Merge branch 'develop' into blas_tuning

src/include/migraphx/common.hpp

@@ -41,6 +41,11 @@ std::vector<shape::dynamic_dimension> compute_broadcasted_dyn_dims(shape s0, sha
 
 shape common_shape(const std::vector<shape>& shapes);
 
+std::vector<instruction_ref>
+insert_common_args(module& m, instruction_ref ins, std::vector<instruction_ref> inputs);
+
+std::vector<instruction_ref> add_common_args(module& m, std::vector<instruction_ref> inputs);
+
 instruction_ref insert_common_op(module& m,
                                  instruction_ref ins,
                                  const operation& op,

src/include/migraphx/convolution.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_RTGLIB_CONVOLUTION_HPP
+#define MIGRAPHX_GUARD_RTGLIB_CONVOLUTION_HPP
+
+#include <migraphx/config.hpp>
+#include <migraphx/dfor.hpp>
+#include <migraphx/par_for.hpp>
+#include <migraphx/shape_for_each.hpp>
+#include <migraphx/tensor_view.hpp>
+#include <vector>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+template <class Output, class T, class Padding, class Stride>
+void convolution(Output output, T input, T weights, Padding padding, Stride stride, int group)
+{
+    auto output_shape = output.get_shape();
+    auto in_lens      = input.get_shape().lens();
+
+    auto wei_lens = weights.get_shape().lens();
+    auto wei_n    = wei_lens[0];
+    auto wei_c    = wei_lens[1];
+    std::vector<std::size_t> win_size(wei_lens.begin() + 1, wei_lens.end());
+
+    par_for(output_shape.elements(), [&](auto i) {
+        auto idx_o = output_shape.multi(i);
+        auto w     = idx_o[1];
+        auto n_dim = idx_o.size();
+
+        std::vector<std::ptrdiff_t> win_start;
+        for(std::size_t dim = 2; dim < n_dim; ++dim)
+        {
+            auto d_2 = dim - 2;
+            win_start.push_back(std::ptrdiff_t(idx_o[dim] * stride[d_2]) -
+                                std::ptrdiff_t(padding[d_2]));
+        }
+        const auto group_id = w / (wei_n / group);
+
+        shape win_shape{output_shape.type(), win_size};
+
+        double acc = 0.0;
+        shape_for_each(win_shape, [&](auto idx_win) {
+            auto k           = idx_win[0];
+            const auto in_ch = group_id * wei_c + k;
+            std::vector<std::ptrdiff_t> idx(idx_o.begin(), idx_o.end());
+            idx[1] = in_ch;
+            std::transform(idx_win.begin() + 1,
+                           idx_win.end(),
+                           win_start.begin(),
+                           idx.begin() + 2,
+                           [](std::ptrdiff_t ii, std::ptrdiff_t jj) { return ii + jj; });
+            std::vector<std::ptrdiff_t> idx_wei(idx_o.size());
+            idx_wei[0] = w;
+            std::copy(idx_win.begin(), idx_win.end(), idx_wei.begin() + 1);
+            if(std::all_of(idx.begin() + 2, idx.end(), [&](auto ii) { return ii >= 0; }) and
+               std::equal(idx.begin(),
+                          idx.end(),
+                          in_lens.begin(),
+                          in_lens.end(),
+                          std::less<std::ptrdiff_t>{}))
+            {
+                acc += input(idx.begin(), idx.end()) * weights(idx_wei.begin(), idx_wei.end());
+            }
+        });
+
+        output[i] = acc;
+    });
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/cpp_generator.hpp

@@ -77,6 +77,7 @@ struct cpp_generator
         function& set_types(const module& m);
         function& set_types(const module& m, const std::function<std::string(shape)>& parse);
         function& set_generic_types(const module& m);
+        function& add_generic_param(const std::string& pname);
     };
 
     cpp_generator();
@@ -105,6 +106,10 @@ struct cpp_generator
 
     std::string create_function(const function& f);
 
+    static std::vector<std::string>
+    to_args(const std::vector<instruction_ref>& inputs,
+            const std::unordered_map<instruction_ref, std::string>& names);
+
     private:
     std::unique_ptr<cpp_generator_impl> impl;
 };

src/include/migraphx/fuse_reduce.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_FUSE_REDUCE_HPP
+#define MIGRAPHX_GUARD_MIGRAPHX_FUSE_REDUCE_HPP
+
+#include <migraphx/config.hpp>
+#include <string>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct module_pass_manager;
+
+struct fuse_reduce
+{
+    std::string name() const { return "fuse_reduce"; }
+    void apply(module_pass_manager& mpm) const;
+};
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_MIGRAPHX_FUSE_POINTWISE_HPP

src/include/migraphx/matcher.hpp

@@ -347,6 +347,7 @@ match::matcher_result find_match(module& modl, M&& m)
 }
 
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_MATCHES)
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_VALIDATE_MATCHES)
 
 /// Find matches for an instruction in the module
 template <class Mod, class... Ms>
@@ -356,7 +357,11 @@ void find_matches(Mod& mod, instruction_ref ins, Ms&&... ms)
     const
 #endif
         int trace = value_of(MIGRAPHX_TRACE_MATCHES{});
-    bool match    = false;
+#if !defined(__GNUC__) || defined(__clang__) || __GNUC__ > 5
+    const
+#endif
+        bool validate = enabled(MIGRAPHX_VALIDATE_MATCHES{});
+    bool match        = false;
     each_args(
         [&](auto&& m) {
             if(match)
@@ -371,7 +376,20 @@ void find_matches(Mod& mod, instruction_ref ins, Ms&&... ms)
                 std::cout << "Matched by " << get_type_name(m) << std::endl;
                 get_module(mod).debug_print(ins);
             }
+            // If its already invalid dont validate it again
+            bool invalidated = validate and get_module(mod).validate() != get_module(mod).end();
             m.apply(mod, r);
+            if(validate and not invalidated)
+            {
+                auto invalid = get_module(mod).validate();
+                if(invalid != get_module(mod).end())
+                {
+                    std::cout << "Invalid program from match: " << get_type_name(m) << std::endl;
+                    std::cout << "Invalid instructions: " << std::endl;
+                    get_module(mod).debug_print(invalid->inputs());
+                    get_module(mod).debug_print(invalid);
+                }
+            }
             match = true;
         },
         ms...);
@@ -520,6 +538,8 @@ MIGRAPHX_PRED_MATCHER(not_standard_shape, instruction_ref ins)
 {
     return not ins->get_shape().standard();
 }
+MIGRAPHX_PRED_MATCHER(dynamic_shape, instruction_ref ins) { return ins->get_shape().dynamic(); }
+MIGRAPHX_PRED_MATCHER(static_shape, instruction_ref ins) { return not ins->get_shape().dynamic(); }
 MIGRAPHX_PRED_MATCHER(broadcast_shape, instruction_ref ins)
 {
     return ins->get_shape().broadcasted();

src/include/migraphx/module.hpp

@@ -178,6 +178,8 @@ struct module
 
     bool has_instruction(instruction_ref ins) const;
 
+    std::vector<instruction_ref> get_returns() const;
+
     std::size_t size() const;
     instruction_ref begin() const;
     instruction_ref end() const;

src/include/migraphx/msgpack.hpp

@@ -26,6 +26,7 @@
 
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
+#include <functional>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/include/migraphx/op/convolution.hpp

@@ -24,9 +24,12 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_HPP
 #define MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_HPP
 
+#include <migraphx/argument.hpp>
 #include <migraphx/op/common.hpp>
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/convolution.hpp>
+#include <migraphx/pad_calc.hpp>
 #include <migraphx/value.hpp>
 #include <cmath>
 #include <utility>
@@ -210,6 +213,37 @@ struct convolution
         check_attribute_size();
         return stride.size();
     }
+
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        std::vector<std::size_t> new_padding;
+        if(padding_mode != op::padding_mode_t::default_)
+        {
+            auto input_lens   = args[0].get_shape().lens();
+            auto weights_lens = args[1].get_shape().lens();
+            new_padding =
+                padding_mode == op::same_upper
+                    ? calc_dyn_auto_pad(input_lens, weights_lens, stride, dilation, true)
+                    : calc_dyn_auto_pad(input_lens, weights_lens, stride, dilation, false);
+            output_shape = compute_padded_shape(
+                args[0].get_shape(), args[1].get_shape(), new_padding, stride, dilation);
+        }
+        else
+        {
+            new_padding = padding;
+            if(output_shape.dynamic())
+            {
+                output_shape =
+                    normalize_compute_shape({args.at(0).get_shape(), args.at(1).get_shape()});
+            }
+        }
+
+        argument result{output_shape};
+        visit_all(result, args[0], args[1])([&](auto output, auto input, auto weights) {
+            migraphx::convolution(output, input, weights, new_padding, stride, group);
+        });
+        return result;
+    }
 };
 
 } // namespace op

src/include/migraphx/op/dequantizelinear.hpp

@@ -40,7 +40,11 @@ struct dequantizelinear
     std::string name() const { return "dequantizelinear"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.same_dims();
+        check_shapes{inputs, *this}.same_dims().has(2, 3);
+        if(inputs.size() == 3 and inputs[0].type() != inputs[2].type())
+        {
+            MIGRAPHX_THROW("DEQUANTIZELINEAR: Zero point and input should be the same type.");
+        }
         return {inputs[1].type(), inputs[0].lens(), inputs[0].strides()};
     }
 

src/include/migraphx/op/pointwise.hpp

@@ -45,14 +45,15 @@ struct pointwise
         {
             MIGRAPHX_THROW("should have one submodule.");
         }
-        auto* pm    = mods.front();
+        auto* pm = mods.front();
+        if(pm->get_output_shapes().size() != 1)
+            MIGRAPHX_THROW("pointwise should have only one output.");
+        if(inputs.empty())
+            MIGRAPHX_THROW("pointwise should have at least one input");
         auto pnames = pm->get_parameter_names();
         std::sort(pnames.begin(), pnames.end());
         check_shapes{inputs, *this}.has(pnames.size()).same_dims();
 
-        if(pm->get_output_shapes().size() != 1)
-            MIGRAPHX_THROW("submodule should have only one output.");
-
         auto type = pm->get_output_shapes().front().type();
 
         // Scalar output if all inputs are scalar

src/include/migraphx/op/quant_convolution.hpp

@@ -25,8 +25,10 @@
 #define MIGRAPHX_GUARD_OPERATORS_QUANT_CONVOLUTION_HPP
 
 #include <migraphx/op/common.hpp>
+#include <migraphx/argument.hpp>
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/convolution.hpp>
 #include <migraphx/value.hpp>
 #include <cmath>
 #include <utility>
@@ -114,6 +116,17 @@ struct quant_convolution
         check_attribute_size();
         return stride.size();
     }
+
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        argument result{output_shape};
+        result.visit([&](auto output) {
+            visit_all(args[0], args[1])([&](auto input, auto weights) {
+                migraphx::convolution(output, input, weights, padding, stride, group);
+            });
+        });
+        return result;
+    }
 };
 
 } // namespace op

src/include/migraphx/op/quantizelinear.hpp

@@ -40,7 +40,11 @@ struct quantizelinear
     std::string name() const { return "quantizelinear"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.same_dims();
+        check_shapes{inputs, *this}.same_dims().has(2, 3);
+        if(inputs[0].type() != inputs[1].type())
+        {
+            MIGRAPHX_THROW("QUANTIZELINEAR: Scales and input must be the same type");
+        }
         if(inputs.size() == 3)
         {
             return {inputs[2].type(), inputs[0].lens(), inputs[0].strides()};
@@ -61,17 +65,15 @@ struct quantizelinear
 
         argument result{output_shape};
         visit_all(result, y_zero_point)([&](auto output, auto zero_pts) {
-            x.visit([&](auto input) {
-                y_scale.visit([&](auto scales) {
-                    using quant_type = typename decltype(output)::value_type;
-                    auto min_value   = std::numeric_limits<quant_type>::min();
-                    auto max_value   = std::numeric_limits<quant_type>::max();
-                    par_for(output_shape.elements(), [&](auto i) {
-                        int64_t quantized = static_cast<int64_t>(std::round(input[i] / scales[i])) +
-                                            static_cast<int64_t>(zero_pts[i]);
-                        output[i] = std::max(static_cast<int64_t>(min_value),
-                                             std::min(static_cast<int64_t>(max_value), quantized));
-                    });
+            visit_all(x, y_scale)([&](auto input, auto scales) {
+                using quant_type = typename decltype(output)::value_type;
+                auto min_value   = std::numeric_limits<quant_type>::min();
+                auto max_value   = std::numeric_limits<quant_type>::max();
+                par_for(output_shape.elements(), [&](auto i) {
+                    int64_t quantized = static_cast<int64_t>(std::round(input[i] / scales[i])) +
+                                        static_cast<int64_t>(zero_pts[i]);
+                    output[i] = std::max(static_cast<int64_t>(min_value),
+                                         std::min(static_cast<int64_t>(max_value), quantized));
                 });
             });
         });

src/include/migraphx/op/reduce_op.hpp

@@ -91,7 +91,7 @@ struct reduce_op : op_name<Derived>
     {
         value normalize;
         normalize["axes"] = value::array{normalize_attribute::include_min};
-        return {{"normalize_axes", normalize}};
+        return {{"normalize_axes", normalize}, {"reduce", true}};
     }
 
     std::vector<int64_t> tune_axes(std::size_t n_dim) const

src/include/migraphx/op/select_module.hpp

@@ -57,6 +57,7 @@ struct select_module
                      param_names.cend(),
                      std::back_inserter(ret),
                      [](auto pn) { return not contains(pn, "#output_"); });
+        std::sort(ret.begin(), ret.end());
         return ret;
     }
 
@@ -68,6 +69,8 @@ struct select_module
                      param_names.cend(),
                      std::back_inserter(ret),
                      [](auto pn) { return contains(pn, "#output_"); });
+        // needs to be sorted to ensure output parameter ordering
+        std::sort(ret.begin(), ret.end());
         return ret;
     }
 
@@ -111,6 +114,7 @@ struct select_module
 
         // One tuple output parameter in main module to multiple output parameters in submodule
         auto out_param_names    = get_output_parameter_names(module_to_run);
+        auto param_shapes       = module_to_run->get_parameter_shapes();
         auto output_sub_objects = args.back().get_sub_objects();
         assert(out_param_names.size() == output_sub_objects.size());
         std::transform(out_param_names.begin(),
@@ -118,7 +122,7 @@ struct select_module
                        output_sub_objects.begin(),
                        std::inserter(p_map, p_map.end()),
                        [&](auto&& name, auto&& a) {
-                           auto ps = module_to_run->get_parameter_shape(name);
+                           auto ps = param_shapes.at(name);
                            if(a.get_shape() != ps)
                            {
                                assert(ps.bytes() == a.get_shape().bytes());

src/include/migraphx/shape.hpp

@@ -189,19 +189,19 @@ struct shape
 
     /*!
      * Minimum lengths for dynamic shape.
-     * lens() for fixed shape.
+     * lens() for static shape.
      */
     std::vector<std::size_t> min_lens() const;
 
     /*!
      * Maximum lengths for dynamic shape.
-     * lens() for fixed shape.
+     * lens() for static shape.
      */
     std::vector<std::size_t> max_lens() const;
 
     /*!
      * Optimum lengths for dynamic shape.
-     * Empty for fixed shape.
+     * Empty for static shape.
      */
     std::vector<std::set<std::size_t>> opt_lens() const;
 
@@ -259,6 +259,9 @@ struct shape
     // convert the shape to an equivalent dynamic shape with empty optimals
     shape to_dynamic() const;
 
+    // convert the shape to a static one setting any non-fixed dynamic_dimensions to x
+    shape to_static(std::size_t x) 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/module.cpp

@@ -595,6 +595,14 @@ std::vector<shape> module::get_output_shapes() const
     }
 }
 
+std::vector<instruction_ref> module::get_returns() const
+{
+    auto last = std::prev(this->end());
+    if(last->name() == "@return")
+        return last->inputs();
+    return {last};
+}
+
 instruction_ref module::validate() const
 {
     return std::find_if(

src/onnx/onnx_parser.cpp

@@ -39,6 +39,20 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
+static shape shape_from_dyn_dims(shape::type_t shape_type,
+                                 const std::vector<shape::dynamic_dimension>& dyn_dims)
+{
+    if(std::all_of(dyn_dims.begin(), dyn_dims.end(), [](auto dd) { return dd.is_fixed(); }))
+    {
+        std::vector<std::size_t> dims;
+        std::transform(dyn_dims.cbegin(), dyn_dims.cend(), std::back_inserter(dims), [](auto d) {
+            return d.max;
+        });
+        return {shape_type, dims};
+    }
+    return {shape_type, dyn_dims};
+}
+
 namespace onnx {
 
 static onnx_parser::attribute_map get_attributes(const onnx::NodeProto& node)
@@ -300,7 +314,7 @@ onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph, bool inlini
             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)};
+                s = shape_from_dyn_dims(shape_type, map_dyn_input_dims.at(name));
             }
             else
             {
@@ -503,16 +517,7 @@ shape onnx_parser::parse_type(const onnx::TypeProto& t,
     {
         return {shape_type};
     }
-    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};
+    return shape_from_dyn_dims(shape_type, dynamic_dims);
 }
 
 shape::type_t get_type(int dtype)

src/onnx/parse_quantizelinear.cpp

@@ -26,6 +26,7 @@
 #include <migraphx/ranges.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/tune_axis.hpp>
+#include <migraphx/common.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -47,18 +48,15 @@ struct parse_quantizelinear : op_parser<parse_quantizelinear>
         auto input_lens = args[0]->get_shape().lens();
         auto n_dim      = input_lens.size();
 
-        instruction_ref y_scale;
+        instruction_ref y_scale = args[1];
         if(args[1]->get_shape().elements() != 1)
         {
             auto tuned_axis = tune_axis(n_dim, axis, opd.op_name);
             y_scale         = info.add_instruction(
                 make_op("broadcast", {{"axis", tuned_axis}, {"out_lens", input_lens}}), args[1]);
         }
-        else
-        {
-            y_scale = info.add_instruction(make_op("multibroadcast", {{"out_lens", input_lens}}),
-                                           args[1]);
-        }
+
+        auto common_args = add_common_args(*info.mod, {args[0], y_scale});
 
         if(args.size() == 3)
         {
@@ -76,10 +74,10 @@ struct parse_quantizelinear : op_parser<parse_quantizelinear>
                     make_op("multibroadcast", {{"out_lens", input_lens}}), y_zero_point);
             }
 
-            return info.add_instruction(make_op("quantizelinear"), args[0], y_scale, y_zero_point);
+            common_args.push_back(y_zero_point);
         }
 
-        return info.add_instruction(make_op("quantizelinear"), args[0], y_scale);
+        return info.add_instruction(make_op("quantizelinear"), common_args);
     }
 };
 

src/pass_manager.cpp

@@ -103,6 +103,7 @@ struct module_pm : module_pass_manager
 
     virtual void run_pass(const pass& p) override
     {
+        trace("Pass: ", p.name());
         assert(mod);
         assert(mod->validate() == mod->end());
         if(enabled(MIGRAPHX_TIME_PASSES{}))

src/program.cpp

@@ -331,7 +331,8 @@ std::vector<argument> generic_eval(const module* mod,
                         MIGRAPHX_THROW("Parameter not found: " + param_name);
                     auto param = params[param_name];
                     // TODO: may want to check correct number of dimensions and/or was within bounds
-                    if(not ins->get_shape().dynamic() and param.get_shape() != ins->get_shape())
+                    if(not ins->get_shape().any_of_dynamic() and
+                       param.get_shape() != ins->get_shape())
                     {
                         MIGRAPHX_THROW("Incorrect shape {" + to_string(param.get_shape()) +
                                        "} for parameter: " + param_name +