Merge

src/include/migraphx/op/topk.hpp

@@ -26,6 +26,7 @@
 
 #include <algorithm>
 #include <migraphx/check_shapes.hpp>
+#include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
 #include <migraphx/par_for.hpp>

src/include/migraphx/op/transpose.hpp

@@ -24,14 +24,11 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_TRANSPOSE_HPP
 #define MIGRAPHX_GUARD_OPERATORS_TRANSPOSE_HPP
 
-#include <array>
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
-#include <migraphx/functional.hpp>
 #include <migraphx/config.hpp>
-#include <migraphx/lifetime.hpp>
-#include <cmath>
-#include <utility>
+#include <migraphx/value.hpp>
+#include <migraphx/op/normalize_attribute.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/include/migraphx/op/unary_not.hpp

@@ -24,10 +24,9 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_UNARY_NOT_HPP
 #define MIGRAPHX_GUARD_OPERATORS_UNARY_NOT_HPP
 
-#include <migraphx/op/unary.hpp>
-#include <migraphx/operation.hpp>
-#include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/op/unary.hpp>
+#include <cmath>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/include/migraphx/op/unknown.hpp

@@ -25,7 +25,6 @@
 #define MIGRAPHX_GUARD_RTGLIB_UNKNOWN_HPP
 
 #include <migraphx/config.hpp>
-#include <migraphx/argument.hpp>
 #include <migraphx/check_shapes.hpp>
 
 namespace migraphx {

src/include/migraphx/op/unsqueeze.hpp

@@ -24,16 +24,11 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_UNSQUEEZE_HPP
 #define MIGRAPHX_GUARD_OPERATORS_UNSQUEEZE_HPP
 
-#include <array>
 #include <migraphx/check_shapes.hpp>
-#include <migraphx/stringutils.hpp>
-#include <migraphx/streamutils.hpp>
-#include <migraphx/shape_for_each.hpp>
+#include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
-#include <migraphx/lifetime.hpp>
-#include <cmath>
-#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/include/migraphx/op/where.hpp

@@ -24,18 +24,11 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_WHERE_HPP
 #define MIGRAPHX_GUARD_OPERATORS_WHERE_HPP
 
-#include <array>
-#include <migraphx/argument.hpp>
-#include <migraphx/par_for.hpp>
 #include <migraphx/check_shapes.hpp>
-#include <migraphx/stringutils.hpp>
-#include <migraphx/streamutils.hpp>
-#include <migraphx/shape_for_each.hpp>
+#include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
-#include <migraphx/op/normalize_attribute.hpp>
-#include <cmath>
-#include <utility>
+#include <migraphx/par_for.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/include/migraphx/operation.hpp

@@ -68,8 +68,10 @@ struct operation
      *
      * @param ctx This is the context created by the `target` during compilation. Implementations
      * can use the target's `context` class rather than the `context` interface class.
-     * @param output This is the output shape. It is equivalent to running `compute_shape` with each
-     * `shape` of the `argument`.
+     * @param output Equivalent to running `compute_shape` with each `shape` of the `argument`.
+     * For a fixed shape, the returned argument will have the same shape as `output`.
+     * For a dynamic shape, the returned `argument` will be a fixed shape within the bounds
+     * set in the dynamic shape `output`.
      * @param input This is the `argument` result from the previous instruction's computation.
      * @return Return an `argument` of the result computation. The `shape` of `argument` should be
      * the same the `output` shape.
@@ -137,7 +139,7 @@ auto compute_shape_op(rank<2>, const T& x, const std::vector<shape>& inputs)
     -> decltype(x.normalize_compute_shape(inputs))
 {
     dependent_type<operation, T> y = x;
-    normalize_attributes(y, inputs[0].lens());
+    normalize_attributes(y, inputs[0].max_lens());
     return any_cast<T>(y).normalize_compute_shape(inputs);
 }
 

src/include/migraphx/operators.hpp

@@ -57,6 +57,7 @@
 #include <migraphx/op/exp.hpp>
 #include <migraphx/op/flatten.hpp>
 #include <migraphx/op/floor.hpp>
+#include <migraphx/op/fmod.hpp>
 #include <migraphx/op/gather.hpp>
 #include <migraphx/op/gathernd.hpp>
 #include <migraphx/op/get_tuple_elem.hpp>
@@ -79,6 +80,7 @@
 #include <migraphx/op/lstm.hpp>
 #include <migraphx/op/max.hpp>
 #include <migraphx/op/min.hpp>
+#include <migraphx/op/mod.hpp>
 #include <migraphx/op/mul.hpp>
 #include <migraphx/op/multibroadcast.hpp>
 #include <migraphx/op/neg.hpp>

src/include/migraphx/pad_calc.hpp

@@ -24,38 +24,36 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
 #define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
 
-#include <utility>
+#include <migraphx/config.hpp>
 #include <cstdint>
 #include <vector>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-inline 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 = true)
-{
-    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;
+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 = true);
 
-    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;
-    }
-}
+/*!
+ * Calculate the padding for auto_padding. Used for dynamic shapes
+ * where the padding calculation must be done at evaluation time.
+ * \param tensor_lens input tensor image shape
+ * \param k_lens weights kernel shape
+ * \param strides strides for the kernel
+ * \param dilations dilations for the kernel
+ * \param use_upper put odd padding on upper or lower side
+ * \return padding in the form of {x0_begin, x1_begin, ... x0_end , x1_end, ...}
+ */
+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 = true);
 
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/include/migraphx/par_dfor.hpp

@@ -25,6 +25,7 @@
 #define MIGRAPHX_GUARD_RTGLIB_PAR_DFOR_HPP
 
 #include <migraphx/par_for.hpp>
+#include <migraphx/dfor.hpp>
 #include <migraphx/functional.hpp>
 #include <array>
 #include <numeric>

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/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/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());