Merge branch 'develop' into jit-reduce-reg

src/include/migraphx/streamutils.hpp

@@ -26,7 +26,9 @@
 
 #include <ostream>
 #include <algorithm>
+#include <migraphx/reflect.hpp>
 #include <migraphx/rank.hpp>
+#include <migraphx/requires.hpp>
 #include <migraphx/config.hpp>
 #include <vector>
 
@@ -83,6 +85,20 @@ auto stream_write_value_impl(rank<0>, std::ostream& os, const Range& r)
     os << "}";
 }
 
+template <class T, MIGRAPHX_REQUIRES(is_reflectable<T>{})>
+void stream_write_value_impl(rank<0>, std::ostream& os, const T& x)
+{
+    char delim = '{';
+    reflect_each(x, [&](auto&& y, auto name) {
+        os << delim;
+        os << name << "=";
+        stream_write_value_impl(rank<2>{}, os, y);
+        delim = ',';
+    });
+    if(delim == ',')
+        os << "}";
+}
+
 } // namespace detail
 
 template <class T>

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/rewrite_batchnorm.cpp → src/layout_nhwc.cpp

@@ -21,63 +21,98 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#include <migraphx/rewrite_batchnorm.hpp>
-#include <migraphx/program.hpp>
+#include <migraphx/layout_nhwc.hpp>
+#include <migraphx/module.hpp>
 #include <migraphx/instruction.hpp>
-#include <migraphx/op/batch_norm_inference.hpp>
-#include <migraphx/op/broadcast.hpp>
-#include <migraphx/op/add.hpp>
-#include <migraphx/op/mul.hpp>
 #include <migraphx/iterator_for.hpp>
+#include <migraphx/permutation.hpp>
+#include <migraphx/functional.hpp>
 #include <migraphx/ranges.hpp>
 #include <migraphx/make_op.hpp>
-
-#include <migraphx/dfor.hpp>
+#include <migraphx/eliminate_contiguous.hpp>
+#include <migraphx/dead_code_elimination.hpp>
+#include <migraphx/pass_manager.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-void rewrite_batchnorm::apply(module& m) const
+template <class Predicate>
+std::vector<instruction_ref> find_lasts(const module& m, Predicate pred)
+{
+    std::vector<instruction_ref> result;
+    fix([&](auto self, auto ins) {
+        if(pred(ins))
+        {
+            result.push_back(ins);
+            return;
+        }
+        for(auto input : ins->inputs())
+            self(input);
+    })(std::prev(m.end()));
+    return result;
+}
+
+std::unordered_set<instruction_ref> preserve_output_layout(module& m)
+{
+    std::unordered_set<instruction_ref> result;
+    std::vector<instruction_ref> outputs = find_lasts(m, [](auto ins) {
+        return ins->name() == "convolution" and ins->get_shape().lens().size() == 4;
+    });
+    for(auto output : outputs)
+    {
+        auto permutation = find_permutation(output->get_shape());
+        auto layout      = m.insert_instruction(
+            std::next(output), make_op("layout", {{"permutation", permutation}}), output);
+        result.insert(m.replace_instruction(output, layout));
+    }
+    return result;
+}
+
+void transform_convolutions(module& m)
 {
     for(auto ins : iterator_for(m))
     {
-        if(ins->name() != "batch_norm_inference")
+        if(ins->name() != "convolution")
             continue;
-        // Get scale, bias, mean, variance from inputs
-        auto gamma    = ins->inputs()[1]->eval();
-        auto bias     = ins->inputs()[2]->eval();
-        auto mean     = ins->inputs()[3]->eval();
-        auto variance = ins->inputs()[4]->eval();
-        if(any_of({gamma, bias, mean, variance}, [](auto arg) { return arg.empty(); }))
+        if(ins->get_shape().lens().size() != 4)
             continue;
+        auto v = ins->get_operator().to_value();
+        if(v.at("group").to<int>() > 1)
+            continue;
+        auto args = ins->inputs();
+        std::transform(args.begin(), args.end(), args.begin(), [&](const auto& i) {
+            return m.insert_instruction(ins, make_op("layout", {{"permutation", {0, 2, 3, 1}}}), i);
+        });
+        auto conv = m.insert_instruction(ins, ins->get_operator(), args);
+        auto c    = m.insert_instruction(ins, make_op("contiguous"), conv);
+        m.replace_instruction(ins, c);
+    }
+}
 
-        std::vector<std::size_t> lens = ins->inputs()[1]->get_shape().lens();
-        shape s{ins->get_shape().type(), lens};
-        // Get epsilon
-        auto bn_op   = any_cast<op::batch_norm_inference>(ins->get_operator());
-        auto epsilon = bn_op.epsilon;
-
-        argument a{s};
-        argument b{s};
-        visit_all(gamma, bias, mean, variance, a, b)(
-            [&](auto gamma2, auto bias2, auto mean2, auto variance2, auto a2, auto b2) {
-                dfor(a.get_shape().elements())(
-                    [&](std::size_t c) { a2[c] = gamma2[c] / std::sqrt(variance2[c] + epsilon); });
-                dfor(b.get_shape().elements())([&](std::size_t c) {
-                    b2[c] = bias2[c] - (gamma2[c] * mean2[c] / std::sqrt(variance2[c] + epsilon));
-                });
-            });
-
-        auto broadcast   = op::broadcast{1, ins->get_shape().lens()};
-        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);
+void remove_layout(module& m, const std::unordered_set<instruction_ref>& output_layouts)
+{
+    for(auto ins : iterator_for(m))
+    {
+        if(ins->name() != "layout")
+            continue;
+        if(ins->get_shape() != ins->inputs().front()->get_shape())
+            continue;
+        if(contains(output_layouts, ins))
+            continue;
+        m.replace_instruction(ins, ins->inputs().front());
     }
 }
 
+void layout_nhwc::apply(module_pass_manager& mpm) const
+{
+    std::unordered_set<instruction_ref> output_layouts = preserve_output_layout(mpm.get_module());
+    transform_convolutions(mpm.get_module());
+    mpm.run_pass(dead_code_elimination{});
+    mpm.run_pass(eliminate_contiguous{"contiguous"});
+    mpm.run_pass(dead_code_elimination{});
+    remove_layout(mpm.get_module(), output_layouts);
+    mpm.run_pass(dead_code_elimination{});
+}
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/load_save.cpp

@@ -25,7 +25,6 @@
 #include <migraphx/file_buffer.hpp>
 #include <migraphx/json.hpp>
 #include <migraphx/msgpack.hpp>
-#include <migraphx/file_buffer.hpp>
 #include <fstream>
 
 namespace migraphx {

src/module.cpp

@@ -34,7 +34,6 @@
 #include <migraphx/pass_manager.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/register_target.hpp>
-#include <migraphx/make_op.hpp>
 #include <migraphx/json.hpp>
 #include <iostream>
 #include <sstream>
@@ -790,6 +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();
@@ -805,6 +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();
@@ -814,6 +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,
@@ -875,6 +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/conv.cpp

@@ -30,7 +30,7 @@ namespace onnx {
 
 void recalc_conv_attributes(value& v, size_t kdims)
 {
-    if(not(v["padding"].size() == kdims or v["padding"].size() == kdims * 2))
+    if(v["padding"].size() != kdims and v["padding"].size() != kdims * 2)
     {
         v["padding"].resize(kdims);
         std::fill_n(v["padding"].begin(), kdims, 0);

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_batchnorm.cpp

@@ -44,7 +44,7 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
         {
             epsilon = parser.parse_value(info.attributes.at("epsilon")).at<float>();
         }
-        auto x_lens = args[0]->get_shape().lens();
+        auto x_lens = args[0]->get_shape().max_lens();
         auto x_type = args[0]->get_shape().type();
 
         if(std::any_of(args.cbegin() + 1, args.cend(), [](auto a) {
@@ -54,18 +54,19 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
             MIGRAPHX_THROW("PARSE_BATCHNORM: argument scale, bias, mean, or var rank != 1");
         }
 
-        if(x_lens.size() == 1)
+        auto x_rank = x_lens.size();
+        if(x_rank == 1 or x_rank == 2)
         {
-            auto rt   = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
-            auto eps  = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
-            auto n0   = info.add_broadcastable_binary_op("sub", args[0], args[3]);
-            auto d0   = info.add_broadcastable_binary_op("add", args[4], eps);
-            auto d1   = info.add_broadcastable_binary_op("pow", d0, rt);
-            auto div0 = info.add_broadcastable_binary_op("div", n0, d1);
-            auto r0   = info.add_broadcastable_binary_op("mul", div0, args[1]);
+            auto rt      = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
+            auto eps     = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
+            auto numer   = info.add_broadcastable_binary_op("sub", args[0], args[3]);
+            auto var_eps = info.add_broadcastable_binary_op("add", args[4], eps);
+            auto denom   = info.add_broadcastable_binary_op("pow", var_eps, rt);
+            auto div0    = info.add_broadcastable_binary_op("div", numer, denom);
+            auto r0      = info.add_broadcastable_binary_op("mul", div0, args[1]);
             return info.add_broadcastable_binary_op("add", r0, args[2]);
         }
-        else if(x_lens.size() > 2)
+        else if(x_rank > 2)
         {
             // unsqueeze tensors of shape (C) to broadcast correctly
             std::vector<int64_t> unsqueeze_axes(x_lens.size() - 2);
@@ -89,7 +90,7 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
         }
         else
         {
-            // num dims either 0 or 2
+            // rank == 0
             MIGRAPHX_THROW("PARSE_BATCHNORM: rank " + std::to_string(x_lens.size()) +
                            " input tensor, unhandled data format");
         }

src/onnx/parse_binary_op.cpp

@@ -57,6 +57,12 @@ struct parse_binary_op : op_parser<parse_binary_op>
                 parser.parse_value(info.attributes.at("broadcast")).at<uint64_t>();
             if(broadcasted != 0)
             {
+                if(std::any_of(
+                       args.cbegin(), args.cend(), [](auto a) { return a->get_shape().dynamic(); }))
+                {
+                    MIGRAPHX_THROW(
+                        "Binary op broadcast attribute not supported for dynamic input shapes");
+                }
                 uint64_t axis = parser.parse_value(info.attributes.at("axis")).at<uint64_t>();
                 auto l        = info.add_instruction(
                     make_op("broadcast",

src/onnx/parse_convolution.cpp

@@ -125,11 +125,9 @@ struct parse_convolution : op_parser<parse_convolution>
                     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());

src/onnx/parse_deconvolution.cpp

@@ -95,6 +95,8 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
             check_attr_sizes(
                 kdims, values["dilation"].size(), "PARSE_CONV_TRANSPOSE: inconsistent dilations");
         }
+
+        // TODO: auto padding needs to be implemented for this parser and operator
         if(contains(info.attributes, "auto_pad"))
         {
             auto s = info.attributes["auto_pad"].s();
@@ -106,7 +108,9 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
 
             if(s.find("SAME") != std::string::npos)
             {
-                values["padding_mode"] = to_value(op::padding_mode_t::same);
+                bool is_same_upper     = (s.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);
             }
         }
 

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_split.cpp

@@ -26,6 +26,9 @@
 #include <migraphx/ranges.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/tune_axis.hpp>
+#include <migraphx/onnx/checks.hpp>
+
+#include <migraphx/stringutils.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -55,12 +58,12 @@ struct parse_split : op_parser<parse_split>
         {
             literal s = parser.parse_value(info.attributes.at("split"));
             s.visit([&](auto v) { vec_splits.assign(v.begin(), v.end()); });
-
-            if(std::accumulate(vec_splits.begin(), vec_splits.end(), int64_t(0)) !=
-               static_cast<int64_t>(lens[tuned_axis]))
-            {
-                MIGRAPHX_THROW("PARSE_SPLIT: sum of split attribute unequal to dim size of axis!");
-            }
+        }
+        else if(args.size() == 2)
+        {
+            auto s = args[1]->eval();
+            check_arg_empty(s, "Split: dynamic shape is not supported");
+            s.visit([&](auto v) { vec_splits.assign(v.begin(), v.end()); });
         }
         // no split attribute, input is equally divided
         else
@@ -74,6 +77,15 @@ struct parse_split : op_parser<parse_split>
             vec_splits.resize(info.num_outputs, dl);
         }
 
+        if(std::accumulate(vec_splits.begin(), vec_splits.end(), int64_t(0)) !=
+           static_cast<int64_t>(lens[tuned_axis]))
+        {
+            MIGRAPHX_THROW(
+                "PARSE_SPLIT: sum of split attribute unequal to dim size of axis! tuned axis:" +
+                std::to_string(lens[tuned_axis]) + " Output " + to_string_range(vec_splits) +
+                " Rank " + std::to_string(n_rank) + " Len outs " + to_string_range(lens));
+        }
+
         std::vector<instruction_ref> ret_ins;
         int64_t start = 0;
         for(auto sl : vec_splits)

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/pad_calc.cpp

@@ -52,19 +52,21 @@ void calculate_padding(int64_t idx,
     }
 }
 
-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,
+std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
+                                           const std::vector<std::size_t>& wei_lens,
+                                           const std::vector<std::size_t>& strides,
+                                           const std::vector<std::size_t>& dilations,
                                            bool use_upper)
 {
     std::vector<std::size_t> padding;
-    padding.resize(2 * k_lens.size());
-    for(std::size_t i = 0; i < padding.size() / 2; i++)
+    assert(input_lens.size() >= 3);
+    std::size_t num_spatial_dims = input_lens.size() - 2;
+    padding.resize(2 * num_spatial_dims);
+    for(std::size_t i = 0; i < num_spatial_dims; i++)
     {
-        std::ptrdiff_t input_dim      = tensor_lens[i];
+        std::ptrdiff_t input_dim      = input_lens[i + 2];
         std::ptrdiff_t stride         = strides[i];
-        std::ptrdiff_t weight_dim     = k_lens[i];
+        std::ptrdiff_t weight_dim     = wei_lens[i + 2];
         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);
@@ -86,5 +88,28 @@ std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
     return padding;
 }
 
+shape compute_padded_shape(const shape& input,
+                           const shape& weights,
+                           const std::vector<std::size_t>& padding,
+                           const std::vector<std::size_t>& stride,
+                           const std::vector<std::size_t>& dilation)
+{
+    const size_t num_spatial_dims = input.lens().size() - 2;
+
+    std::vector<size_t> output_lens{input.lens()[0], weights.lens()[0]};
+    // calculate the output shape of the convolution: ((W - K + 2P) / S) + 1
+    for(size_t i = 0; i < num_spatial_dims; ++i)
+    {
+        auto padding_factor = padding[i] + padding[i + num_spatial_dims];
+        output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(
+            1,
+            (input.lens()[i + 2] - (1 + dilation[i] * (weights.lens()[i + 2] - 1)) +
+             padding_factor) /
+                    stride[i] +
+                1)));
+    }
+    return input.with_lens(output_lens);
+}
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/pass_manager.cpp

@@ -94,11 +94,19 @@ struct module_pm : module_pass_manager
     virtual void run_pass(const pass& p) override
     {
         assert(mod);
+
+        timer ts{};
+        using seconds = std::chrono::duration<double>;
+
         trace("Module: ", mod->name(), ", Pass: ", p.name());
+        const double t1 = ts.record<seconds>();
         assert(mod->validate() == mod->end());
         p.apply(*this);
         trace(*mod);
         validate_pass(*mod, p, *t);
+
+        const double t2 = ts.record<seconds>();
+        trace("Pass: ", p.name(), " completed in (s): ", (t2 - t1));
     }
 };
 

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

@@ -46,9 +46,6 @@
 #include <migraphx/iterator_for.hpp>
 #include <migraphx/dfor.hpp>
 #include <migraphx/ranges.hpp>
-#include <migraphx/op/common.hpp>
-#include <migraphx/op/rnn_var_sl_last_output.hpp>
-#include <migraphx/op/rnn_variable_seq_lens.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -95,7 +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];
     }
@@ -120,7 +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]);
@@ -132,7 +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]);
@@ -198,14 +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];
         }
@@ -401,7 +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];
     }
@@ -426,7 +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]);
@@ -437,7 +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]);
@@ -504,14 +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];
         }
@@ -787,7 +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];
     }
@@ -816,7 +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]);
@@ -827,7 +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]);
@@ -843,7 +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]);
@@ -859,7 +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]);
@@ -943,14 +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];
         }
@@ -961,7 +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];
         }
@@ -972,7 +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

@@ -71,6 +71,19 @@ struct shape_impl
     {
     }
 
+    shape_impl(shape::type_t t,
+               std::vector<std::size_t> mins,
+               std::vector<std::size_t> maxes,
+               std::vector<std::size_t> opts)
+        : m_type(t)
+    {
+        assert(mins.size() == maxes.size() and maxes.size() == opts.size());
+        for(size_t i = 0; i < mins.size(); ++i)
+        {
+            m_dyn_dims.push_back(shape::dynamic_dimension{mins[i], maxes[i], opts[i]});
+        }
+    }
+
     shape_impl(const std::vector<shape>& subs) : m_type(shape::tuple_type), m_shapes(subs) {}
 
     shape::type_t m_type;
@@ -224,6 +237,14 @@ shape::shape(type_t t, std::vector<shape::dynamic_dimension> dims)
 {
 }
 
+shape::shape(type_t t,
+             std::vector<std::size_t> mins,
+             std::vector<std::size_t> maxes,
+             std::vector<std::size_t> opts)
+    : impl(std::make_shared<shape_impl>(t, std::move(mins), std::move(maxes), std::move(opts)))
+{
+}
+
 shape::shape(const std::vector<shape>& subs) : impl(std::make_shared<shape_impl>(subs)) {}
 
 shape::shape(std::shared_ptr<shape_impl> pimpl) : impl(std::move(pimpl)) {}
@@ -244,6 +265,15 @@ const std::vector<std::size_t>& shape::lens() const { return impl->m_lens; }
 
 const std::vector<std::size_t>& shape::strides() const { return impl->m_strides; }
 
+std::size_t shape::ndim() const
+{
+    if(this->dynamic())
+    {
+        return dyn_dims().size();
+    }
+    return lens().size();
+}
+
 std::size_t shape::elements() const { return impl->elements(); }
 
 std::size_t shape::bytes() const
@@ -437,6 +467,16 @@ shape shape::with_type(type_t t) const
     return {c};
 }
 
+shape shape::to_dynamic() const
+{
+    if(this->dynamic())
+    {
+        return *this;
+    }
+    std::vector<std::size_t> zeroes(this->ndim(), 0);
+    return {type(), lens(), lens(), zeroes};
+}
+
 std::size_t shape::element_space() const { return impl->element_space(); }
 
 std::string shape::type_string() const { return name(this->type()); }
@@ -464,15 +504,11 @@ bool shape::dynamic_dimension::is_fixed() const { return this->min == this->max;
 
 bool shape::dynamic_dimension::has_optimal() const { return opt != 0; }
 
-template <class Self, class F>
-auto shape::dynamic_dimension::reflect(Self& self, F f)
-{
-    return pack(f(self.min, "min"), f(self.max, "max"), f(self.opt, "opt"));
-}
-
 bool operator==(const shape::dynamic_dimension& x, const shape::dynamic_dimension& y)
 {
-    return (x.min == y.min and x.max == y.max and x.opt == y.opt);
+    // don't check opt if both are fixed
+    return (x.min == y.min and x.max == y.max and
+            ((x.is_fixed() and y.is_fixed()) or (x.opt == y.opt)));
 }
 
 bool operator!=(const shape::dynamic_dimension& x, const shape::dynamic_dimension& y)
@@ -485,6 +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())