Merge branch 'dyn_squeeze' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

src/include/migraphx/op/mod.hpp

@@ -24,17 +24,8 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_MOD_HPP
 #define MIGRAPHX_GUARD_OPERATORS_MOD_HPP
 
-#include <array>
 #include <migraphx/op/binary.hpp>
-#include <migraphx/check_shapes.hpp>
-#include <migraphx/stringutils.hpp>
-#include <migraphx/streamutils.hpp>
-#include <migraphx/literal.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/config.hpp>
 #include <cmath>
-#include <utility>
-#include <type_traits>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -47,9 +38,9 @@ struct mod : binary<mod>
     {
         auto a           = base_attributes();
         a["commutative"] = false;
+        a["point_op"]    = "${function:fmod}((${function:remainder}(${0}, ${1})) + ${1}, ${1})";
         return a;
     }
-    std::string point_function() const { return "mod"; }
     auto apply() const
     {
         return [](auto x, auto y) { return std::fmod((std::remainder(x, y)) + y, y); };

src/include/migraphx/op/multibroadcast.hpp

@@ -26,64 +26,105 @@
 
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
+#include <migraphx/dyn_output.hpp>
+#include <migraphx/common.hpp>
 #include <migraphx/config.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+/**
+ * Broadcast multiple dimensions between two tensors.
+ * Two versions of this operator: one input and two inputs.
+ * One input version uses output_lens attribute and broadcasts to it.
+ * Two inputs version broadcasts both inputs to the common shape at evaluation time.
+ */
 struct multibroadcast
 {
-    std::vector<std::size_t> output_lens;
+    std::vector<std::size_t> output_lens = {};
+
+    // optional attribute
+    std::vector<shape::dynamic_dimension> output_dyn_dims = {};
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(f(self.output_lens, "out_lens"));
+        return pack(f(self.output_lens, "out_lens"), f(self.output_dyn_dims, "out_dyn_dims"));
     }
 
     std::string name() const { return "multibroadcast"; }
 
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        auto t     = inputs.at(0).type();
-        auto input = inputs.at(0);
+        check_shapes{inputs, *this, true}.has(1, 2);
 
-        if(input.lens().empty())
-        {
-            MIGRAPHX_THROW("MULTIBROADCAST: inputs dimensions should be > 0");
-        }
+        auto t  = inputs.at(0).type();
+        auto s0 = inputs.at(0);
 
-        if(input.lens().size() > output_lens.size())
+        if(s0.max_lens().empty())
         {
-            MIGRAPHX_THROW("MULTIBROADCAST: inputs dimensions should <= output size");
+            MIGRAPHX_THROW("MULTIBROADCAST: input dimensions should be > 0");
         }
 
-        auto offset = output_lens.size() - input.lens().size();
-        for(std::ptrdiff_t i = input.lens().size() - 1; i >= 0; i--)
+        auto make_bcast_strides = [&](std::vector<std::size_t> bcast_lens, std::size_t offset) {
+            std::vector<size_t> bcast_strides(bcast_lens.size(), 0);
+            for(std::ptrdiff_t i = s0.lens().size() - 1; i >= 0; i--)
+            {
+                if(bcast_lens[i + offset] == s0.lens()[i])
+                {
+                    bcast_strides[i + offset] = s0.strides()[i];
+                }
+            }
+            return bcast_strides;
+        };
+
+        if(inputs.size() == 1)
         {
-            if(output_lens[i + offset] != input.lens()[i] and input.lens()[i] != 1)
+            if(s0.lens().size() > output_lens.size())
             {
-                MIGRAPHX_THROW("MULTIBROADCAST: input shape {" + to_string_range(input.lens()) +
-                               "} cannot be broadcasted to {" + to_string_range(output_lens) +
-                               "}!");
+                MIGRAPHX_THROW("MULTIBROADCAST: input dimensions should <= output size");
             }
-        }
 
-        std::vector<size_t> bcast_strides(output_lens.size(), 0);
-        for(std::ptrdiff_t i = input.lens().size() - 1; i >= 0; i--)
+            auto offset = output_lens.size() - s0.lens().size();
+            for(std::ptrdiff_t i = s0.lens().size() - 1; i >= 0; i--)
+            {
+                if(output_lens[i + offset] != s0.lens()[i] and s0.lens()[i] != 1)
+                {
+                    MIGRAPHX_THROW("MULTIBROADCAST: input shape {" + to_string_range(s0.lens()) +
+                                   "} cannot be broadcasted to {" + to_string_range(output_lens) +
+                                   "}!");
+                }
+            }
+
+            auto bcast_strides = make_bcast_strides(output_lens, offset);
+            return {t, output_lens, std::move(bcast_strides)};
+        }
+        else
         {
-            if(output_lens[i + offset] == input.lens()[i])
+            // two inputs
+            auto s1 = inputs.at(1);
+            if(s0.dynamic() or s1.dynamic())
             {
-                bcast_strides[i + offset] = input.strides()[i];
+                if(not output_dyn_dims.empty())
+                {
+                    return {t, output_dyn_dims};
+                }
+                return {t, compute_broadcasted_dyn_dims(s0, s1)};
+            }
+            else
+            {
+                auto bcast_lens    = compute_broadcasted_lens(s0.lens(), s1.lens());
+                auto offset        = bcast_lens.size() - s0.lens().size();
+                auto bcast_strides = make_bcast_strides(bcast_lens, offset);
+                return {t, std::move(bcast_lens), std::move(bcast_strides)};
             }
         }
-        return {t, output_lens, bcast_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/nonmaxsuppression.hpp

@@ -45,11 +45,13 @@ namespace op {
 struct nonmaxsuppression
 {
     bool center_point_box = false;
+    bool use_dyn_output   = false;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(f(self.center_point_box, "center_point_box"));
+        return pack(f(self.center_point_box, "center_point_box"),
+                    f(self.use_dyn_output, "use_dyn_output"));
     }
 
     std::string name() const { return "nonmaxsuppression"; }
@@ -57,27 +59,81 @@ struct nonmaxsuppression
     shape compute_shape(std::vector<shape> inputs) const
     {
         // requires at least 2 inputs
-        check_shapes{{inputs.at(0), inputs.at(1)}, *this}.only_dims(3);
-        auto lens = inputs.front().lens();
+        check_shapes{{inputs.at(0), inputs.at(1)}, *this, true}.only_dims(3).same_ndims();
+        auto boxes_max_lens = inputs.at(0).max_lens();
+        // num batches * num boxes
+        const auto max_num_boxes = boxes_max_lens.at(0) * boxes_max_lens.at(1);
 
-        // check input shape
-        if(lens[1] != inputs.at(1).lens()[2])
+        auto fixed_shape_error_check = [&]() {
+            auto lens = inputs.front().lens();
+            if(lens[1] != inputs.at(1).lens()[2])
+            {
+                MIGRAPHX_THROW(
+                    "NonMaxSuppression: spatial dimension mismatch between boxes and scores input");
+            }
+            if(lens[0] != inputs.at(1).lens()[0])
+            {
+                MIGRAPHX_THROW(
+                    "NonMaxSuppression: number of batches mismatch between boxes and scores input");
+            }
+        };
+
+        if(use_dyn_output)
         {
-            MIGRAPHX_THROW(
-                "NonMaxSuppression: spatial dimension mismatch between boxes and scores input");
+            if(inputs.at(0).dynamic())
+            {
+                // both boxes and scores should be dynamic
+                // check dynamic dimensions are consistent
+                const auto boxes_dims  = inputs.at(0).dyn_dims();
+                const auto scores_dims = inputs.at(1).dyn_dims();
+                if(boxes_dims.at(1) != scores_dims.at(2))
+                {
+                    MIGRAPHX_THROW("NonMaxSuppression: dynamic spatial dimension mismatch between "
+                                   "boxes and scores input");
+                }
+                if(boxes_dims.at(0) != scores_dims.at(0))
+                {
+                    MIGRAPHX_THROW("NonMaxSuppression: dynamic number of batches mismatch between "
+                                   "boxes and scores input");
+                }
+            }
+            else if(inputs.at(1).dynamic())
+            {
+                // scores has dynamic shape, boxes fixed shape
+                // check that it is only a dynamic number of classes
+                const auto scores_dims = inputs.at(1).dyn_dims();
+                const auto boxes_lens  = inputs.at(0).lens();
+                if(not scores_dims.at(0).is_fixed() or scores_dims.at(0).max != boxes_lens.at(0))
+                {
+                    MIGRAPHX_THROW("NonMaxSuppression: scores dynamic num_classes; num_batches not "
+                                   "fixed or mismatched");
+                }
+                if(not scores_dims.at(2).is_fixed() or scores_dims.at(2).max != boxes_lens.at(1))
+                {
+                    MIGRAPHX_THROW("NonMaxSuppression: scores dynamic num_classes; "
+                                   "spatial_dimension not fixed or mismatches");
+                }
+            }
+            else
+            {
+                fixed_shape_error_check();
+            }
+            std::vector<shape::dynamic_dimension> out_lens = {};
+            out_lens.push_back({0, max_num_boxes, 0});
+            out_lens.push_back({3, 3, 0});
+            return {shape::int64_type, out_lens};
         }
-
-        // check batch sizes
-        if(lens[0] != inputs.at(1).lens()[0])
+        else
         {
-            MIGRAPHX_THROW(
-                "NonMaxSuppression: number of batches mismatch between boxes and scores input");
+            if(inputs.at(0).dynamic() or inputs.at(1).dynamic())
+            {
+                MIGRAPHX_THROW(
+                    "NonMaxSuppression: dynamic input shape with use_dyn_output set to false");
+            }
+            fixed_shape_error_check();
+            std::vector<std::size_t> out_lens = {max_num_boxes, 3};
+            return {shape::int64_type, out_lens};
         }
-
-        std::vector<int64_t> out_lens(2);
-        out_lens.at(0) = lens.at(1);
-        out_lens.at(1) = 3;
-        return {shape::int64_type, out_lens};
     }
 
     struct box
@@ -181,13 +237,13 @@ struct nonmaxsuppression
     }
 
     template <class Output, class Boxes, class Scores>
-    void compute_nms(Output output,
-                     Boxes boxes,
-                     Scores scores,
-                     const shape& output_shape,
-                     std::size_t max_output_boxes_per_class,
-                     double iou_threshold,
-                     double score_threshold) const
+    std::size_t compute_nms(Output output,
+                            Boxes boxes,
+                            Scores scores,
+                            const shape& max_output_shape,
+                            std::size_t max_output_boxes_per_class,
+                            double iou_threshold,
+                            double score_threshold) const
     {
         std::fill(output.begin(), output.end(), 0);
         const auto& lens       = scores.get_shape().lens();
@@ -197,7 +253,7 @@ struct nonmaxsuppression
         // boxes of a class with NMS applied [score, index]
         std::vector<std::pair<double, int64_t>> selected_boxes_inside_class;
         std::vector<int64_t> selected_indices;
-        selected_boxes_inside_class.reserve(output_shape.elements());
+        selected_boxes_inside_class.reserve(max_output_shape.elements());
         // iterate over batches and classes
         shape comp_s{shape::double_type, {num_batches, num_classes}};
         shape_for_each(comp_s, [&](auto idx) {
@@ -210,7 +266,7 @@ struct nonmaxsuppression
             auto boxes_heap = filter_boxes_by_score(scores_start, num_boxes, score_threshold);
             selected_boxes_inside_class.clear();
             // Get the next box with top score, filter by iou_threshold
-            while(!boxes_heap.empty() &&
+            while(not boxes_heap.empty() &&
                   selected_boxes_inside_class.size() < max_output_boxes_per_class)
             {
                 // Check with existing selected boxes for this class, remove box if it
@@ -237,11 +293,14 @@ struct nonmaxsuppression
             }
         });
         std::copy(selected_indices.begin(), selected_indices.end(), output.begin());
+        return selected_indices.size() / 3;
     }
 
     argument compute(const shape& output_shape, std::vector<argument> args) const
     {
-        argument result{output_shape};
+        // make buffer of maximum size
+        shape max_output_shape = {output_shape.type(), output_shape.max_lens()};
+        argument result{max_output_shape};
 
         std::size_t max_output_boxes_per_class =
             (args.size() > 2) ? (args.at(2).at<std::size_t>()) : 0;
@@ -249,22 +308,29 @@ struct nonmaxsuppression
         {
             return result;
         }
-        double iou_threshold   = (args.size() > 3) ? (args.at(3).at<double>()) : 0.0f;
-        double score_threshold = (args.size() > 4) ? (args.at(4).at<double>()) : 0.0f;
+        double iou_threshold     = (args.size() > 3) ? (args.at(3).at<double>()) : 0.0f;
+        double score_threshold   = (args.size() > 4) ? (args.at(4).at<double>()) : 0.0f;
+        std::size_t num_selected = 0;
 
         result.visit([&](auto output) {
             visit_all(args[0], args[1])([&](auto boxes, auto scores) {
-                compute_nms(output,
-                            boxes,
-                            scores,
-                            output_shape,
-                            max_output_boxes_per_class,
-                            iou_threshold,
-                            score_threshold);
+                num_selected = compute_nms(output,
+                                           boxes,
+                                           scores,
+                                           max_output_shape,
+                                           max_output_boxes_per_class,
+                                           iou_threshold,
+                                           score_threshold);
             });
         });
-
-        return result;
+        if(use_dyn_output)
+        {
+            return result.reshape({output_shape.type(), {num_selected, 3}});
+        }
+        else
+        {
+            return result;
+        }
     }
 };
 

src/include/migraphx/op/pooling.hpp

@@ -64,8 +64,8 @@ struct 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
+           stride.size() != lengths.size())
         {
             MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
         }
@@ -83,7 +83,7 @@ struct pooling
         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))
+        if(input_size != padding_size / 2 + 2 and input_size != padding_size + 2)
         {
             MIGRAPHX_THROW("POOLING: input and attribute size mismatch!");
         }

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/quant_dot.hpp

@@ -49,13 +49,14 @@ struct quant_dot
             MIGRAPHX_THROW("QUANT_DOT: only support data type int8_t");
         }
 
-        if(!std::all_of(inputs.begin(), inputs.end(), [](auto s) { return s.lens().size() >= 2; }))
+        if(not std::all_of(
+               inputs.begin(), inputs.end(), [](auto s) { return s.lens().size() >= 2; }))
         {
             MIGRAPHX_THROW("QUANT_DOT: dot only accept 2 or more dims operands");
         }
 
         // only handle the case that the batch size of a and b are the same
-        if(!std::equal(
+        if(not std::equal(
                a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2, b.lens().rend()))
         {
             MIGRAPHX_THROW("QUANT_DOT: batch size of A and B mismatch: {" +

src/include/migraphx/op/slice.hpp

@@ -78,7 +78,7 @@ struct slice
         const std::vector<std::size_t>& lens    = s.lens();
         const std::vector<std::size_t>& strides = s.strides();
         auto offset                             = 0;
-        if(!axes.empty())
+        if(not axes.empty())
         {
             for(std::size_t i = 0; i < axes.size(); i++)
             {
@@ -109,7 +109,7 @@ struct slice
             MIGRAPHX_THROW("SLICE: input axis " + to_string_range(axes) + " out of range");
         }
 
-        if(starts.size() != axes.size() || axes.size() != ends.size())
+        if(starts.size() != axes.size() or axes.size() != ends.size())
         {
             MIGRAPHX_THROW("SLICE: inconsistent sizes");
         }

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 +55,90 @@ 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()))
+            std::vector<shape::dynamic_dimension> one_dyn_dims{{1, 1, 0}, {1, 1, 1}};
+            if(std::any_of(axes.begin(), axes.end(), [&](auto axis) {
+                   return not contains(one_dyn_dims, input_shape.dyn_dims()[axis]);
+               }))
+            {
+                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())
             {
-                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]);
+                    auto dd = input_shape.dyn_dims()[i];
+                    if(not contains(one_dyn_dims, dd))
+                    {
+                        dyn_dims.push_back(dd);
+                    }
                 }
             }
-        }
-        else
-        {
-            for(auto i : range(old_lens.size()))
+            else
             {
-                if(std::find(axes.begin(), axes.end(), i) == axes.end())
+                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]);
+                    }
                 }
             }
-        }
-        if(new_lens.empty())
-        {
-            return shape{type};
+            return {input_shape.type(), dyn_dims};
         }
         else
         {
-            return shape{type, new_lens, new_strides};
+            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; }))
+            {
+                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()))
+                {
+                    if(old_lens[i] != 1)
+                    {
+                        new_lens.push_back(old_lens[i]);
+                        new_strides.push_back(old_strides[i]);
+                    }
+                }
+            }
+            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

@@ -59,7 +59,7 @@ struct transpose
         }
         std::vector<int64_t> axes(dims.size());
         std::iota(axes.begin(), axes.end(), 0);
-        if(!std::is_permutation(axes.begin(), axes.end(), dims.begin()))
+        if(not std::is_permutation(axes.begin(), axes.end(), dims.begin()))
         {
             MIGRAPHX_THROW("TRANSPOSE: Invalid permutation");
         }

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>&)
@@ -1066,7 +1088,7 @@ struct operation
         template <typename PrivateDetailTypeErasedU = PrivateDetailTypeErasedT>
         private_detail_te_handle_type(
             PrivateDetailTypeErasedT value,
-            typename std::enable_if<!std::is_reference<PrivateDetailTypeErasedU>::value,
+            typename std::enable_if<not std::is_reference<PrivateDetailTypeErasedU>::value,
                                     int>::type* = nullptr) noexcept
             : private_detail_te_value(std::move(value))
         {
@@ -1237,7 +1259,7 @@ struct operation
     private_detail_te_handle_base_type& private_detail_te_get_handle()
     {
         assert(private_detail_te_handle_mem_var != nullptr);
-        if(!private_detail_te_handle_mem_var.unique())
+        if(not private_detail_te_handle_mem_var.unique())
             private_detail_te_handle_mem_var = private_detail_te_handle_mem_var->clone();
         return *private_detail_te_handle_mem_var;
     }
@@ -1276,7 +1298,7 @@ inline const ValueType& any_cast(const operation& x)
 }
 #endif
 
-inline bool operator!=(const operation& x, const operation& y) { return !(x == y); }
+inline bool operator!=(const operation& x, const operation& y) { return not(x == y); }
 
 inline value
 compile(operation& op, context& ctx, const shape& output_shape, const std::vector<shape>& input)

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/pass.hpp

@@ -238,7 +238,7 @@ struct pass
         template <typename PrivateDetailTypeErasedU = PrivateDetailTypeErasedT>
         private_detail_te_handle_type(
             PrivateDetailTypeErasedT value,
-            typename std::enable_if<!std::is_reference<PrivateDetailTypeErasedU>::value,
+            typename std::enable_if<not std::is_reference<PrivateDetailTypeErasedU>::value,
                                     int>::type* = nullptr) noexcept
             : private_detail_te_value(std::move(value))
         {
@@ -292,7 +292,7 @@ struct pass
     private_detail_te_handle_base_type& private_detail_te_get_handle()
     {
         assert(private_detail_te_handle_mem_var != nullptr);
-        if(!private_detail_te_handle_mem_var.unique())
+        if(not private_detail_te_handle_mem_var.unique())
             private_detail_te_handle_mem_var = private_detail_te_handle_mem_var->clone();
         return *private_detail_te_handle_mem_var;
     }

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;
@@ -124,7 +125,7 @@ struct program
 
     friend std::ostream& operator<<(std::ostream& os, const program& p);
     friend bool operator==(const program& x, const program& y);
-    friend bool operator!=(const program& x, const program& y) { return !(x == y); }
+    friend bool operator!=(const program& x, const program& y) { return not(x == y); }
 
     // module related api
     module* create_module(const std::string& name);

src/include/migraphx/raw_data.hpp

@@ -147,7 +147,7 @@ struct raw_data : raw_data_base
         template <class T>
         bool matches() const
         {
-            return is_data_ptr<T>{} ||
+            return is_data_ptr<T>{} or
                    self->get_shape().type() == migraphx::shape::get_type<get_data_type<T>>{};
         }
 
@@ -232,7 +232,7 @@ auto visit_all(T&& x, Ts&&... xs)
 {
     auto&& s                                   = x.get_shape();
     std::initializer_list<shape::type_t> types = {xs.get_shape().type()...};
-    if(!std::all_of(types.begin(), types.end(), [&](shape::type_t t) { return t == s.type(); }))
+    if(not std::all_of(types.begin(), types.end(), [&](shape::type_t t) { return t == s.type(); }))
         MIGRAPHX_THROW("Types must be the same");
     return [&](auto... vs) { detail::visit_all_pack(s, vs...)(x, xs...); };
 }
@@ -241,7 +241,7 @@ template <class T>
 auto visit_all(const std::vector<T>& x)
 {
     auto&& s = x.front().get_shape();
-    if(!std::all_of(
+    if(not std::all_of(
            x.begin(), x.end(), [&](const T& y) { return y.get_shape().type() == s.type(); }))
         MIGRAPHX_THROW("Types must be the same");
     return [&](auto v) {
@@ -281,7 +281,7 @@ template <class T,
                             std::is_base_of<raw_data_base, U>{})>
 bool operator!=(const T& x, const U& y)
 {
-    return !(x == y);
+    return not(x == y);
 }
 
 } // namespace MIGRAPHX_INLINE_NS

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>
@@ -129,7 +139,7 @@ template <class T>
 struct reflect_equality
 {
     friend bool operator==(const T& x, const T& y) { return reflect_tie(x) == reflect_tie(y); }
-    friend bool operator!=(const T& x, const T& y) { return !(x == y); }
+    friend bool operator!=(const T& x, const T& y) { return not(x == y); }
 };
 
 template <class T>

src/include/migraphx/requires.hpp

@@ -31,7 +31,7 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
 template <bool... Bs>
-struct and_ : std::is_same<and_<Bs...>, and_<(Bs || true)...>> // NOLINT
+struct and_ : std::is_same<and_<Bs...>, and_<(Bs or true)...>> // NOLINT
 {
 };
 

src/include/migraphx/rewrite_batchnorm.hpp → src/include/migraphx/rewrite_gelu.hpp

@@ -21,8 +21,8 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_RTGLIB_FWD_CONV_BATCHNORM_REWRITE_HPP
-#define MIGRAPHX_GUARD_RTGLIB_FWD_CONV_BATCHNORM_REWRITE_HPP
+#ifndef MIGRAPHX_GUARD_RTGLIB_REWRITE_GELU_HPP
+#define MIGRAPHX_GUARD_RTGLIB_REWRITE_GELU_HPP
 
 #include <string>
 #include <migraphx/instruction_ref.hpp>
@@ -34,11 +34,11 @@ inline namespace MIGRAPHX_INLINE_NS {
 struct module;
 
 /**
- * Rewrite batchnorm to a multiply and add.
+ * Rewrite gelu standard formula as the sigmoid approximation formula
  */
-struct rewrite_batchnorm
+struct rewrite_gelu
 {
-    std::string name() const { return "rewrite_batchnorm"; }
+    std::string name() const { return "rewrite_gelu"; }
     void apply(module& m) const;
 };