Merge branch 'develop' into dyn_onnx_matmul

Dockerfile

@@ -87,7 +87,7 @@ RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXR
 
 ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
 
-RUN cget -p /usr/local install ROCmSoftwarePlatform/llvm-project-mlir@c0723a7e50043d973cb73ae51dc30d36679ee7e5 -DBUILD_MIXR_TARGET=On
+RUN cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@0f38fb33f518b53b94b541feb9b079668c5518e8 -DBUILD_MIXR_TARGET=On -DLLVM_ENABLE_ZSTD=Off -DLLVM_ENABLE_THREADS=Off
 
 ENV MIOPEN_FIND_DB_PATH=/tmp/miopen/find-db
 ENV MIOPEN_USER_DB_PATH=/tmp/miopen/user-db

src/common.cpp

@@ -77,7 +77,6 @@ std::vector<shape::dynamic_dimension> compute_broadcasted_dyn_dims(shape s0, sha
     }
     auto offset = s1.ndim() - s0.ndim();
     std::vector<shape::dynamic_dimension> out_dims(s1.dyn_dims());
-    shape::dynamic_dimension one_dyn_dim{1, 1, 0};
     std::transform(
         s0.dyn_dims().cbegin(),
         s0.dyn_dims().cend(),
@@ -88,7 +87,7 @@ std::vector<shape::dynamic_dimension> compute_broadcasted_dyn_dims(shape s0, sha
             {
                 return a;
             }
-            else if(a == one_dyn_dim or b == one_dyn_dim)
+            else if(a == 1 or b == 1)
             {
                 // setting opt to 0, may need to be changed
                 return shape::dynamic_dimension{std::max(a.min, b.min), std::max(a.max, b.max), 0};

src/dead_code_elimination.cpp

@@ -51,8 +51,8 @@ void dead_code_elimination::apply(module& m) const
         // Skip instruction with empty shape as output unless its [dynamic, builtin, undefined,
         // identity, allocate]
         if((not i->get_shape().dynamic() and i->get_shape().elements() == 0) and
-           i->name().front() != '@' and
-           not contains({"undefined", "identity", "allocate"}, i->name()))
+           not(i->name().front() == '@') and not contains({"identity", "allocate"}, i->name()) and
+           not i->is_undefined())
             continue;
         assert(std::distance(m.begin(), i) <= std::distance(m.begin(), last));
         std::unordered_set<instruction_ref> visited;

src/driver/main.cpp

@@ -109,8 +109,12 @@ struct loader
         ap(brief, {"--brief"}, ap.help("Make the output brief."), ap.set_value(true));
         ap(output_type,
            {"--cpp"},
-           ap.help("Print out the program as cpp program."),
+           ap.help("Print out the program as C++ program."),
            ap.set_value("cpp"));
+        ap(output_type,
+           {"--python", "--py"},
+           ap.help("Print out the program as python program."),
+           ap.set_value("py"));
         ap(output_type, {"--json"}, ap.help("Print out program as json."), ap.set_value("json"));
         ap(output_type,
            {"--text"},
@@ -259,7 +263,9 @@ struct loader
                 type = "binary";
         }
 
-        if(type == "cpp")
+        if(type == "py")
+            p.print_py(*os);
+        else if(type == "cpp")
             p.print_cpp(*os);
         else if(type == "graphviz")
             p.print_graph(*os, brief);

src/file_buffer.cpp

@@ -30,23 +30,31 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
 template <class T>
-T generic_read_file(const std::string& filename)
+T generic_read_file(const std::string& filename, size_t offset = 0, size_t nbytes = 0)
 {
     std::ifstream is(filename, std::ios::binary | std::ios::ate);
-    std::streamsize size = is.tellg();
-    if(size < 1)
+    if(nbytes == 0)
+    {
+        // if there is a non-zero offset and nbytes is not set,
+        // calculate size of remaining bytes to read
+        nbytes = is.tellg();
+        if(offset > nbytes)
+            MIGRAPHX_THROW("offset is larger than file size");
+        nbytes -= offset;
+    }
+    if(nbytes < 1)
         MIGRAPHX_THROW("Invalid size for: " + filename);
-    is.seekg(0, std::ios::beg);
+    is.seekg(offset, std::ios::beg);
 
-    T buffer(size, 0);
-    if(not is.read(&buffer[0], size))
+    T buffer(nbytes, 0);
+    if(not is.read(&buffer[0], nbytes))
         MIGRAPHX_THROW("Error reading file: " + filename);
     return buffer;
 }
 
-std::vector<char> read_buffer(const std::string& filename)
+std::vector<char> read_buffer(const std::string& filename, size_t offset, size_t nbytes)
 {
-    return generic_read_file<std::vector<char>>(filename);
+    return generic_read_file<std::vector<char>>(filename, offset, nbytes);
 }
 
 std::string read_string(const std::string& filename)

src/include/migraphx/file_buffer.hpp

@@ -31,7 +31,7 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-std::vector<char> read_buffer(const std::string& filename);
+std::vector<char> read_buffer(const std::string& filename, size_t offset = 0, size_t nbytes = 0);
 std::string read_string(const std::string& filename);
 
 void write_buffer(const std::string& filename, const char* buffer, std::size_t size);

src/include/migraphx/instruction.hpp

@@ -121,6 +121,8 @@ struct instruction
 
     bool can_eval() const;
 
+    bool is_undefined() const;
+
     argument eval(bool check_eval = true) const;
 
     void finalize(context& ctx);

src/include/migraphx/literal.hpp

@@ -80,6 +80,7 @@ struct literal : raw_data<literal>
         fill(start, end);
     }
 
+    // Directly copies buffer of x
     template <class T, MIGRAPHX_REQUIRES(sizeof(T) == 1)>
     literal(const shape& s, T* x) : buffer(make_shared_array<char>(s.bytes())), m_shape(s)
     {
@@ -107,25 +108,15 @@ struct literal : raw_data<literal>
     std::shared_ptr<char> buffer;
     shape m_shape;
 
+    // Keeps the same data ordering as the given container
     template <class Iterator>
     void fill(Iterator start, Iterator end)
     {
         assert(std::distance(start, end) == m_shape.elements());
-        if(m_shape.standard())
-        {
-            m_shape.visit_type([&](auto as) { std::copy(start, end, as.from(buffer.get())); });
-        }
-        else
-        {
-            auto it = start;
-            m_shape.visit_type([&](auto as) {
-                auto output = make_view(m_shape, as.from(buffer.get()));
-                shape_for_each(output.get_shape(), [&](const auto& idx) {
-                    output(idx.begin(), idx.end()) = *it; // NOLINT(bugprone-signed-char-misuse)
-                    it++;
-                });
-            });
-        }
+        m_shape.visit_type([&](auto as) {
+            auto output = make_view(m_shape, as.from(buffer.get()));
+            std::copy(start, end, output.begin());
+        });
     }
 };
 

src/include/migraphx/module.hpp

@@ -205,6 +205,12 @@ struct module
 
     void print_graph(std::ostream& os, bool brief = false) const;
 
+    void print_py(std::ostream& os) const;
+    std::unordered_map<instruction_ref, std::string>
+    print_py(std::ostream& os,
+             const std::string& mname,
+             std::unordered_map<instruction_ref, std::string> names) const;
+
     void print_cpp(std::ostream& os) const;
     std::unordered_map<instruction_ref, std::string>
     print_cpp(std::ostream& os,

src/include/migraphx/op/argmax.hpp

@@ -30,6 +30,7 @@
 #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 {
@@ -56,12 +57,20 @@ struct argmax
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        auto lens = inputs[0].lens();
-
-        lens[axis] = 1;
-
-        return {shape::int64_type, lens};
+        check_shapes{inputs, *this, true}.has(1);
+        const auto& s0 = inputs[0];
+        if(s0.dynamic())
+        {
+            auto dyn_dims  = s0.dyn_dims();
+            dyn_dims[axis] = {1, 1, 0};
+            return {shape::int64_type, dyn_dims};
+        }
+        else
+        {
+            auto lens  = s0.lens();
+            lens[axis] = 1;
+            return {shape::int64_type, lens};
+        }
     }
 
     template <class T>
@@ -79,19 +88,18 @@ struct argmax
                 max_index = i;
             }
         }
-
         return max_index;
     }
 
-    argument compute(const shape& output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
+        argument result{dyn_out.computed_shape};
         auto batch_item_num = args.front().get_shape().lens()[axis];
 
         result.visit([&](auto output) {
             args[0].visit([&](auto input) {
-                par_for(output_shape.elements(), [&](auto i) {
-                    auto data_idx = output_shape.multi(i);
+                par_for(dyn_out.computed_shape.elements(), [&](auto i) {
+                    auto data_idx = dyn_out.computed_shape.multi(i);
                     output[i]     = this->calc_argmax(input, data_idx, batch_item_num);
                 });
             });

src/include/migraphx/op/flatten.hpp

@@ -55,17 +55,47 @@ struct flatten
     std::string name() const { return "flatten"; }
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1).standard();
-        auto&& lens = inputs.front().lens();
-        auto x =
-            std::accumulate(lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<>{});
-        auto y =
-            std::accumulate(lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
-        return {inputs.at(0).type(), {x, y}};
+        check_shapes{inputs, *this, true}.has(1);
+        auto s = inputs[0];
+        if(s.dynamic())
+        {
+            auto min_lens = s.min_lens();
+            auto max_lens = s.max_lens();
+            auto opt_lens = s.opt_lens();
+            // If any of the opt values is 0, output opt will be 0
+            shape::dynamic_dimension x = {
+                std::accumulate(
+                    min_lens.begin(), min_lens.begin() + axis, std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(
+                    max_lens.begin(), max_lens.begin() + axis, std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(opt_lens.begin(),
+                                opt_lens.begin() + axis,
+                                std::size_t{1},
+                                std::multiplies<>{})};
+            shape::dynamic_dimension y = {
+                std::accumulate(
+                    min_lens.begin() + axis, min_lens.end(), std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(
+                    max_lens.begin() + axis, max_lens.end(), std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(
+                    opt_lens.begin() + axis, opt_lens.end(), std::size_t{1}, std::multiplies<>{}),
+            };
+            return {s.type(), {x, y}};
+        }
+        else
+        {
+            check_shapes{inputs, *this}.standard();
+            auto&& lens = s.lens();
+            auto x      = std::accumulate(
+                lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<>{});
+            auto y = std::accumulate(
+                lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
+            return {s.type(), {x, y}};
+        }
     }
-    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/pooling.hpp

@@ -31,7 +31,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/par_for.hpp>
 #include <migraphx/shape_for_each.hpp>
-#include <migraphx/int_divide.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <cmath>
 #include <utility>
 
@@ -49,6 +49,9 @@ struct pooling
     bool ceil_mode                   = false;
     int lp_order                     = 2;
 
+    // Global pooling with dynamic shape input
+    bool dyn_global = false;
+
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
@@ -57,7 +60,8 @@ struct pooling
                     f(self.stride, "stride"),
                     f(self.lengths, "lengths"),
                     f(self.ceil_mode, "ceil_mode"),
-                    f(self.lp_order, "lp_order"));
+                    f(self.lp_order, "lp_order"),
+                    f(self.dyn_global, "dyn_global"));
     }
 
     std::string name() const { return "pooling"; }
@@ -65,51 +69,111 @@ struct pooling
     void check_attribute_size() const
     {
         if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
-           stride.size() != lengths.size())
+           (not dyn_global and stride.size() != lengths.size()))
         {
             MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
         }
     }
 
+    size_t kdims() const
+    {
+        check_attribute_size();
+        return stride.size();
+    }
+
     value attributes() const { return {{"normalize_padding", "padding"}}; }
 
+    std::vector<std::size_t> calc_spatial_dim_out(const std::vector<std::size_t>& input_lens,
+                                                  std::size_t kdims) const
+    {
+        std::vector<std::size_t> output_lens{};
+        for(size_t i = 0; i < kdims; ++i)
+        {
+            if(input_lens[i + 2] == 0)
+            {
+                // handle opt = 0
+                output_lens.push_back(0);
+            }
+            else
+            {
+                std::size_t padding_factor = 2 * padding[i];
+                if(padding.size() == 2 * kdims)
+                    padding_factor = padding[i] + padding[i + kdims];
+                assert(input_lens[i + 2] + padding_factor >= lengths[i]);
+                std::size_t dim_size = input_lens[i + 2] + padding_factor - lengths[i];
+                std::size_t len =
+                    (ceil_mode)
+                        ? dim_size / stride[i] + static_cast<std::size_t>((dim_size % stride[i] !=
+                                                                           0)) // ceil uint divide
+                        : dim_size / stride[i];                                // floor divide
+                output_lens.push_back(len + 1);
+            }
+        }
+        return output_lens;
+    }
+
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
+        check_shapes{inputs, *this, true}.has(1);
+        check_attribute_size();
 
         const shape& input = inputs.at(0);
-
-        auto input_lens   = input.lens();
-        size_t kdims      = input_lens.size() - 2;
-        auto input_size   = inputs[0].lens().size();
-        auto padding_size = padding.size();
-        if(input_size != padding_size / 2 + 2 and input_size != padding_size + 2)
+        auto padding_size  = padding.size();
+        size_t kdims       = input.ndim() - 2;
+        if(input.ndim() != padding_size / 2 + 2 and input.ndim() != padding_size + 2)
         {
             MIGRAPHX_THROW("POOLING: input and attribute size mismatch!");
         }
 
-        std::vector<std::size_t> output_lens(input_lens.begin(), input_lens.begin() + 2);
-
-        for(size_t i = 0; i < kdims; i++)
+        if(input.dynamic())
         {
-            std::ptrdiff_t dim_size;
-            auto padding_factor = 2 * padding[i];
-            if(padding_size == 2 * kdims)
-                padding_factor = padding[i] + padding[i + kdims];
-            dim_size = input_lens[i + 2] + padding_factor - lengths[i];
-            assert(dim_size >= 0);
-            std::size_t len = (ceil_mode) ? ceil_divide<std::ptrdiff_t>(dim_size, stride[i])
-                                          : floor_divide<std::ptrdiff_t>(dim_size, stride[i]);
-
-            output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(1, len + 1)));
+            auto input_dyn_dims = input.dyn_dims();
+            std::vector<shape::dynamic_dimension> output_dyn_dims(input_dyn_dims.begin(),
+                                                                  input_dyn_dims.begin() + 2);
+            if(dyn_global)
+            {
+                for(size_t i = 0; i < kdims; ++i)
+                {
+                    output_dyn_dims.push_back(shape::dynamic_dimension{1, 1, 1});
+                }
+                return {input.type(), output_dyn_dims};
+            }
+            else
+            {
+                auto min_spatial_dims = calc_spatial_dim_out(input.min_lens(), kdims);
+                auto max_spatial_dims = calc_spatial_dim_out(input.max_lens(), kdims);
+                auto opt_spatial_dims = calc_spatial_dim_out(input.opt_lens(), kdims);
+                for(size_t i = 0; i < kdims; ++i)
+                {
+                    output_dyn_dims.push_back(shape::dynamic_dimension{
+                        min_spatial_dims[i], max_spatial_dims[i], opt_spatial_dims[i]});
+                }
+                return {input.type(), output_dyn_dims};
+            }
         }
-        return inputs[0].with_lens(output_lens);
-    }
+        else
+        {
+            auto input_lens = input.lens();
 
-    size_t kdims() const
-    {
-        check_attribute_size();
-        return stride.size();
+            std::vector<std::size_t> output_lens(input_lens.begin(), input_lens.begin() + 2);
+            // Used for when normalize_compute_shape() is called again at model eval time
+            // for an originally dynamic shape. Since kernel shape is not used with dyn_global.
+            if(dyn_global)
+            {
+                for(size_t i = 0; i < kdims; ++i)
+                {
+                    output_lens.push_back(1);
+                }
+                return {input.type(), output_lens};
+            }
+            else
+            {
+                auto output_spatial_lens = calc_spatial_dim_out(input_lens, kdims);
+                output_lens.insert(
+                    output_lens.end(), output_spatial_lens.begin(), output_spatial_lens.end());
+                return inputs[0].with_lens(output_lens);
+            }
+        }
     }
 
     struct lpnorm_pool
@@ -158,7 +222,11 @@ struct pooling
     };
 
     template <class Type, class Out, class In, class Op>
-    void calc_pooling(const shape& output_shape, Out& output, const In& input, Op op) const
+    void calc_pooling(const shape& output_shape,
+                      Out& output,
+                      const In& input,
+                      const std::vector<std::size_t>& kernel_dims,
+                      Op op) const
     {
         auto in_s    = input.get_shape();
         auto in_lens = in_s.lens();
@@ -172,7 +240,7 @@ struct pooling
                 auto d_2 = dim - 2;
                 int start =
                     static_cast<int>(idx_o[dim] * stride[d_2]) - static_cast<int>(padding[d_2]);
-                int end = std::min(start + lengths[d_2], in_lens[dim]);
+                int end = std::min(start + kernel_dims[d_2], in_lens[dim]);
                 start   = std::max(start, 0);
                 win_start.push_back(start);
                 win_size.push_back(end - start);
@@ -198,21 +266,32 @@ struct pooling
         });
     }
 
-    argument compute(const shape& output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
+        argument result{dyn_out.computed_shape};
+        auto input_lens = args[0].get_shape().lens();
+        std::vector<std::size_t> kernel_dims;
+        if(dyn_global)
+        {
+            kernel_dims.insert(kernel_dims.end(), input_lens.begin() + 2, input_lens.end());
+        }
+        else
+        {
+            kernel_dims = this->lengths;
+        }
         visit_all(result, args[0])([&](auto output, auto input) {
             using type = typename decltype(output)::value_type;
             switch(mode)
             {
             case migraphx::op::pooling_mode::average:
-                calc_pooling<type>(output_shape, output, input, avg_pool{});
+                calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, avg_pool{});
                 break;
             case migraphx::op::pooling_mode::max:
-                calc_pooling<type>(output_shape, output, input, max_pool{});
+                calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, max_pool{});
                 break;
             case migraphx::op::pooling_mode::lpnorm:
-                calc_pooling<type>(output_shape, output, input, lpnorm_pool{lp_order});
+                calc_pooling<type>(
+                    dyn_out.computed_shape, output, input, kernel_dims, lpnorm_pool{lp_order});
                 break;
             }
         });

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

@@ -59,9 +59,8 @@ struct squeeze
         auto input_shape = inputs[0];
         if(input_shape.dynamic())
         {
-            shape::dynamic_dimension one_dyn_dim{1, 1, 0};
             if(std::any_of(axes.begin(), axes.end(), [&](auto axis) {
-                   return input_shape.dyn_dims()[axis] != one_dyn_dim;
+                   return input_shape.dyn_dims()[axis] != 1;
                }))
             {
                 MIGRAPHX_THROW(
@@ -70,14 +69,10 @@ struct squeeze
             std::vector<shape::dynamic_dimension> dyn_dims = {};
             if(axes.empty())
             {
-                for(auto i : range(input_shape.ndim()))
-                {
-                    auto dd = input_shape.dyn_dims()[i];
-                    if(dd != one_dyn_dim)
-                    {
-                        dyn_dims.push_back(dd);
-                    }
-                }
+                std::copy_if(input_shape.dyn_dims().cbegin(),
+                             input_shape.dyn_dims().cend(),
+                             std::back_inserter(dyn_dims),
+                             [&](auto dd) { return dd != 1; });
             }
             else
             {

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