Merge branch 'develop' into fix_parse_if

src/include/migraphx/op/softmax.hpp

@@ -53,15 +53,15 @@ struct softmax
     std::string name() const { return "softmax"; }
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        if(inputs.at(0).packed())
+        check_shapes{inputs, *this, true}.has(1);
+        auto s0 = inputs[0];
+        if(s0.dynamic() or s0.packed())
         {
-            return inputs.at(0);
+            return s0;
         }
         else
         {
-            auto lens = inputs.at(0).lens();
-            return {inputs.at(0).type(), lens};
+            return {s0.type(), s0.lens()};
         }
     }
 

src/include/migraphx/op/squeeze.hpp

@@ -29,6 +29,7 @@
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -54,52 +55,85 @@ struct squeeze
     std::string name() const { return "squeeze"; }
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
+        check_shapes{inputs, *this, true}.has(1);
         auto input_shape = inputs[0];
-        auto type        = input_shape.type();
-        auto old_lens    = input_shape.lens();
-        auto old_strides = input_shape.strides();
-        if(std::any_of(axes.begin(), axes.end(), [&](auto axis) { return old_lens[axis] != 1; }))
+        if(input_shape.dynamic())
         {
-            MIGRAPHX_THROW("squeeze axis dimension should be equal to 1");
-        }
-        std::vector<std::size_t> new_lens;
-        std::vector<std::size_t> new_strides;
-        if(axes.empty())
-        {
-            for(auto i : range(old_lens.size()))
+            if(std::any_of(axes.begin(), axes.end(), [&](auto axis) {
+                   return input_shape.dyn_dims()[axis] != 1;
+               }))
+            {
+                MIGRAPHX_THROW(
+                    "SQUEEZE: dynamic axis dimension should be equal to {1, 1, 0} or {1, 1, 1}");
+            }
+            std::vector<shape::dynamic_dimension> dyn_dims = {};
+            if(axes.empty())
+            {
+                std::copy_if(input_shape.dyn_dims().cbegin(),
+                             input_shape.dyn_dims().cend(),
+                             std::back_inserter(dyn_dims),
+                             [&](auto dd) { return dd != 1; });
+            }
+            else
             {
-                if(old_lens[i] != 1)
+                for(auto i : range(input_shape.ndim()))
                 {
-                    new_lens.push_back(old_lens[i]);
-                    new_strides.push_back(old_strides[i]);
+                    if(std::find(axes.begin(), axes.end(), i) == axes.end())
+                    {
+                        dyn_dims.push_back(input_shape.dyn_dims()[i]);
+                    }
                 }
             }
+            return {input_shape.type(), dyn_dims};
         }
         else
         {
-            for(auto i : range(old_lens.size()))
+            auto type        = input_shape.type();
+            auto old_lens    = input_shape.lens();
+            auto old_strides = input_shape.strides();
+            if(std::any_of(
+                   axes.begin(), axes.end(), [&](auto axis) { return old_lens[axis] != 1; }))
             {
-                if(std::find(axes.begin(), axes.end(), i) == axes.end())
+                MIGRAPHX_THROW("SQUEEZE: static axis dimension should be equal to 1");
+            }
+            std::vector<std::size_t> new_lens;
+            std::vector<std::size_t> new_strides;
+            if(axes.empty())
+            {
+                for(auto i : range(old_lens.size()))
                 {
-                    new_lens.push_back(old_lens[i]);
-                    new_strides.push_back(old_strides[i]);
+                    if(old_lens[i] != 1)
+                    {
+                        new_lens.push_back(old_lens[i]);
+                        new_strides.push_back(old_strides[i]);
+                    }
                 }
             }
-        }
-        if(new_lens.empty())
-        {
-            return shape{type};
-        }
-        else
-        {
-            return shape{type, new_lens, new_strides};
+            else
+            {
+                for(auto i : range(old_lens.size()))
+                {
+                    if(std::find(axes.begin(), axes.end(), i) == axes.end())
+                    {
+                        new_lens.push_back(old_lens[i]);
+                        new_strides.push_back(old_strides[i]);
+                    }
+                }
+            }
+            if(new_lens.empty())
+            {
+                return shape{type};
+            }
+            else
+            {
+                return shape{type, new_lens, new_strides};
+            }
         }
     }
 
-    argument compute(shape output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        return args[0].reshape(output_shape);
+        return args[0].reshape(dyn_out.computed_shape);
     }
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };

src/include/migraphx/op/transpose.hpp

@@ -29,6 +29,7 @@
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -45,17 +46,15 @@ struct transpose
     }
 
     std::string name() const { return "transpose"; }
+
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        auto input         = inputs.at(0);
-        auto input_lens    = input.lens();
-        auto input_strides = input.strides();
-        auto t             = input.type();
+        check_shapes{inputs, *this, true}.has(1);
+        auto input = inputs.at(0);
 
-        if(dims.size() != input_lens.size())
+        if(dims.size() != input.ndim())
         {
-            MIGRAPHX_THROW("Permutation has wrong number of axes");
+            MIGRAPHX_THROW("TRANSPOSE: Permutation has wrong number of axes");
         }
         std::vector<int64_t> axes(dims.size());
         std::iota(axes.begin(), axes.end(), 0);
@@ -63,19 +62,36 @@ struct transpose
         {
             MIGRAPHX_THROW("TRANSPOSE: Invalid permutation");
         }
-        std::vector<size_t> output_lens(input_lens.size());
-        std::vector<size_t> output_strides(input_lens.size());
-        for(std::size_t i = 0; i < output_lens.size(); i++)
+
+        if(input.dynamic())
         {
-            output_lens[i]    = input_lens[dims[i]];
-            output_strides[i] = input_strides[dims[i]];
+            std::vector<shape::dynamic_dimension> output_dyn_dims(input.ndim());
+            std::transform(dims.cbegin(), dims.cend(), output_dyn_dims.begin(), [&](auto dim) {
+                return input.dyn_dims()[dim];
+            });
+            return {input.type(), output_dyn_dims};
+        }
+        else
+        {
+            auto input_lens    = input.lens();
+            auto input_strides = input.strides();
+
+            std::vector<size_t> output_lens(input.ndim());
+            std::vector<size_t> output_strides(input.ndim());
+            for(std::size_t i = 0; i < input.ndim(); i++)
+            {
+                output_lens[i]    = input_lens[dims[i]];
+                output_strides[i] = input_strides[dims[i]];
+            }
+            return {input.type(), output_lens, output_strides};
         }
-        return {t, output_lens, output_strides};
     }
-    argument compute(shape output_shape, std::vector<argument> args) const
+
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        return args[0].reshape(output_shape);
+        return args[0].reshape(dyn_out.computed_shape);
     }
+
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };
 

src/include/migraphx/op/unsqueeze.hpp

@@ -29,11 +29,20 @@
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+/**
+ * Adds dimensions to a tensor based on the axes attribute.
+ * `axes` are based on the number of output shape dimensions and should not contain duplicates.
+ * `steps` are for modifying dimensions added to the middle of the original shape.
+ * Each step must be a factor of the original dimension.
+ * ex: unsqueeze(shape = [3, 4, 10], axes = [2, 4, 5], steps = [2]) -> shape = [3, 4, 2, 5, 1, 1]
+ * Dynamic shape version does not handle `steps`.
+ */
 struct unsqueeze
 {
     std::vector<int64_t> axes;
@@ -56,63 +65,89 @@ struct unsqueeze
     std::string name() const { return "unsqueeze"; }
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
+        check_shapes{inputs, *this, true}.has(1);
         auto input_shape = inputs[0];
-        auto type        = input_shape.type();
-        auto old_lens    = input_shape.lens();
-        auto old_strides = input_shape.strides();
-        if(input_shape.scalar())
+
+        if(input_shape.dynamic())
         {
-            if(old_lens.size() == 1 and old_lens.front() == 1)
-                return shape{type, old_lens};
-            else
-                MIGRAPHX_THROW("UNSQUEEZE: Input must be a scalar");
+            if(not steps.empty())
+            {
+                MIGRAPHX_THROW("UNSQUEEZE_dyn: nonempty steps attribute");
+            }
+            std::vector<shape::dynamic_dimension> dyn_dims = {};
+            auto new_ndim                                  = input_shape.ndim() + axes.size();
+            std::size_t k                                  = 0;
+            for(auto i : range(new_ndim))
+            {
+                if(std::find(axes.begin(), axes.end(), i) != axes.end())
+                {
+                    dyn_dims.push_back({1, 1, 0});
+                }
+                else
+                {
+                    dyn_dims.push_back(input_shape.dyn_dims().at(k++));
+                }
+            }
+            return {input_shape.type(), dyn_dims};
         }
+        else
+        {
+            auto type        = input_shape.type();
+            auto old_lens    = input_shape.lens();
+            auto old_strides = input_shape.strides();
+            if(input_shape.scalar())
+            {
+                if(old_lens.size() == 1 and old_lens.front() == 1)
+                    return shape{type, old_lens};
+                else
+                    MIGRAPHX_THROW("UNSQUEEZE: Input must be a scalar");
+            }
 
-        if(steps.size() > axes.size())
-            MIGRAPHX_THROW("UNSQUEEZE: Steps provided with no axis");
+            if(steps.size() > axes.size())
+                MIGRAPHX_THROW("UNSQUEEZE: Steps provided with no axis");
 
-        std::size_t new_size = old_lens.size() + axes.size();
+            std::size_t new_size = old_lens.size() + axes.size();
 
-        std::vector<std::size_t> new_lens(new_size);
-        std::vector<std::size_t> new_strides(new_size);
-        std::size_t p = 0;
-        for(auto i : range(new_size))
-        {
-            auto axis_idx = std::find(axes.begin(), axes.end(), i) - axes.begin();
-            if(axis_idx < axes.size())
+            std::vector<std::size_t> new_lens(new_size);
+            std::vector<std::size_t> new_strides(new_size);
+            std::size_t p = 0;
+            for(auto i : range(new_size))
             {
-                std::int64_t step = 1;
-                if(axis_idx < steps.size())
-                    step = steps[axis_idx];
-                if(step == 0)
-                    MIGRAPHX_THROW("UNSQUEEZE: step must be non-zero");
-                new_lens[i] = step;
-                if(p < old_strides.size())
+                auto axis_idx = std::find(axes.begin(), axes.end(), i) - axes.begin();
+                if(axis_idx < axes.size())
                 {
-                    if((old_lens[p] % step) != 0)
-                        MIGRAPHX_THROW("UNSQUEEZE: Axis dimenstion is not divisible by step");
-                    old_lens[p] /= step;
-                    new_strides[i] = old_strides[p] * old_lens[p];
+                    std::int64_t step = 1;
+                    if(axis_idx < steps.size())
+                        step = steps[axis_idx];
+                    if(step == 0)
+                        MIGRAPHX_THROW("UNSQUEEZE: step must be non-zero");
+                    new_lens[i] = step;
+                    if(p < old_strides.size())
+                    {
+                        if((old_lens[p] % step) != 0)
+                            MIGRAPHX_THROW("UNSQUEEZE: Axis dimenstion is not divisible by step");
+                        old_lens[p] /= step;
+                        new_strides[i] = old_strides[p] * old_lens[p];
+                    }
+                    else
+                    {
+                        if(step != 1)
+                            MIGRAPHX_THROW("UNSQUEEZE: Step must be 1 for extra axes");
+                        new_strides[i] = 1;
+                    }
                 }
                 else
                 {
-                    if(step != 1)
-                        MIGRAPHX_THROW("UNSQUEEZE: Step must be 1 for extra axes");
-                    new_strides[i] = 1;
+                    new_lens[i]    = old_lens[p];
+                    new_strides[i] = old_strides[p++];
                 }
             }
-            else
-            {
-                new_lens[i]    = old_lens[p];
-                new_strides[i] = old_strides[p++];
-            }
+            return shape{type, new_lens, new_strides};
         }
-        return shape{type, new_lens, new_strides};
     }
-    argument compute(shape output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        return args[0].reshape(output_shape);
+        return args[0].reshape(dyn_out.computed_shape);
     }
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };

src/include/migraphx/program.hpp

@@ -115,6 +115,7 @@ struct program
                    print_func) const;
 
     void print_graph(std::ostream& os, bool brief = false) const;
+    void print_py(std::ostream& os) const;
     void print_cpp(std::ostream& os) const;
 
     void dry_run(parameter_map params) const;

src/include/migraphx/shape.hpp

@@ -101,6 +101,12 @@ struct shape
         friend bool operator==(const dynamic_dimension& x, const dynamic_dimension& y);
         friend bool operator!=(const dynamic_dimension& x, const dynamic_dimension& y);
         friend std::ostream& operator<<(std::ostream& os, const dynamic_dimension& x);
+
+        // compare to fixed std::size_t dimension
+        friend bool operator==(const dynamic_dimension& x, const std::size_t& y);
+        friend bool operator==(const std::size_t& x, const dynamic_dimension& y);
+        friend bool operator!=(const dynamic_dimension& x, const std::size_t& y);
+        friend bool operator!=(const std::size_t& x, const dynamic_dimension& y);
     };
 
     static const std::vector<type_t>& types();

src/include/migraphx/shape_for_each.hpp

@@ -31,6 +31,9 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
+/**
+ * Iterates the given function over the indices from the shape in order.
+ */
 template <class F>
 void shape_for_each(const migraphx::shape& s, F f)
 {
@@ -51,7 +54,6 @@ void shape_for_each(const migraphx::shape& s, F f)
         call(indices);
     }
 }
-
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/insert_pad.cpp

@@ -77,14 +77,14 @@ static void update_pooling(const instruction_ref& input, const instruction_ref&
     {
         return;
     }
-    auto kdims = input->get_shape().lens().size() - 2;
+    auto kdims = input->get_shape().ndim() - 2;
     if(std::equal(op.padding.begin(),
                   op.padding.begin() + kdims,
                   op.padding.begin() + kdims,
                   op.padding.end()))
         return;
 
-    std::vector<int64_t> padding(input->get_shape().lens().size() * 2, 0);
+    std::vector<int64_t> padding(input->get_shape().ndim() * 2, 0);
     std::vector<size_t> pads_l(op.padding.begin(), op.padding.begin() + kdims);
     std::vector<size_t> pads_r(op.padding.begin() + kdims, op.padding.end());
     op.padding = std::vector<size_t>(kdims * 2, 0);

src/instruction.cpp

@@ -302,6 +302,24 @@ void instruction::replace_mod_argument(module_ref old, module_ref new_mod)
     std::replace(module_args.begin(), module_args.end(), old, new_mod);
 }
 
+bool instruction::is_undefined() const
+{
+    if(op.name() == "undefined")
+    {
+        return true;
+    }
+    else if(this->inputs().empty())
+    {
+        return false;
+    }
+    else
+    {
+        return std::all_of(this->inputs().begin(), this->inputs().end(), [](auto arg) {
+            return arg->is_undefined();
+        });
+    }
+}
+
 bool instruction::can_eval() const
 {
     if(op.name() == "@literal")

src/module.cpp

@@ -789,6 +789,22 @@ static std::string cpp_var_name(const std::string& name)
     return to_c_id("x_" + replace_string(name, ":", "_module_"));
 }
 
+static void print_py_op(std::ostream& os, const operation& op)
+{
+    auto v = op.to_value();
+    os << "migraphx.op(" << enclose_name(op.name());
+
+    auto default_values = make_op(op.name()).to_value();
+    for(auto&& x : v)
+    {
+        auto name = x.get_key();
+        if(default_values[name] == x)
+            continue;
+        os << ", " << name << "=" << to_json_string(x.without_key());
+    }
+    os << ")";
+}
+
 static void print_make_op(std::ostream& os, const operation& op)
 {
     auto v = op.to_value();
@@ -804,6 +820,14 @@ static void print_make_op(std::ostream& os, const operation& op)
     os << ")";
 }
 
+static void print_py_shape(std::ostream& os, const migraphx::shape& s)
+{
+    os << "migraphx.shape(" << s.type_string() << ", lens=" << to_json_string(s.lens());
+    if(not s.standard())
+        os << ", strides=" << to_json_string(s.strides());
+    os << ")";
+}
+
 static void print_cpp_shape(std::ostream& os, const migraphx::shape& s)
 {
     os << "migraphx::shape{migraphx::shape::" << s.type_string();
@@ -813,6 +837,68 @@ static void print_cpp_shape(std::ostream& os, const migraphx::shape& s)
     os << "}";
 }
 
+std::unordered_map<instruction_ref, std::string>
+module::print_py(std::ostream& os,
+                 const std::string& mname,
+                 std::unordered_map<instruction_ref, std::string> names) const
+{
+    // cppcheck-suppress variableScope
+    unsigned long seed = names.size();
+    auto last          = std::prev(this->end());
+    names              = this->print(
+        [&](auto ins, auto ins_names) {
+            std::vector<std::string> input_vars;
+            std::transform(ins->inputs().begin(),
+                           ins->inputs().end(),
+                           std::back_inserter(input_vars),
+                           [&](auto input) { return cpp_var_name(ins_names.at(input)); });
+            if(ins != last)
+                os << cpp_var_name(ins_names.at(ins)) << " = ";
+            if(ins->name() == "@literal")
+            {
+                os << mname << ".add_literal(";
+                bool use_abs = false;
+                ins->get_literal().visit([&](auto v) {
+                    use_abs = std::none_of(v.begin(), v.end(), [](auto x) { return x < 0; });
+                });
+                // Disable abs for now
+                use_abs = false;
+                if(use_abs)
+                    os << "migraphx.abs_literal(";
+                os << "migraphx.generate_literal(";
+                print_py_shape(os, ins->get_shape());
+                os << ", " << seed << ")";
+                if(use_abs)
+                    os << ")";
+                os << ")" << std::endl;
+                seed++;
+            }
+            else if(ins->name() == "@param")
+            {
+                std::string name = any_cast<builtin::param>(ins->get_operator()).parameter;
+                os << mname << ".add_parameter(" << enclose_name(name) << ",";
+                print_py_shape(os, ins->get_shape());
+                os << ")" << std::endl;
+            }
+            else if(ins->name() == "@return")
+            {
+                os << mname << ".add_return([" << join_strings(input_vars, ", ") << "])"
+                   << std::endl;
+            }
+            else
+            {
+                assert(ins->name().front() != '@');
+                os << mname << ".add_instruction(";
+                print_py_op(os, ins->get_operator());
+                os << ", [" << join_strings(input_vars, ", ") << "]";
+                os << ")" << std::endl;
+            }
+        },
+        names);
+
+    return names;
+}
+
 std::unordered_map<instruction_ref, std::string>
 module::print_cpp(std::ostream& os,
                   const std::string& mname,
@@ -874,6 +960,8 @@ module::print_cpp(std::ostream& os,
     return names;
 }
 
+void module::print_py(std::ostream& os) const { this->print_py(os, this->name(), {}); }
+
 void module::print_cpp(std::ostream& os) const { this->print_cpp(os, this->name(), {}); }
 
 void module::annotate(std::ostream& os, std::function<void(instruction_ref)> a) const

src/onnx/onnx_parser.cpp

@@ -393,18 +393,31 @@ literal onnx_parser::parse_value(const onnx::AttributeProto& attr) const
 literal onnx_parser::parse_tensor(const onnx::TensorProto& t) const
 {
     std::vector<std::size_t> dims(t.dims().begin(), t.dims().end());
-    if(not t.external_data().empty())
+    auto type = get_type(t.data_type());
+    shape tensor_shape(type, dims);
+    auto external_data = t.external_data();
+    if(not external_data.empty())
     {
-        const std::string& data_file = t.external_data().at(0).value();
-        auto raw_buffer              = read_buffer(path + "/" + data_file);
+        const std::string& data_file = external_data.at(0).value();
+        size_t num_data_fields       = external_data.size();
+        size_t offset                = 0;
+        size_t nbytes                = tensor_shape.bytes();
+
+        if(num_data_fields > 1) // if offset field is present
+        {
+            offset = std::stoul(t.external_data().at(1).value());
+        }
+        if(num_data_fields > 2) // if nbytes field is present
+        {
+            nbytes = std::stoul(t.external_data().at(2).value());
+        }
+        auto raw_buffer = read_buffer(path + "/" + data_file, offset, nbytes);
         std::string s(raw_buffer.begin(), raw_buffer.end());
-        auto type = get_type(t.data_type());
         return create_literal(type, dims, s.data());
     }
     if(t.has_raw_data())
     {
         const std::string& s = t.raw_data();
-        auto type            = get_type(t.data_type());
         return create_literal(type, dims, s.data());
     }
 

src/onnx/parse_pooling.cpp

@@ -47,52 +47,42 @@ struct parse_pooling : op_parser<parse_pooling>
                 {"GlobalLpPool", "lpnorm"}};
     }
 
-    instruction_ref parse(const op_desc& opd,
-                          const onnx_parser& /*parser*/,
-                          onnx_parser::node_info info,
-                          std::vector<instruction_ref> args) const
+    value handle_values(const op_desc& opd,
+                        onnx_parser::node_info info,
+                        const shape& in_shape,
+                        value values) 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;
-        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();
-        assert(in_lens.size() > 2);
-        auto kdims = in_lens.size() - 2;
-
+        auto kdims = in_shape.ndim() - 2;
         if(starts_with(opd.onnx_name, "Global"))
         {
-            values["lengths"] = std::vector<size_t>(in_lens.begin() + 2, in_lens.end());
+            // if spatial dimensions are dynamic use dyn_global flag
+            if(in_shape.dynamic() and std::any_of(in_shape.dyn_dims().cbegin() + 2,
+                                                  in_shape.dyn_dims().cend(),
+                                                  [](auto dd) { return not dd.is_fixed(); }))
+            {
+                values["dyn_global"] = true;
+                values["lengths"]    = std::vector<size_t>();
+            }
+            else
+            {
+                // works with static and fixed dynamic shape
+                auto m_lens       = in_shape.max_lens();
+                values["lengths"] = std::vector<size_t>(m_lens.begin() + 2, m_lens.end());
+            }
         }
 
-        // does not support ceil_mode
         if(contains(info.attributes, "ceil_mode"))
         {
             values["ceil_mode"] = static_cast<bool>(info.attributes.at("ceil_mode").i());
         }
 
-        // count include padding, if count include pad is 1, we always use
-        // explicit pad
-        int count_include_pad = 0;
-        if(contains(info.attributes, "count_include_pad"))
-        {
-            count_include_pad = info.attributes.at("count_include_pad").i();
-        }
-
         if(contains(info.attributes, "strides"))
         {
             values["stride"].clear();
             copy(info.attributes["strides"].ints(), std::back_inserter(values["stride"]));
             check_attr_sizes(kdims, values["stride"].size(), "PARSE_POOLING: inconsistent strides");
         }
+
         if(contains(info.attributes, "kernel_shape"))
         {
             values["lengths"].clear();
@@ -110,6 +100,46 @@ struct parse_pooling : op_parser<parse_pooling>
         // ensure pads availabe only when auto_pad is "NOT_SET"
         check_padding_mode(info, "POOLING");
 
+        return values;
+    }
+
+    instruction_ref parse(const op_desc& opd,
+                          const onnx_parser& /*parser*/,
+                          onnx_parser::node_info info,
+                          std::vector<instruction_ref> args) const
+    {
+        std::string mode                                                 = opd.op_name;
+        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}};
+        if(not contains(mode_map, mode))
+        {
+            MIGRAPHX_THROW(
+                "PARSE_POOLING: 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_shape = l0->get_shape();
+        assert(in_shape.ndim() > 2);
+        auto kdims = in_shape.ndim() - 2;
+
+        values = handle_values(opd, info, in_shape, values);
+
+        // count include padding, if count include pad is 1, we always use
+        // explicit pad
+        int count_include_pad = 0;
+        if(contains(info.attributes, "count_include_pad"))
+        {
+            if(in_shape.dynamic())
+            {
+                MIGRAPHX_THROW("PARSE_POOLING: count_include_pad attribute is not supported for "
+                               "dynamic input shape");
+            }
+            count_include_pad = info.attributes.at("count_include_pad").i();
+        }
+
         std::vector<int64_t> paddings;
         float pad_val = ((mode == "max") ? std::numeric_limits<float>::lowest() : 0.0f);
 
@@ -123,14 +153,22 @@ struct parse_pooling : op_parser<parse_pooling>
 
         if(contains(info.attributes, "auto_pad"))
         {
-            values["padding"].clear();
-            // return paddings could be empty, then setting to 0 for no padding
-            cal_auto_padding_size(info,
-                                  values,
-                                  values["lengths"].to_vector<std::size_t>(),
-                                  {1, 1},
-                                  in_lens,
-                                  paddings);
+            if(in_shape.dynamic())
+            {
+                MIGRAPHX_THROW(
+                    "PARSE_POOLING: Auto padding pooling with dynamic input shape not supported");
+            }
+            else
+            {
+                values["padding"].clear();
+                // return paddings could be empty, then setting to 0 for no padding
+                cal_auto_padding_size(info,
+                                      values,
+                                      values["lengths"].to_vector<std::size_t>(),
+                                      {1, 1},
+                                      in_shape.lens(),
+                                      paddings);
+            }
         }
 
         if(paddings.size() != 2 * kdims)
@@ -150,6 +188,7 @@ struct parse_pooling : op_parser<parse_pooling>
             values["stride"].resize(kdims);
             std::fill_n(values["stride"].begin(), kdims, 1);
         }
+
         // used to calculate the supposed output shape
         std::vector<int64_t> orig_padding = paddings;
 
@@ -159,6 +198,11 @@ struct parse_pooling : op_parser<parse_pooling>
 
         if(not slice_start.empty())
         {
+            if(in_shape.dynamic())
+            {
+                MIGRAPHX_THROW(
+                    "PARSE_POOLING: asymmetric padding not supported for dynamic input shape");
+            }
             // calculate expected output shape
             orig_padding.insert(orig_padding.begin() + kdims, 2, 0);
             orig_padding.insert(orig_padding.begin(), 2, 0);

src/onnx/parse_reduce_op.cpp

@@ -68,8 +68,7 @@ instruction_ref parse_reduce_oper(const std::string& op_name,
         }
         else
         {
-            std::size_t n_dim = args.front()->get_shape().lens().size();
-            axes.resize(n_dim);
+            axes.resize(args.front()->get_shape().ndim());
             std::iota(axes.begin(), axes.end(), 0);
         }
     }

src/onnx/parse_transpose.cpp

@@ -47,7 +47,7 @@ struct parse_transpose : op_parser<parse_transpose>
         }
 
         // if perm is empty, use the default value
-        auto n_dim = args.front()->get_shape().lens().size();
+        auto n_dim = args.front()->get_shape().ndim();
         if(perm.empty())
         {
             perm.resize(n_dim);

src/program.cpp

@@ -854,6 +854,25 @@ void program::print_graph(std::ostream& os, bool brief) const
     mm->print_graph(os, brief);
 }
 
+void program::print_py(std::ostream& os) const
+{
+    auto vec_modules = this->get_modules();
+    std::unordered_map<instruction_ref, std::string> names;
+    os << "p = migraphx.program()\n";
+    for(auto& mod : vec_modules)
+    {
+        std::string var_name = "m" + mod->name();
+        os << var_name << " = ";
+        if(mod->name() == "main")
+            os << "p.get_main_module()";
+        else
+            os << "p.create_module(\"" << mod->name() << "\");";
+        os << std::endl;
+        names = mod->print_py(os, var_name, names);
+        os << std::endl;
+    }
+}
+
 void program::print_cpp(std::ostream& os) const
 {
     auto vec_modules = this->get_modules();

src/rewrite_rnn.cpp

@@ -92,7 +92,7 @@ void rewrite_rnn::apply_vanilla_rnn(module& m, instruction_ref ins) const
 
     // process sequence length
     instruction_ref seq_lens = m.end();
-    if((args.size() >= 5) && args[4]->name() != "undefined")
+    if((args.size() >= 5) and not args[4]->is_undefined())
     {
         seq_lens = args[4];
     }
@@ -117,7 +117,7 @@ void rewrite_rnn::apply_vanilla_rnn(module& m, instruction_ref ins) const
         // process bias
         instruction_ref bias_forward = m.end();
         instruction_ref bias_reverse = m.end();
-        if(args.size() >= 4 && args[3]->name() != "undefined")
+        if(args.size() >= 4 and not args[3]->is_undefined())
         {
             bias_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[3]);
@@ -129,7 +129,7 @@ void rewrite_rnn::apply_vanilla_rnn(module& m, instruction_ref ins) const
         // or the 5th one (if the sequence len argument is ignored)
         instruction_ref ih_forward{};
         instruction_ref ih_reverse{};
-        if(args.size() == 6 && args[5]->name() != "undefined")
+        if(args.size() == 6 and not args[5]->is_undefined())
         {
             ih_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[5]);
@@ -195,14 +195,14 @@ void rewrite_rnn::apply_vanilla_rnn(module& m, instruction_ref ins) const
 
         // process bias and initial hidden state
         instruction_ref bias = m.end();
-        if(args.size() >= 4 && args[3]->name() != "undefined")
+        if(args.size() >= 4 and not args[3]->is_undefined())
         {
             bias = args[3];
         }
 
         // process intial hidden state
         instruction_ref ih;
-        if(args.size() == 6 && args[5]->name() != "undefined")
+        if(args.size() == 6 and not args[5]->is_undefined())
         {
             ih = args[5];
         }
@@ -398,7 +398,7 @@ void rewrite_rnn::apply_gru(module& m, instruction_ref ins) const
 
     // process sequence length
     instruction_ref seq_lens = m.end();
-    if((args.size() >= 5) && args[4]->name() != "undefined")
+    if((args.size() >= 5) and not args[4]->is_undefined())
     {
         seq_lens = args[4];
     }
@@ -423,7 +423,7 @@ void rewrite_rnn::apply_gru(module& m, instruction_ref ins) const
         // bias
         instruction_ref bias_forward = m.end();
         instruction_ref bias_reverse = m.end();
-        if(args.size() >= 4 && args[3]->name() != "undefined")
+        if(args.size() >= 4 and not args[3]->is_undefined())
         {
             bias_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[3]);
@@ -434,7 +434,7 @@ void rewrite_rnn::apply_gru(module& m, instruction_ref ins) const
         // intial hidden state
         instruction_ref ih_forward{};
         instruction_ref ih_reverse{};
-        if(args.size() == 6 && args[5]->name() != "undefined")
+        if(args.size() == 6 and not args[5]->is_undefined())
         {
             ih_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[5]);
@@ -501,14 +501,14 @@ void rewrite_rnn::apply_gru(module& m, instruction_ref ins) const
 
         // bias
         instruction_ref bias = m.end();
-        if(args.size() >= 4 && args[3]->name() != "undefined")
+        if(args.size() >= 4 and not args[3]->is_undefined())
         {
             bias = args[3];
         }
 
         // intial hidden state
         instruction_ref ih{};
-        if(args.size() == 6 && args[5]->name() != "undefined")
+        if(args.size() == 6 and not args[5]->is_undefined())
         {
             ih = args[5];
         }
@@ -784,7 +784,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
 
     // process sequence length
     instruction_ref seq_lens = m.end();
-    if((args.size() >= 5) && args[4]->name() != "undefined")
+    if((args.size() >= 5) and not args[4]->is_undefined())
     {
         seq_lens = args[4];
     }
@@ -813,7 +813,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
         // process bias
         instruction_ref bias_forward = m.end();
         instruction_ref bias_reverse = m.end();
-        if(args.size() >= 4 && args[3]->name() != "undefined")
+        if(args.size() >= 4 and not args[3]->is_undefined())
         {
             bias_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[3]);
@@ -824,7 +824,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
         // process intial hidden state, it is the 6th argument
         instruction_ref ih_forward{};
         instruction_ref ih_reverse{};
-        if(args.size() >= 6 && args[5]->name() != "undefined")
+        if(args.size() >= 6 and not args[5]->is_undefined())
         {
             ih_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[5]);
@@ -840,7 +840,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
         // process initial cell value
         instruction_ref ic_forward{};
         instruction_ref ic_reverse{};
-        if(args.size() >= 7 && args[6]->name() != "undefined")
+        if(args.size() >= 7 and not args[6]->is_undefined())
         {
             ic_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[6]);
@@ -856,7 +856,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
         // process weight of the peephole
         instruction_ref pph_forward = m.end();
         instruction_ref pph_reverse = m.end();
-        if(args.size() == 8 && args[7]->name() != "undefined")
+        if(args.size() == 8 and not args[7]->is_undefined())
         {
             pph_forward = m.insert_instruction(
                 ins, make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {1}}}), args[7]);
@@ -940,14 +940,14 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
 
         // bias
         instruction_ref bias = m.end();
-        if(args.size() >= 4 && args[3]->name() != "undefined")
+        if(args.size() >= 4 and not args[3]->is_undefined())
         {
             bias = args[3];
         }
 
         // initial hidden state
         instruction_ref ih{};
-        if(args.size() >= 6 && args[5]->name() != "undefined")
+        if(args.size() >= 6 and not args[5]->is_undefined())
         {
             ih = args[5];
         }
@@ -958,7 +958,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
 
         // initial cell value
         instruction_ref ic{};
-        if(args.size() >= 7 && args[6]->name() != "undefined")
+        if(args.size() >= 7 and not args[6]->is_undefined())
         {
             ic = args[6];
         }
@@ -969,7 +969,7 @@ void rewrite_rnn::apply_lstm(module& m, instruction_ref ins) const
 
         // process weight of the peephole
         instruction_ref pph = m.end();
-        if(args.size() == 8 && args[7]->name() != "undefined")
+        if(args.size() == 8 and not args[7]->is_undefined())
         {
             pph = args[7];
         }

src/shape.cpp

@@ -521,6 +521,14 @@ std::ostream& operator<<(std::ostream& os, const shape::dynamic_dimension& x)
     return os;
 }
 
+bool operator==(const shape::dynamic_dimension& x, const std::size_t& y)
+{
+    return x.min == y and x.max == y;
+}
+bool operator==(const std::size_t& x, const shape::dynamic_dimension& y) { return y == x; }
+bool operator!=(const shape::dynamic_dimension& x, const std::size_t& y) { return not(x == y); }
+bool operator!=(const std::size_t& x, const shape::dynamic_dimension& y) { return not(x == y); }
+
 bool operator==(const shape& x, const shape& y)
 {
     if(x.dynamic() and y.dynamic())

src/targets/gpu/CMakeLists.txt

@@ -233,11 +233,14 @@ get_target_property(MIOPEN_LOCATION MIOpen LOCATION)
 check_library_exists(MIOpen "miopenHiddenSetConvolutionFindMode" "${MIOPEN_LOCATION}" HAS_FIND_MODE_API)
 check_library_exists(MIOpen "miopenFindSolutions" "${MIOPEN_LOCATION}" HAS_FIND_2_API)
 
-if(HAS_FIND_2_API) 
+# TODO: Set default to HAS_FIND_2_API
+set(MIGRAPHX_USE_FIND_2_API OFF CACHE BOOL "")
+
+if(MIGRAPHX_USE_FIND_2_API) 
     target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API)
     message(STATUS "MIGraphx is using Find-2.0 API of MIOpen")
 else()
-    message(STATUS "MIOpen does not have Find-2.0 API")
+    message(STATUS "MIGraphx is using legacy Find API in MIOpen")
 endif()
 
 if(HAS_FIND_MODE_API)

src/targets/gpu/compile_hip.cpp

@@ -185,7 +185,7 @@ compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std
     options.push_back("-fno-gpu-rdc");
     options.push_back(" -O" + string_value_of(MIGRAPHX_GPU_OPTIMIZE{}, "3"));
     options.push_back("-Wno-cuda-compat");
-    options.push_back("--cuda-gpu-arch=" + arch);
+    options.push_back("--offload-arch=" + arch);
     prog.compile(options);
     return {prog.get_code_obj()};
 }
@@ -237,7 +237,7 @@ compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std
     }
     else if(is_hip_clang_compiler())
     {
-        params += " --cuda-gpu-arch=" + arch;
+        params += " --offload-arch=" + arch;
         params += " --cuda-device-only";
         params += " -O" + string_value_of(MIGRAPHX_GPU_OPTIMIZE{}, "3") + " ";
     }

src/targets/gpu/include/migraphx/gpu/hip.hpp

@@ -105,7 +105,7 @@ struct hip_copy_to_gpu
     std::string name() const { return "hip::copy_to_gpu"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1, 2);
+        check_shapes{inputs, *this}.has(1, 2).same_type();
         return inputs.at(0);
     }
     argument compute(context& ctx, const shape&, const std::vector<argument>& args) const
@@ -131,7 +131,7 @@ struct hip_copy_from_gpu
     std::string name() const { return "hip::copy_from_gpu"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1, 2);
+        check_shapes{inputs, *this}.has(1, 2).same_type();
         return inputs.at(0);
     }
     argument
@@ -159,7 +159,7 @@ struct hip_copy
     std::string name() const { return "hip::copy"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(2);
+        check_shapes{inputs, *this}.has(2).same_type();
         return inputs.at(1);
     }
     argument compute(context& ctx, const shape&, std::vector<argument> args) const