manual merge

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)
     {
@@ -57,59 +60,120 @@ 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"; }
 
     void check_attribute_size() const
     {
-        if(not((padding.size() == stride.size() or (padding.size() / 2) == stride.size()) and
-               stride.size() == lengths.size()))
+        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
+           (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(not(input_size == padding_size / 2 + 2 or 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/quant_convolution.hpp

@@ -41,9 +41,8 @@ struct quant_convolution
     std::vector<std::size_t> stride   = {1, 1};
     std::vector<std::size_t> dilation = {1, 1};
 
-    padding_mode_t padding_mode    = default_;
-    int group                      = 1;
-    bool use_dynamic_same_auto_pad = false;
+    padding_mode_t padding_mode = default_;
+    int group                   = 1;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
@@ -52,8 +51,7 @@ struct quant_convolution
                     f(self.stride, "stride"),
                     f(self.dilation, "dilation"),
                     f(self.padding_mode, "padding_mode"),
-                    f(self.group, "group"),
-                    f(self.use_dynamic_same_auto_pad, "use_dynamic_same_auto_pad"));
+                    f(self.group, "group"));
     }
 
     value attributes() const
@@ -65,8 +63,8 @@ struct quant_convolution
 
     void check_attribute_size() const
     {
-        if(not((padding.size() == stride.size() or (padding.size() / 2) == stride.size()) and
-               stride.size() == dilation.size()))
+        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
+           stride.size() != dilation.size())
         {
             MIGRAPHX_THROW("QUANT_CONVOLUTION: inconsistent attribute sizes");
         }

src/include/migraphx/op/reduce_op.hpp

@@ -26,6 +26,7 @@
 
 #include <migraphx/op/name.hpp>
 #include <migraphx/check_shapes.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/tensor_view.hpp>
 #include <migraphx/shape_for_each.hpp>
@@ -105,18 +106,41 @@ struct reduce_op : op_name<Derived>
         return tuned_axes;
     }
 
+    /**
+     * @brief returns a shape in which the axis or axes named
+     * for reduction by this op are set, to size 1.
+     *
+     * @param inputs list of input shapes
+     * @return shape
+     */
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        auto s          = inputs.at(0);
-        auto lens       = s.lens();
-        auto tuned_axes = tune_axes(lens.size());
-        for(auto axis : tuned_axes)
+        check_shapes{inputs, *this, true}.has(1);
+        auto s = inputs.at(0);
+        if(s.dynamic())
         {
-            lens[axis] = 1;
+            auto output_dyn_dims = s.dyn_dims();
+            auto tuned_axes      = tune_axes(output_dyn_dims.size());
+            for(const auto& axis : tuned_axes)
+            {
+                // At the time of writing, there's no functional difference between
+                // optimum of 0 (no opt) or 1.
+                output_dyn_dims[axis] = {1, 1, 0};
+            }
+
+            return shape{s.type(), output_dyn_dims};
+        }
+        else
+        {
+            auto lens       = s.lens();
+            auto tuned_axes = tune_axes(lens.size());
+            for(const auto& axis : tuned_axes)
+            {
+                lens[axis] = 1;
+            }
+
+            return inputs[0].with_lens(lens);
         }
-
-        return inputs[0].with_lens(lens);
     }
 
     template <class T>
@@ -124,7 +148,7 @@ struct reduce_op : op_name<Derived>
                    const std::vector<T>& in_lens,
                    std::vector<T>& out_lens) const
     {
-        for(auto axis : tuned_axes)
+        for(const auto& axis : tuned_axes)
         {
             out_lens[axis] = in_lens[axis];
         }
@@ -151,17 +175,17 @@ struct reduce_op : op_name<Derived>
             static_cast<const Derived&>(*this).output(batch_shape)(val);
     }
 
-    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 arg_lens   = args.front().get_shape().lens();
         auto tuned_axes = tune_axes(arg_lens.size());
-        std::vector<std::size_t> batch_lens(output_shape.lens().size(), 1);
+        std::vector<std::size_t> batch_lens(dyn_out.computed_shape.lens().size(), 1);
         tune_dims(tuned_axes, arg_lens, batch_lens);
-        shape batch_shape{output_shape.type(), batch_lens};
+        shape batch_shape{dyn_out.computed_shape.type(), batch_lens};
         visit_all(result, args[0])([&](auto output, auto input) {
-            par_for(output_shape.elements(), [&](auto i) {
-                auto out_idx = output_shape.multi(i);
+            par_for(dyn_out.computed_shape.elements(), [&](auto i) {
+                auto out_idx = dyn_out.computed_shape.multi(i);
                 this->reduce(input, batch_shape, tuned_axes, out_idx, output);
             });
         });

src/include/migraphx/op/reshape.hpp

@@ -28,6 +28,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -46,14 +47,60 @@ struct reshape
     value attributes() const { return {{"require_std_shape", true}}; }
 
     std::string name() const { return "reshape"; }
-    shape compute_shape(std::vector<shape> inputs) const
+
+    shape dyn_compute_shape(shape s0) const
+    {
+        auto dyn_dims      = s0.dyn_dims();
+        auto num_not_fixed = std::count_if(
+            dyn_dims.cbegin(), dyn_dims.cend(), [](auto dd) { return not dd.is_fixed(); });
+        if(num_not_fixed != 1)
+        {
+            MIGRAPHX_THROW("Reshape: Only supports one non-fixed dynamic_dimension");
+        }
+        // track number of fixed elements in input and output
+        std::size_t num_dims_ele = 1;
+        std::size_t num_dd_ele   = 1;
+        for(std::size_t i = 0; i < dyn_dims.size(); ++i)
+        {
+            if(dyn_dims[i].is_fixed())
+            {
+                num_dims_ele *= dims[i];
+                num_dd_ele *= dyn_dims[i].min;
+            }
+            else
+            {
+                if(dims[i] != 0 and dims[i] != -1)
+                {
+                    MIGRAPHX_THROW(
+                        "Reshape: Non-fixed dynamic_dimension doesn't match with 0 or -1 "
+                        "output dimension");
+                }
+            }
+        }
+        if(num_dims_ele != num_dd_ele)
+        {
+            MIGRAPHX_THROW("Reshape: Number of fixed elements must match. Input: " +
+                           std::to_string(num_dd_ele) + " Output: " + std::to_string(num_dims_ele));
+        }
+        // construct output dynamic shape from dims attribute
+        std::vector<shape::dynamic_dimension> output_dyn_dims(dims.size());
+        std::transform(dims.cbegin(),
+                       dims.cend(),
+                       dyn_dims.cbegin(),
+                       output_dyn_dims.begin(),
+                       [](std::size_t dim, auto dyn_dim) {
+                           if(not dyn_dim.is_fixed())
+                               return dyn_dim;
+                           return shape::dynamic_dimension{dim, dim};
+                       });
+        return {s0.type(), output_dyn_dims};
+    }
+
+    shape static_compute_shape(std::vector<shape> inputs, std::size_t n_neg_dims) const
     {
-        check_shapes{inputs, *this}.has(1).standard();
+        check_shapes{inputs, *this}.standard();
         auto&& idims = inputs.front().lens();
         std::vector<std::size_t> rdims(dims.begin(), dims.end());
-        auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
-        if(n_neg_dims > 1)
-            MIGRAPHX_THROW("Reshape: Dimensions for reshape can only have one -1 dim");
 
         for(std::size_t i = 0; i < dims.size(); i++)
         {
@@ -86,9 +133,26 @@ struct reshape
         return s;
     }
 
-    argument compute(shape output_shape, std::vector<argument> args) const
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this, true}.has(1);
+        auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
+        if(n_neg_dims > 1)
+            MIGRAPHX_THROW("Reshape: Dimensions for reshape can only have one -1 dim");
+        auto s0 = inputs[0];
+        if(s0.dynamic())
+        {
+            return dyn_compute_shape(s0);
+        }
+        else
+        {
+            return static_compute_shape(inputs, n_neg_dims);
+        }
+    }
+
+    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/softmax.hpp

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

src/include/migraphx/op/squeeze.hpp

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

src/include/migraphx/op/transpose.hpp

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

src/include/migraphx/op/unary.hpp

@@ -30,6 +30,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/value.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -62,9 +63,9 @@ struct unary : op_name<Derived>
     value attributes() const { return base_attributes(); }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, static_cast<const Derived&>(*this)}.has(1);
+        check_shapes{inputs, static_cast<const Derived&>(*this), true}.has(1);
         auto s = inputs.at(0);
-        if(s.scalar())
+        if(s.dynamic() or s.scalar())
         {
             return s;
         }
@@ -78,9 +79,9 @@ struct unary : op_name<Derived>
         }
     }
 
-    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};
         result.visit([&](auto output) {
             args[0].visit([&](auto input) {
                 std::transform(input.begin(),

src/include/migraphx/op/unsqueeze.hpp

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

src/include/migraphx/operation.hpp

@@ -32,6 +32,8 @@
 #include <utility>
 #include <unordered_map>
 #include <migraphx/reflect.hpp>
+#include <migraphx/dyn_output.hpp>
+#include <migraphx/functional.hpp>
 #include <migraphx/streamutils.hpp>
 #include <migraphx/normalize_attributes.hpp>
 #include <migraphx/argument.hpp>
@@ -199,9 +201,12 @@ auto compute_op(rank<1>,
                 context& ctx,
                 const shape& output_shape,
                 const std::vector<argument>& input)
-    -> decltype(x.compute(auto_any_cast(ctx), output_shape, input))
+    -> decltype(x.compute(auto_any_cast(ctx),
+                          make_compute_output_shape(pack(x, output_shape, input)),
+                          input))
 {
-    return x.compute(auto_any_cast(ctx), output_shape, input);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -220,9 +225,9 @@ compute_op(const T& x, context& ctx, const shape& output_shape, const std::vecto
 
 template <class T>
 auto compute_op(rank<1>, const T& x, const shape& output_shape, const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input))
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input))
 {
-    return x.compute(output_shape, input);
+    return x.compute(make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -244,9 +249,11 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -278,9 +285,17 @@ auto compute_op(rank<4>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(auto_any_cast(ctx), output, inputs, module_args, f))
+                F f) -> decltype(x.compute(auto_any_cast(ctx),
+                                           make_compute_output_shape(pack(x, output, inputs)),
+                                           inputs,
+                                           module_args,
+                                           f))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs, module_args, f);
+    return x.compute(auto_any_cast(ctx),
+                     make_compute_output_shape(pack(x, output, inputs)),
+                     inputs,
+                     module_args,
+                     f);
 }
 
 template <class T, class F>
@@ -290,9 +305,11 @@ auto compute_op(rank<3>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -302,9 +319,10 @@ auto compute_op(rank<2>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(output, inputs))
+                F)
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs))
 {
-    return x.compute(output, inputs);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -314,9 +332,12 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(auto_any_cast(ctx), output, inputs))
+                F) -> decltype(x.compute(auto_any_cast(ctx),
+                                         make_compute_output_shape(pack(x, output, inputs)),
+                                         inputs))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -348,7 +369,8 @@ auto is_context_free_op(rank<1>,
                         const T& x,
                         const shape& output_shape,
                         const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input), std::true_type{});
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input),
+                std::true_type{});
 
 template <class T>
 auto is_context_free_op(rank<0>, const T&, const shape&, const std::vector<argument>&)

src/include/migraphx/operators.hpp

@@ -35,7 +35,6 @@
 #include <migraphx/op/as_shape.hpp>
 #include <migraphx/op/atan.hpp>
 #include <migraphx/op/atanh.hpp>
-#include <migraphx/op/batch_norm_inference.hpp>
 #include <migraphx/op/binary.hpp>
 #include <migraphx/op/broadcast.hpp>
 #include <migraphx/op/capture.hpp>

src/include/migraphx/pad_calc.hpp

@@ -24,9 +24,10 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
 #define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
 
-#include <migraphx/config.hpp>
 #include <cstdint>
 #include <vector>
+#include <migraphx/config.hpp>
+#include <migraphx/shape.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -42,18 +43,21 @@ void calculate_padding(int64_t idx,
 /*!
  * 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);
+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);
+
+// Used for dynamic auto padding of convolution operators since padding needs to be computed at
+// evaulation time.
+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);
 
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/include/migraphx/program.hpp

@@ -37,6 +37,7 @@
 #include <migraphx/assignment_options.hpp>
 #include <migraphx/env.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/execution_environment.hpp>
 #include <algorithm>
 #include <iostream>
 
@@ -76,8 +77,8 @@ struct program
 
     std::unordered_map<std::string, shape> get_parameter_shapes() const;
 
-    std::vector<argument> eval(parameter_map params) const;
-
+    std::vector<argument> eval(parameter_map params,
+                               execution_environment exec_env = execution_environment{}) const;
     std::size_t size() const;
 
     std::vector<shape> get_output_shapes() const;
@@ -114,6 +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/reflect.hpp

@@ -56,11 +56,11 @@ auto reflect_impl(rank<0>, T&, Selector)
 }
 
 template <class T>
-auto reflectable_impl(rank<1>, T&& x)
+auto reflectable_impl(rank<1>, const T& x)
     -> decltype(T::reflect(x, reflect_placeholder{}), std::true_type{});
 
 template <class T>
-auto reflectable_impl(rank<0>, T &&) -> decltype(std::false_type{});
+auto reflectable_impl(rank<0>, const T&) -> decltype(std::false_type{});
 
 template <class T>
 struct remove_rvalue_reference
@@ -111,8 +111,18 @@ auto reflect(T& x, Selector f)
 template <class T>
 auto reflect_tie(T& x)
 {
-    return reflect(x, [](auto&& y, auto&&...) { return detail::wrap<decltype(y)>(y); })(
-        [](auto&&... xs) { return detail::auto_tuple(xs.get()...); });
+    return reflect(x, [](auto&& y, auto&&...) {
+        // cppcheck-suppress UnnecessaryElseStatement
+        if constexpr(is_reflectable<decltype(y)>{})
+        {
+            auto t = reflect_tie(y);
+            return detail::wrap<decltype(t)>(t);
+        }
+        else
+        {
+            return detail::wrap<decltype(y)>(y);
+        }
+    })([](auto&&... xs) { return detail::auto_tuple(xs.get()...); });
 }
 
 template <class T, class F>

src/include/migraphx/shape.hpp

@@ -30,6 +30,7 @@
 #include <numeric>
 #include <memory>
 
+#include <migraphx/functional.hpp>
 #include <migraphx/errors.hpp>
 #include <migraphx/half.hpp>
 #include <migraphx/config.hpp>
@@ -89,7 +90,10 @@ struct shape
         std::size_t opt = 0;
 
         template <class Self, class F>
-        static auto reflect(Self& self, F f);
+        static auto reflect(Self& self, F f)
+        {
+            return pack(f(self.min, "min"), f(self.max, "max"), f(self.opt, "opt"));
+        }
 
         bool is_fixed() const;
         bool has_optimal() const;
@@ -97,6 +101,19 @@ 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);
+
+        // add and subtract fixed std::size_t dimension
+        dynamic_dimension& operator+=(const std::size_t& x);
+        dynamic_dimension& operator-=(const std::size_t& x);
+        friend dynamic_dimension operator+(const dynamic_dimension& x, const std::size_t& y);
+        friend dynamic_dimension operator+(const std::size_t& x, const dynamic_dimension& y);
+        friend dynamic_dimension operator-(const dynamic_dimension& x, const std::size_t& y);
     };
 
     static const std::vector<type_t>& types();
@@ -115,6 +132,12 @@ struct shape
 
     shape(type_t t, std::vector<dynamic_dimension> dims);
 
+    // Construct a dynamic shape from three sets of lengths (of the same rank)
+    shape(type_t t,
+          std::vector<std::size_t> mins,
+          std::vector<std::size_t> maxes,
+          std::vector<std::size_t> opts);
+
     template <class Range>
     shape(type_t t, const Range& l) : shape(t, std::vector<std::size_t>(l.begin(), l.end()))
     {
@@ -136,6 +159,12 @@ struct shape
     const std::vector<std::size_t>& lens() const;
     const std::vector<std::size_t>& strides() const;
 
+    /*!
+     * The number of dimensions in the shape.
+     * Same as the number of indices required to get a data value.
+     */
+    std::size_t ndim() const;
+
     /*!
      * Return the number of elements in the tensor.
      */
@@ -221,6 +250,9 @@ struct shape
 
     shape with_type(type_t t) const;
 
+    // convert the shape to an equivalent dynamic shape
+    shape to_dynamic() const;
+
     friend bool operator==(const shape& x, const shape& y);
     friend bool operator!=(const shape& x, const shape& y);
     friend std::ostream& operator<<(std::ostream& os, const shape& x);

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/include/migraphx/streamutils.hpp

@@ -26,8 +26,11 @@
 
 #include <ostream>
 #include <algorithm>
+#include <migraphx/reflect.hpp>
 #include <migraphx/rank.hpp>
+#include <migraphx/requires.hpp>
 #include <migraphx/config.hpp>
+#include <vector>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -59,10 +62,22 @@ inline stream_range_container<Range> stream_range(const Range& r)
 
 namespace detail {
 
-inline void stream_write_value_impl(rank<2>, std::ostream& os, const std::string& x) { os << x; }
+template <class T>
+auto stream_write_value_impl(rank<1>, std::ostream& os, const T& x) -> decltype(os << x, void())
+{
+    os << x;
+}
+
+template <class T>
+void stream_write_value_impl(rank<1>, std::ostream& os, const std::vector<T>& r)
+{
+    os << "{";
+    os << stream_range(r);
+    os << "}";
+}
 
 template <class Range>
-auto stream_write_value_impl(rank<1>, std::ostream& os, const Range& r)
+auto stream_write_value_impl(rank<0>, std::ostream& os, const Range& r)
     -> decltype(r.begin(), r.end(), void())
 {
     os << "{";
@@ -70,17 +85,26 @@ auto stream_write_value_impl(rank<1>, std::ostream& os, const Range& r)
     os << "}";
 }
 
-template <class T>
+template <class T, MIGRAPHX_REQUIRES(is_reflectable<T>{})>
 void stream_write_value_impl(rank<0>, std::ostream& os, const T& x)
 {
-    os << 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>
 void stream_write_value(std::ostream& os, const T& x)
 {
-    detail::stream_write_value_impl(rank<2>{}, os, x);
+    detail::stream_write_value_impl(rank<1>{}, os, x);
 }
 
 } // namespace MIGRAPHX_INLINE_NS

src/include/migraphx/value.hpp

@@ -184,6 +184,12 @@ struct value
         {
         }
         explicit binary(std::size_t s) : base(s) {}
+
+        friend std::ostream& operator<<(std::ostream& os, const binary& obj)
+        {
+            os << "{binary_object: " << obj.size() << "}";
+            return os;
+        }
     };
 
     value() = default;

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")