, name] : op_times_sorted)
     {
-        auto&& name    = p.second;
-        double avg     = p.first;
         double percent = std::ceil(100.0 * avg / total_instruction_time);
-        os << name << ": " << avg << "ms, " << percent << "%" << std::endl;
+        double per_ins = avg / nn;
+        os << name << ": " << avg << "ms / " << nn << " = " << per_ins << "ms, " << percent << "%"
+           << std::endl;
     }
 
     os << std::endl;

src/serialize.cpp

@@ -36,7 +36,7 @@ void raw_data_to_value(value& v, const RawData& rd)
     result["shape"] = migraphx::to_value(rd.get_shape());
     if(rd.get_shape().type() == shape::tuple_type)
         result["sub"] = migraphx::to_value(rd.get_sub_objects());
-    else
+    else if(not rd.empty())
         result["data"] = migraphx::value::binary(rd.data(), rd.get_shape().bytes());
     v = result;
 }
@@ -56,7 +56,7 @@ void migraphx_from_value(const value& v, argument& a)
         literal l = migraphx::from_value<literal>(v);
         a         = l.get_argument();
     }
-    else
+    else if(v.contains("sub"))
     {
         a = migraphx::from_value<std::vector<argument>>(v.at("sub"));
     }