Merge branch 'develop' into jit-reduce-reg

.clang-tidy

@@ -9,6 +9,8 @@ CheckOptions:
     value:           risky   
   - key:             modernize-loop-convert.NamingStyle
     value:           lower_case
+  - key:             misc-const-correctness.AnalyzeValues
+    value:           'false'
   - key:             performance-unnecessary-copy-initialization.AllowedTypes
     value:           'shape'
   - key:             performance-unnecessary-value-param.AllowedTypes

.github/workflows/ci.yaml

@@ -32,7 +32,8 @@ jobs:
     # In this step, this action saves a list of existing images,
     # the cache is created without them in the post run.
     # It also restores the cache if it exists.
-    - uses: satackey/action-docker-layer-caching@v0.0.11
+    # name: Docker Layer Caching2
+    - uses: jpribyl/action-docker-layer-caching@v0.1.1
       # Ignore the failure of a step and avoid terminating the job.
       continue-on-error: true
 
@@ -81,7 +82,7 @@ jobs:
     # In this step, this action saves a list of existing images,
     # the cache is created without them in the post run.
     # It also restores the cache if it exists.
-    - uses: satackey/action-docker-layer-caching@v0.0.11
+    - uses: jpribyl/action-docker-layer-caching@v0.1.1
       # Ignore the failure of a step and avoid terminating the job.
       continue-on-error: true
 
@@ -126,7 +127,7 @@ jobs:
     # In this step, this action saves a list of existing images,
     # the cache is created without them in the post run.
     # It also restores the cache if it exists.
-    - uses: satackey/action-docker-layer-caching@v0.0.11
+    - uses: jpribyl/action-docker-layer-caching@v0.1.1
       # Ignore the failure of a step and avoid terminating the job.
       continue-on-error: true
 

Dockerfile

@@ -10,7 +10,7 @@ RUN apt-get update && apt-get install -y gnupg2 --no-install-recommends curl &&
     curl -sL http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - 
 
 # Add rocm repository
-RUN sh -c 'echo deb [arch=amd64 trusted=yes] http://repo.radeon.com/rocm/apt/5.3/ ubuntu main > /etc/apt/sources.list.d/rocm.list'
+RUN sh -c 'echo deb [arch=amd64 trusted=yes] http://repo.radeon.com/rocm/apt/5.4.2/ ubuntu main > /etc/apt/sources.list.d/rocm.list'
 
 # Install dependencies
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \

hip-clang.docker

@@ -6,7 +6,7 @@ ARG PREFIX=/usr/local
 RUN dpkg --add-architecture i386
 
 # Add rocm repository
-RUN sh -c 'echo deb [arch=amd64 trusted=yes] http://repo.radeon.com/rocm/apt/5.3/ ubuntu main > /etc/apt/sources.list.d/rocm.list'
+RUN sh -c 'echo deb [arch=amd64 trusted=yes] http://repo.radeon.com/rocm/apt/5.4.2/ ubuntu main > /etc/apt/sources.list.d/rocm.list'
 
 # Install dependencies
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \

src/CMakeLists.txt

@@ -58,6 +58,7 @@ add_library(migraphx
     layout_nhwc.cpp
     load_save.cpp
     make_op.cpp
+    memory_coloring.cpp
     module.cpp
     msgpack.cpp
     normalize_attributes.cpp
@@ -65,8 +66,6 @@ add_library(migraphx
     op_enums.cpp
     operation.cpp
     optimize_module.cpp
-    opt/memory_coloring.cpp
-    opt/memory_coloring_impl.cpp
     pad_calc.cpp
     pass_manager.cpp
     permutation.cpp

src/include/migraphx/half.hpp

@@ -58,12 +58,12 @@ using deduce = typename detail::deduce<T>::type;
 namespace std {
 
 template <class T>
-struct common_type<migraphx::half, T> : std::common_type<float, T>
+struct common_type<migraphx::half, T> : std::common_type<float, T> // NOLINT
 {
 };
 
 template <class T>
-struct common_type<T, migraphx::half> : std::common_type<float, T>
+struct common_type<T, migraphx::half> : std::common_type<float, T> // NOLINT
 {
 };
 

src/include/migraphx/instruction_ref.hpp

@@ -41,7 +41,7 @@ migraphx::instruction* as_address(const instruction_ref& ins) noexcept;
 
 namespace std {
 template <>
-struct hash<migraphx::instruction_ref>
+struct hash<migraphx::instruction_ref> // NOLINT
 {
     using argument_type = migraphx::instruction_ref;
     using result_type   = std::size_t;
@@ -52,7 +52,7 @@ struct hash<migraphx::instruction_ref>
 };
 
 template <>
-struct equal_to<migraphx::instruction_ref>
+struct equal_to<migraphx::instruction_ref> // NOLINT
 {
     using argument_type = migraphx::instruction_ref;
     using result_type   = bool;

src/include/migraphx/memory_coloring.hpp

@@ -39,7 +39,7 @@ struct memory_coloring
 {
     std::string allocation_op{};
     bool verify = false;
-    std::string name() const { return "memory coloring"; }
+    std::string name() const { return "memory_coloring"; }
     void apply(module& m) const;
 };
 

src/include/migraphx/op/gathernd.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 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
@@ -25,6 +25,7 @@
 #define MIGRAPHX_GUARD_OPERATORS_GATHERND_HPP
 
 #include <migraphx/check_shapes.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/shape_for_each.hpp>
 #include <migraphx/par_for.hpp>
 #include <migraphx/argument.hpp>
@@ -47,33 +48,103 @@ struct gathernd
 
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(2);
-        auto r = inputs.front().lens().size();
-        auto q = inputs.back().lens().size();
-        auto k = inputs.back().lens().back();
+        check_shapes{inputs, *this, true}.has(2);
+        auto i_shape    = inputs.back();
+        auto data_shape = inputs.front();
+        auto r          = data_shape.ndim();
+        auto q          = i_shape.ndim();
+
+        size_t k;
+        if(i_shape.dynamic())
+        {
+            // the rank of the output is a function of k, so it must be fixed.
+            if(not i_shape.dyn_dims().back().is_fixed())
+            {
+                MIGRAPHX_THROW(
+                    "GATHERND: last dimension of indices tensor must be fixed (min=max)");
+            }
+            k = i_shape.dyn_dims().back().min;
+        }
+        else
+            k = i_shape.lens().back();
+
+        // Begin input validation checks.
+        int output_ndim = int(q) + r - k - batch_dims - 1;
+
         if(k > r - batch_dims)
         {
             MIGRAPHX_THROW("GATHERND: Indices of length " + std::to_string(k) +
                            " cannot be used to access data of rank " +
                            std::to_string(r - batch_dims));
         }
-        auto indices_lens_iter = inputs.back().lens().begin();
-        auto output_lens_size  = q + r - k - batch_dims - 1;
-        std::vector<std::size_t> output_lens(output_lens_size);
-        std::copy(indices_lens_iter, indices_lens_iter + (q - 1), output_lens.begin());
-        if(k < r - batch_dims)
+
+        if(batch_dims >= q or batch_dims >= r)
+        {
+            MIGRAPHX_THROW("GATHERND: rank of an input cannot be less than batch_dims=" +
+                           std::to_string(batch_dims));
+        }
+
+        if(output_ndim < 0)
+        {
+            MIGRAPHX_THROW("GATHERND: Indices too large for static data input: k=" +
+                           std::to_string(k));
+        }
+
+        if(migraphx::none_of(inputs, [](auto v) { return v.dynamic(); }))
+        {
+            auto indices_lens_iter = i_shape.lens().begin();
+
+            // A rank 0 output is a scalar
+            if(output_ndim == 0)
+                return shape{data_shape.type(), {1}};
+
+            // Part of the output shape comes from indices tensor, part from data tensor
+            std::vector<std::size_t> output_lens(output_ndim);
+            std::copy(indices_lens_iter, indices_lens_iter + (q - 1), output_lens.begin());
+            // fill the rest of output shape from data tensor
+            if(k + batch_dims < r)
+            {
+                auto data_lens = data_shape.lens();
+                std::copy(data_lens.begin() + batch_dims + k,
+                          data_lens.end(),
+                          output_lens.begin() + q - 1);
+            }
+            shape output_shape{data_shape.type(), output_lens};
+            return output_shape;
+        }
+        else
         {
-            auto data_lens = inputs.front().lens();
-            std::copy(
-                data_lens.begin() + batch_dims + k, data_lens.end(), output_lens.begin() + q - 1);
+            // If one or both inputs are dynamic shapes, the output is dynamic.
+            // Make both inputs dynamic to simplify computations.
+            data_shape = data_shape.to_dynamic();
+            i_shape    = i_shape.to_dynamic();
+
+            // A rank 0 output is a scalar
+            if(output_ndim == 0)
+                return shape(data_shape.type(), {shape::dynamic_dimension({1, 1, 0})});
+
+            // Part of the output shape comes from indices tensor, part from data tensor
+            std::vector<shape::dynamic_dimension> output_dims(output_ndim);
+            std::copy(i_shape.dyn_dims().begin(),
+                      i_shape.dyn_dims().begin() + q - 1,
+                      output_dims.begin());
+
+            // fill the rest of output shape from data tensor
+            if(k + batch_dims < r)
+            {
+                auto data_dims = data_shape.dyn_dims();
+                std::copy(data_dims.begin() + batch_dims + k,
+                          data_dims.begin() + r,
+                          output_dims.begin() + q - 1);
+            }
+            shape output_shape(data_shape.type(), output_dims);
+            return output_shape;
         }
-        shape output_shape{inputs.front().type(), output_lens};
-        return output_shape;
     }
 
-    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};
         visit_all(result, args[0])([&](auto output, auto data) {
             args[1].visit([&](auto indices) {
                 auto indices_shape        = indices.get_shape();

src/include/migraphx/op/scatternd_op.hpp

@@ -28,44 +28,89 @@
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/par_for.hpp>
+#include <migraphx/ranges.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
-
+/**
+ * @brief
+ * N-dimensional Scatter operations. This struct is parent class to ops which differ in what formula
+ * is used to reduce (combine old and new values of) the scattered value.  It was originally based
+ * on Onnx ScatterND operation (see
+ * https://github.com/onnx/onnx/blob/main/docs/Operators.md#ScatterND) and is also similar to Numpy
+ * numpy.add.at().
+ *
+ * @tparam Derived   a template parameter in the CRTP inheritance idiom, represents one of the child
+ * operations.
+ */
 template <class Derived>
 struct scatternd_op : op_name<Derived>
 {
+    /** Validate input shapes and return the correct output shape.  For Scatter ops, the output
+     * is the same shape as the data tensor (first input), but cast to a standard shape.
+     *
+     */
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(3);
-        auto r         = inputs.front().lens().size();
-        auto q         = inputs.at(1).lens().size();
-        auto k         = inputs.at(1).lens().back();
-        auto ind_lens  = inputs.at(1).lens();
-        auto upd_lens  = inputs.back().lens();
-        auto data_lens = inputs.front().lens();
+        check_shapes{inputs, *this, true}.has(3);
+        auto data_shape  = inputs.front();
+        auto index_shape = inputs.at(1);
+        auto upd_shape   = inputs.back();
+
+        auto r = data_shape.ndim();
+        auto q = index_shape.ndim();
+        size_t k;
+        if(index_shape.dynamic())
+        {
+            // the rank of the output is a function of k, so k must be fixed.
+            if(not index_shape.dyn_dims().back().is_fixed())
+            {
+                MIGRAPHX_THROW(
+                    "GATHERND: last dimension of indices tensor must be fixed (min=max)");
+            }
+            k = index_shape.dyn_dims().back().min;
+        }
+        else
+            k = index_shape.lens().back();
+
+        // Checks on the sizes of input tensors
+        if(q + r != upd_shape.ndim() + k + 1)
+            MIGRAPHX_THROW("ScatterND:  ranks of inputs don't match. " + std::to_string(q) + " + " +
+                           std::to_string(r) + " - " + std::to_string(k) +
+                           " - 1 != " + std::to_string(upd_shape.ndim()));
         if(k > r)
             MIGRAPHX_THROW("ScatterND: index of size " + std::to_string(k) +
                            " is too large for tensor of rank " + std::to_string(r));
-        if(not(std::equal(ind_lens.begin(), ind_lens.begin() + q - 1, upd_lens.begin()) and
-               std::equal(data_lens.begin() + k, data_lens.end(), upd_lens.begin() + q - 1)))
-            MIGRAPHX_THROW("ScatterND: incorrect update shape. update.lens != indices.lens[0:q-1] "
-                           "++ data.lens[k:r-1]");
-        auto s = inputs.front();
-        if(s.broadcasted())
+
+        // Convert all static shape dimensions to dynamic so they can be compared.
+        // It's possible for some of the 3 inputs to be dynamic shapes and some static,
+        // but any dynamic dimension that's compared to a static dimension must be fixed.
+        auto ind_dims  = index_shape.to_dynamic().dyn_dims();
+        auto upd_dims  = upd_shape.to_dynamic().dyn_dims();
+        auto data_dims = data_shape.to_dynamic().dyn_dims();
+
+        // Check that corresponding portions of tensor shapes match.
+        if(not(std::equal(ind_dims.begin(), ind_dims.begin() + q - 1, upd_dims.begin()) and
+               std::equal(data_dims.begin() + k, data_dims.end(), upd_dims.begin() + q - 1)))
+            MIGRAPHX_THROW("ScatterND: incorrect update shape. Update dimensions must match "
+                           "indices and data.");
+
+        if(data_shape.dynamic())
+            return data_shape;
+        else if(data_shape.broadcasted())
         {
-            return {s.type(), s.lens()};
+            return {data_shape.type(), data_shape.lens()};
         }
         else
         {
-            return s.with_lens(s.lens());
+            return data_shape.with_lens(data_shape.lens());
         }
     }
 
-    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& self = static_cast<const Derived&>(*this);
         visit_all(result, args[0], args[2])([&](auto output, auto data, auto updates) {
             std::copy(data.begin(), data.end(), output.begin());
@@ -74,8 +119,8 @@ struct scatternd_op : op_name<Derived>
                 auto updates_std   = shape{updates_shape.type(), updates_shape.lens()};
                 auto indices_shape = indices.get_shape();
                 auto k             = indices_shape.lens().back();
-                auto q             = indices_shape.lens().size();
-                auto r             = output_shape.lens().size();
+                auto q             = indices_shape.ndim();
+                auto r             = dyn_out.computed_shape.ndim();
                 par_for(updates_shape.elements(), [&](const auto i) {
                     auto updates_idx = updates_std.multi(i);
                     std::vector<std::size_t> indices_idx(q, 0);
@@ -89,7 +134,7 @@ struct scatternd_op : op_name<Derived>
                     std::copy(index_start, index_end, out_idx.begin());
                     std::copy(updates_idx.begin() + q - 1, updates_idx.end(), out_idx.begin() + k);
 
-                    self.reduction()(output[output_shape.index(out_idx)], updates[i]);
+                    self.reduction()(output[dyn_out.computed_shape.index(out_idx)], updates[i]);
                 });
             });
         });

src/memory_coloring.cpp

+/*
+ * 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.
+ */
+#include <migraphx/memory_coloring.hpp>
+#include <migraphx/module.hpp>
+#include <migraphx/operators.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/iterator_for.hpp>
+#include <migraphx/functional.hpp>
+#include <migraphx/algorithm.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/stringutils.hpp>
+#include <unordered_set>
+#include <unordered_map>
+#include <map>
+#include <set>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_DEBUG_MEMORY_COLORING);
+
+using instruction_set     = std::unordered_set<instruction_ref>;
+using instruction_set_map = std::unordered_map<instruction_ref, instruction_set>;
+
+// This will do liveness analysis on the module, and it will call the
+// function `f` with the instruction and the set of the other instructions
+// that are live
+template <class F>
+void liveness(const module& m, F f)
+{
+    auto implicit_deps = m.calc_implicit_deps();
+    instruction_set live_set;
+    auto rp = reverse(m);
+    for(auto rins : iterator_for(rp)) // NOLINT
+    {
+        // The base iterator is one ahead, so we need to use the previous iterator
+        auto ins = std::prev(rins.base());
+        // Add live variables
+        auto add_live_variables = [&](const auto& inputs) {
+            for(auto input : inputs)
+            {
+                auto i = instruction::get_output_alias(input);
+                // Skip if variable comes from parent
+                if(not m.has_instruction(i))
+                    continue;
+                live_set.insert(i);
+            }
+        };
+        add_live_variables(ins->inputs());
+        add_live_variables(implicit_deps[ins]);
+        // Remove last usage
+        auto it = live_set.find(ins);
+        if(it != live_set.end())
+        {
+            live_set.erase(it);
+            f(ins, live_set);
+        }
+    }
+}
+
+// This will build the conflict table or interference graph. This is
+// essentially a map from one instruction to a set of instruction that are
+// used together. Each instruction will be the allocation instruction.
+instruction_set_map build_conflict_table(const module& m, std::string allocation_op)
+{
+    instruction_set_map conflict_table;
+    liveness(m, [&](auto ins, auto live_set) {
+        // Skip variables that aren't allocations
+        if(ins->name() != allocation_op)
+            return;
+        // Skip zero allocations
+        if(ins->get_shape().bytes() == 0)
+            return;
+        conflict_table[ins];
+        for(auto i : live_set)
+        {
+            if(i == ins)
+                continue;
+            // Skip variables that aren't allocations
+            if(i->name() != allocation_op)
+                continue;
+            // Skip zero allocations
+            if(i->get_shape().bytes() == 0)
+                continue;
+            conflict_table[i].insert(ins);
+            conflict_table[ins].insert(i);
+        }
+    });
+    assert(std::all_of(conflict_table.begin(), conflict_table.end(), [](auto&& pp) {
+        return pp.second.count(pp.first) == 0;
+    }));
+    return conflict_table;
+}
+
+// Check if intervals overlap
+bool is_overlap(std::pair<std::size_t, std::size_t> x, std::pair<std::size_t, std::size_t> y)
+{
+    return std::max(x.first, y.first) < std::min(x.second, y.second);
+}
+
+struct allocation_segment
+{
+    using segment = std::pair<std::size_t, std::size_t>;
+    std::unordered_map<instruction_ref, segment> ins2segment;
+
+    const segment* add_segment(instruction_ref ins, segment s) { return &(ins2segment[ins] = s); }
+
+    const segment* get_segment(instruction_ref ins) const
+    {
+        auto it = ins2segment.find(ins);
+        if(it == ins2segment.end())
+            return nullptr;
+        return &it->second;
+    }
+
+    // Remove segment for an instruction
+    void remove(instruction_ref ins)
+    {
+        auto it = ins2segment.find(ins);
+        if(it != ins2segment.end())
+        {
+            ins2segment.erase(it);
+        }
+    }
+
+    std::size_t max()
+    {
+        std::size_t n = 0;
+        for(auto&& pp : ins2segment)
+        {
+            auto seg = pp.second;
+            n        = std::max(n, seg.second);
+        }
+        return n;
+    }
+
+    template <class Iterator>
+    static bool overlaps(Iterator first, Iterator last, const segment& s)
+    {
+        return std::any_of(first, last, [&](auto&& t) { return is_overlap(s, t); });
+    }
+
+    static bool overlaps(const std::set<segment>& segments, const segment& s)
+    {
+        return overlaps(segments.begin(), segments.end(), s);
+    }
+
+    static auto find_gap(const std::set<segment>& segments, std::size_t n)
+    {
+        std::size_t max_end = 0;
+        return std::adjacent_find(segments.begin(), segments.end(), [&](segment x, segment y) {
+            if(x.second < max_end)
+                return false;
+            max_end = x.second;
+            if(is_overlap(x, y))
+                return false;
+            assert(y.first >= x.second);
+            auto k = y.first - x.second;
+            return (k >= n);
+        });
+    }
+
+    static std::size_t max_type_size(const shape& s)
+    {
+        return std::accumulate(
+            s.sub_shapes().begin(),
+            s.sub_shapes().end(),
+            s.type_size(),
+            [](auto size, const auto& sub) { return std::max(size, max_type_size(sub)); });
+    }
+
+    static std::size_t compute_alignment(instruction_ref ins)
+    {
+        auto alignment = max_type_size(ins->get_shape());
+        // A rough estimate for the total number of elements
+        auto n = ins->get_shape().bytes() / alignment;
+        // Check for vectorized alignment
+        if(n > 4)
+        {
+            auto d = n % 4;
+            if(d == 0)
+                alignment *= 4;
+            if(d == 2)
+                alignment *= 2;
+        }
+        return alignment;
+    }
+
+    static segment
+    next_segment(std::set<segment>& segments, instruction_ref ins, std::size_t alignment)
+    {
+        assert(ins->get_shape().bytes() > 0);
+        // Compute alignment
+        auto n = 1 + (ins->get_shape().bytes() - 1) / alignment;
+        assert(n > 0);
+        auto start = 0;
+        // Insert at end if it cant fit at the begining
+        if(segments.empty() or segments.begin()->first <= n)
+        {
+            auto it = find_gap(segments, n);
+            if(it == segments.end())
+                it = std::max_element(segments.begin(), segments.end(), [&](segment x, segment y) {
+                    return x.second < y.second;
+                });
+            if(it != segments.end())
+                start = it->second;
+        }
+        auto s = segment{start, start + n};
+        assert(not overlaps(segments, s));
+        segments.insert(s);
+        return s;
+    }
+
+    static std::unordered_map<instruction_ref, int>
+    create_allocation_index(const module& m, const instruction_set_map& conflict_table)
+    {
+        std::unordered_map<instruction_ref, int> result;
+        int i = 0;
+        for(auto ins : iterator_for(m))
+        {
+            if(not contains(conflict_table, ins))
+                continue;
+            result[ins] = i++;
+        }
+        return result;
+    }
+
+    // Build the allocation_color class from the conflict_table
+    static allocation_segment
+    build(const module& m, const instruction_set_map& conflict_table, std::size_t alignment)
+    {
+        allocation_segment as{};
+        std::vector<instruction_ref> conflict_queue;
+        // Add all allocations to the conflict_queue
+        std::transform(conflict_table.begin(),
+                       conflict_table.end(),
+                       std::back_inserter(conflict_queue),
+                       [](auto&& pp) { return pp.first; });
+
+        auto alloc_index = create_allocation_index(m, conflict_table);
+
+        // Sort the conflict queue so we process the allocation with the most
+        // number of adjacent allocations first
+        std::sort(conflict_queue.begin(), conflict_queue.end(), by(std::greater<>{}, [&](auto x) {
+                      return std::make_tuple(
+                          conflict_table.at(x).size(), x->get_shape().bytes(), alloc_index.at(x));
+                  }));
+        // Process the conflict_queue, we refer to the current allocation as
+        // the parent and the adjacent allocations as children
+        for(auto parent : conflict_queue)
+        {
+            // Sort children by size
+            std::vector<instruction_ref> children(conflict_table.at(parent).begin(),
+                                                  conflict_table.at(parent).end());
+            std::sort(children.begin(), children.end(), by(std::less<>{}, [&](auto x) {
+                          return std::make_tuple(x->get_shape().bytes(), alloc_index.at(x));
+                      }));
+            assert(not contains(children, parent));
+            // This set is to track the segments already processed
+            std::set<segment> segments;
+            // Add all segments for the children to the segments already processed
+            transform_if(
+                children.begin(),
+                children.end(),
+                std::inserter(segments, segments.begin()),
+                [&](auto child) { return as.get_segment(child); },
+                [&](auto child) { return *as.get_segment(child); });
+
+            assert(as.get_segment(parent) == nullptr);
+            as.add_segment(parent, next_segment(segments, parent, alignment));
+        }
+        // Reduce the number of segments
+        for(std::size_t n = 0; n < 3; n++)
+        {
+            for(auto parent : conflict_queue)
+            {
+                auto children = conflict_table.at(parent);
+                // This set is to track the segments already processed
+                std::set<segment> segments;
+                // Add all segments for the children to the segments already processed
+                transform_if(
+                    children.begin(),
+                    children.end(),
+                    std::inserter(segments, segments.begin()),
+                    [&](auto child) { return as.get_segment(child); },
+                    [&](auto child) { return *as.get_segment(child); });
+                // Get the segment for the parent
+                const auto* parent_segment = as.get_segment(parent);
+                assert(parent_segment != nullptr);
+
+                auto s = next_segment(segments, parent, alignment);
+                if(s != *parent_segment and s.second <= as.max())
+                {
+                    as.add_segment(parent, s);
+                }
+            }
+        }
+        return as;
+    }
+};
+
+static std::size_t find_max_alignment(const module& m, const std::string& allocation_op)
+{
+    std::size_t alignment = 1;
+    for(auto ins : iterator_for(m))
+    {
+        if(ins->name() != allocation_op)
+            continue;
+        alignment = std::max(allocation_segment::compute_alignment(ins), alignment);
+    }
+    return alignment;
+}
+
+void memory_coloring::apply(module& m) const
+{
+    const std::size_t alignment = find_max_alignment(m, allocation_op);
+    auto conflict_table         = build_conflict_table(m, allocation_op);
+    auto as                     = allocation_segment::build(m, conflict_table, alignment);
+
+    // All allocations should have a segment
+    assert(std::all_of(conflict_table.begin(), conflict_table.end(), [&](auto&& pp) {
+        return as.get_segment(pp.first);
+    }));
+
+    // Adjacent allocations should not have overlapping segments
+    assert(std::none_of(conflict_table.begin(), conflict_table.end(), [&](auto&& pp) {
+        auto* x = as.get_segment(pp.first);
+        return std::any_of(pp.second.begin(), pp.second.end(), [&](auto ins) {
+            auto* y = as.get_segment(ins);
+            assert(x and y);
+            return is_overlap(*x, *y);
+        });
+    }));
+
+    // Print out segments
+    if(enabled(MIGRAPHX_DEBUG_MEMORY_COLORING{}))
+    {
+        for(auto&& pp : conflict_table)
+        {
+            std::cout << "------- conflict -------" << std::endl;
+            auto s1 = as.ins2segment.at(pp.first);
+            std::cout << s1.first << ", " << s1.second << ": ";
+            m.debug_print(pp.first);
+            for(auto ins : pp.second)
+            {
+                auto s2 = as.ins2segment.at(ins);
+                std::cout << s2.first << ", " << s2.second << ": ";
+                m.debug_print(ins);
+            }
+        }
+    }
+
+    // Total memory
+    std::size_t n = as.max() * alignment;
+
+    // Replace allocations
+    auto mem = m.add_parameter("scratch", shape{shape::int8_type, {n}});
+    for(auto&& [ins, seg] : as.ins2segment)
+    {
+        assert(ins->name() == allocation_op);
+        auto s             = ins->get_shape();
+        std::size_t offset = seg.first * alignment;
+        assert(offset < n);
+        m.replace_instruction(ins, op::load{s, offset}, mem);
+    }
+
+    // Replace zero allocation
+    for(auto ins : iterator_for(m))
+    {
+        if(ins->name() != allocation_op)
+            continue;
+        assert(ins->get_shape().bytes() == 0);
+        m.replace_instruction(ins, op::load{ins->get_shape(), 0}, mem);
+    }
+
+    // Remove scratch parameter if its not used
+    if(mem->outputs().empty())
+    {
+        m.remove_instruction(mem);
+    }
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/include/migraphx/onnx/onnx_parser.hpp

@@ -113,7 +113,8 @@ struct onnx_parser
 
     void parse_from(std::istream& is, std::string name = "");
     void parse_from(const void* data, std::size_t size);
-    void parse_graph(module* mod, const onnx::GraphProto& graph);
+    std::vector<instruction_ref>
+    parse_graph(module* mod, const onnx::GraphProto& graph, bool inlining = false);
     literal parse_value(const onnx::AttributeProto& attr) const;
     literal parse_tensor(const onnx::TensorProto& t) const;
     shape parse_type(const onnx::TypeProto& t, const std::vector<std::size_t>& input_dims) const;

src/onnx/onnx_parser.cpp

@@ -220,7 +220,7 @@ void onnx_parser::parse_from(std::istream& is, std::string name)
 
         if(model.has_graph())
         {
-            this->parse_graph(mm, model.graph());
+            (void)this->parse_graph(mm, model.graph());
         }
     }
     else
@@ -240,7 +240,7 @@ void onnx_parser::parse_from(const void* data, std::size_t size)
 
         if(model.has_graph())
         {
-            this->parse_graph(mm, model.graph());
+            (void)this->parse_graph(mm, model.graph());
         }
     }
     else
@@ -264,7 +264,8 @@ int64_t onnx_parser::get_opset_version(const onnx::ModelProto& model)
     return version;
 }
 
-void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
+std::vector<instruction_ref>
+onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph, bool inlining)
 {
     std::unordered_map<std::string, instruction_ref> mod_insts;
     for(auto&& f : graph.initializer())
@@ -372,11 +373,16 @@ void onnx_parser::parse_graph(module* mod, const onnx::GraphProto& graph)
                    std::back_inserter(output_ins),
                    [&](const auto& name) { return instructions[name]; });
 
-    // add the return instuction
-    mod->add_return(output_ins);
+    if(not inlining)
+    {
+        // add the return instuction
+        mod->add_return(output_ins);
+
+        // Remove instructions added in module (this is turned off for subgraph inlining)
+        erase_if(instructions, [&](auto&& p) { return mod->has_instruction(p.second); });
+    }
 
-    // remove instructions added in this mod
-    erase_if(instructions, [&](auto&& p) { return mod->has_instruction(p.second); });
+    return output_ins;
 }
 
 literal onnx_parser::parse_value(const onnx::AttributeProto& attr) const

src/onnx/parse_if.cpp

@@ -51,6 +51,24 @@ struct parse_if : op_parser<parse_if>
                            " condition input can have only one element!");
         }
 
+        // Fold instruction if condition is constant thus can be evaled
+        // prior to inference
+        if(args.front()->can_eval())
+        {
+            auto cond_arg = args.front()->eval();
+            auto* mod     = info.mod;
+            // then branch
+            if(cond_arg.at<bool>())
+            {
+                return parser.parse_graph(mod, then_graph, true);
+            }
+            // else branch
+            else
+            {
+                return parser.parse_graph(mod, else_graph, true);
+            }
+        }
+
         std::string then_name = info.name + "_if";
         module_ref then_mdl   = parser.prog.create_module(then_name);
 
@@ -58,10 +76,10 @@ struct parse_if : op_parser<parse_if>
         module_ref else_mdl   = parser.prog.create_module(else_name);
 
         // parse the then sub_graph
-        parser.parse_graph(then_mdl, then_graph);
+        (void)parser.parse_graph(then_mdl, then_graph);
 
         // parse_the else sub_graph
-        parser.parse_graph(else_mdl, else_graph);
+        (void)parser.parse_graph(else_mdl, else_graph);
 
         auto then_out_shapes = then_mdl->get_output_shapes();
         auto else_out_shapes = else_mdl->get_output_shapes();

src/onnx/parse_loop.cpp

@@ -71,7 +71,7 @@ struct parse_loop : op_parser<parse_loop>
         module_ref sub_mod    = parser.prog.create_module(mod_name);
 
         // parse the sub_graph
-        parser.parse_graph(sub_mod, sub_graph);
+        (void)parser.parse_graph(sub_mod, sub_graph);
 
         auto ret = info.add_instruction(
             make_op("loop", {{"max_iterations", max_iterations}}), args, {sub_mod});

src/pass_manager.cpp

@@ -39,6 +39,7 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_PASSES);
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TIME_PASSES);
 
 void validate_pass(module& mod, const pass& p, tracer trace)
 {
@@ -94,19 +95,19 @@ struct module_pm : module_pass_manager
     virtual void run_pass(const pass& p) override
     {
         assert(mod);
-
-        timer ts{};
-        using seconds = std::chrono::duration<double>;
-
-        trace("Module: ", mod->name(), ", Pass: ", p.name());
-        const double t1 = ts.record<seconds>();
         assert(mod->validate() == mod->end());
-        p.apply(*this);
+        if(enabled(MIGRAPHX_TIME_PASSES{}))
+        {
+            using milliseconds = std::chrono::duration<double, std::milli>;
+            auto ms            = time<milliseconds>([&] { p.apply(*this); });
+            std::cout << p.name() << ": " << ms << "ms\n";
+        }
+        else
+        {
+            p.apply(*this);
+        }
         trace(*mod);
         validate_pass(*mod, p, *t);
-
-        const double t2 = ts.record<seconds>();
-        trace("Pass: ", p.name(), " completed in (s): ", (t2 - t1));
     }
 };
 

src/program.cpp

@@ -336,7 +336,8 @@ std::vector<argument> generic_eval(const module* mod,
                     if(not ins->get_shape().dynamic() and param.get_shape() != ins->get_shape())
                     {
                         MIGRAPHX_THROW("Incorrect shape {" + to_string(param.get_shape()) +
-                                       "} for parameter: " + param_name);
+                                       "} for parameter: " + param_name +
+                                       " should be: " + to_string(ins->get_shape()));
                     }
                     return param;
                 }));

test/memory_coloring_test.cpp

@@ -691,7 +691,7 @@ TEST_CASE(test38)
     auto p83    = m.add_instruction(pass_op{}, p78, p77);
     m.add_instruction(pass_op{}, output, p83, p63);
     run_pass(m);
-    CHECK(m.get_parameter_shape("scratch").bytes() == 7225344); // Optimal solution is 6422528
+    CHECK(m.get_parameter_shape("scratch").bytes() == 6422528);
     CHECK(no_allocate(m));
 }
 
@@ -729,7 +729,7 @@ TEST_CASE(test39)
         run_pass(*smod);
     }
 
-    CHECK(mm->get_parameter_shape("scratch").bytes() == 4);
+    CHECK(mm->get_parameter_shape("scratch").bytes() == 1);
     CHECK(then_mod->get_parameter_shape("scratch").bytes() == 24);
     CHECK(else_mod->get_parameter_shape("scratch").bytes() == 24);
     CHECK(no_allocate(*mm));
@@ -3374,7 +3374,7 @@ TEST_CASE(rnn_dom)
     m.add_instruction(pass_op{}, moutput, mx250, mx249, mx248);
 
     run_pass(m);
-    CHECK(m.get_parameter_shape("scratch").bytes() == 1600);
+    CHECK(m.get_parameter_shape("scratch").bytes() == 1824); // Optimal is 1600
     CHECK(no_allocate(m));
     CHECK(is_disjoint({mx0, mx8}));
     CHECK(is_disjoint({mx0, mx8}));
@@ -3790,4 +3790,23 @@ TEST_CASE(literal_test)
     CHECK(lit == result);
 }
 
+TEST_CASE(test_tuple)
+{
+    migraphx::module m;
+
+    auto s1 = migraphx::shape{migraphx::shape::float_type, {8}};
+    auto s2 = migraphx::shape{migraphx::shape::half_type, {10}};
+
+    auto s = migraphx::shape{{s1, s2}};
+
+    auto a1 = add_alloc(m, s);
+    auto m1 = m.add_instruction(pass_op{}, a1);
+    auto a2 = add_alloc(m, {migraphx::shape::float_type, {4}});
+    m.add_instruction(pass_op{}, a2, m1);
+    run_pass(m);
+    CHECK(m.get_parameter_shape("scratch").bytes() == 68);
+    CHECK(no_allocate(m));
+    CHECK(is_disjoint({a1, a2}));
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/onnx/gen_onnx.py

@@ -2132,6 +2132,19 @@ def gathernd_test():
     return ([node], [x, i], [y])
 
 
+@onnx_test()
+def gathernd_dyn_test():
+    x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [None, 2])
+    i = helper.make_tensor_value_info('indices', TensorProto.INT64, [2, 2])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2])
+
+    node = onnx.helper.make_node('GatherND',
+                                 inputs=['data', 'indices'],
+                                 outputs=['y'])
+
+    return ([node], [x, i], [y])
+
+
 @onnx_test()
 def gathernd_batch_dims_test():
     x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [2, 2, 2])
@@ -2498,6 +2511,58 @@ def if_else_test():
     x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3])
     y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
 
+    then_out = onnx.helper.make_tensor_value_info('then_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3])
+    else_out = onnx.helper.make_tensor_value_info('else_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3])
+
+    xt = np.ones((2, 3)).astype(np.float)
+    xt_tensor = helper.make_tensor(name='xt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=xt.shape,
+                                   vals=xt.flatten().astype(np.float32))
+
+    yt = np.random.randn(2, 3).astype(np.float)
+    yt_tensor = helper.make_tensor(name='yt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=yt.shape,
+                                   vals=yt.flatten().astype(np.float32))
+
+    then_add_node = onnx.helper.make_node('Add',
+                                          inputs=['x', 'xt'],
+                                          outputs=['then_out'])
+
+    else_mul_node = onnx.helper.make_node('Mul',
+                                          inputs=['y', 'yt'],
+                                          outputs=['else_out'])
+
+    then_body = onnx.helper.make_graph([then_add_node], 'then_body', [],
+                                       [then_out])
+
+    else_body = onnx.helper.make_graph([else_mul_node], 'else_body', [],
+                                       [else_out])
+
+    cond_tensor = onnx.helper.make_tensor_value_info("cond",
+                                                     onnx.TensorProto.BOOL,
+                                                     [1])
+    res = onnx.helper.make_tensor_value_info('res', TensorProto.FLOAT, [])
+
+    node = onnx.helper.make_node('If',
+                                 inputs=['cond'],
+                                 outputs=['res'],
+                                 then_branch=then_body,
+                                 else_branch=else_body)
+
+    return ([node], [x, y, cond_tensor], [res], [xt_tensor, yt_tensor])
+
+
+@onnx_test()
+def if_else_test_inlined():
+    x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3])
+    y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
+
     then_out = onnx.helper.make_tensor_value_info('then_out',
                                                   onnx.TensorProto.FLOAT,
                                                   [2, 3])
@@ -2547,6 +2612,149 @@ def if_else_test():
     return ([node], [x, y], [res], [cond_tensor, xt_tensor, yt_tensor])
 
 
+@onnx_test()
+def if_then_else_multi_output_shapes_inlined_test():
+    x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT,
+                                           [2, 3, 1])
+    y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
+
+    then_out = onnx.helper.make_tensor_value_info('then_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3, 1])
+    then_out2 = onnx.helper.make_tensor_value_info('then_out2',
+                                                   onnx.TensorProto.FLOAT,
+                                                   [2, 3, 1])
+    else_out = onnx.helper.make_tensor_value_info('else_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3])
+
+    else_out2 = onnx.helper.make_tensor_value_info('else_out2',
+                                                   onnx.TensorProto.FLOAT,
+                                                   [2, 3])
+
+    xt = np.ones((2, 3, 1)).astype(np.float)
+    xt_tensor = helper.make_tensor(name='xt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=xt.shape,
+                                   vals=xt.flatten().astype(np.float32))
+
+    yt = np.random.randn(2, 3).astype(np.float)
+    yt_tensor = helper.make_tensor(name='yt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=yt.shape,
+                                   vals=yt.flatten().astype(np.float32))
+
+    then_add_node = onnx.helper.make_node('Add',
+                                          inputs=['x', 'xt'],
+                                          outputs=['then_out'])
+
+    then_add_node2 = onnx.helper.make_node('Add',
+                                           inputs=['x', 'x'],
+                                           outputs=['then_out2'])
+
+    else_mul_node = onnx.helper.make_node('Mul',
+                                          inputs=['y', 'yt'],
+                                          outputs=['else_out'])
+
+    else_sub_node = onnx.helper.make_node('Sub',
+                                          inputs=['y', 'yt'],
+                                          outputs=['else_out2'])
+
+    then_body = onnx.helper.make_graph([then_add_node, then_add_node2],
+                                       'then_body', [], [then_out, then_out2])
+
+    else_body = onnx.helper.make_graph([else_mul_node, else_sub_node],
+                                       'else_body', [], [else_out, else_out2])
+
+    cond = np.array([1]).astype(np.bool)
+    cond_tensor = helper.make_tensor(name="cond",
+                                     data_type=TensorProto.BOOL,
+                                     dims=cond.shape,
+                                     vals=cond.astype(bool))
+
+    res1 = onnx.helper.make_tensor_value_info('res1', TensorProto.FLOAT, [])
+    res2 = onnx.helper.make_tensor_value_info('res2', TensorProto.FLOAT, [])
+
+    node = onnx.helper.make_node('If',
+                                 inputs=['cond'],
+                                 outputs=['res1', 'res2'],
+                                 then_branch=then_body,
+                                 else_branch=else_body)
+
+    return ([node], [x, y], [res1, res2], [cond_tensor, xt_tensor, yt_tensor])
+
+
+@onnx_test()
+def if_then_else_multi_output_shapes_test():
+    x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT,
+                                           [2, 3, 1])
+    y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT,
+                                           [2, 3, 1])
+
+    then_out = onnx.helper.make_tensor_value_info('then_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3, 1])
+    then_out2 = onnx.helper.make_tensor_value_info('then_out2',
+                                                   onnx.TensorProto.FLOAT,
+                                                   [2, 3, 1])
+    else_out = onnx.helper.make_tensor_value_info('else_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3, 1])
+
+    else_out2 = onnx.helper.make_tensor_value_info('else_out2',
+                                                   onnx.TensorProto.FLOAT,
+                                                   [2, 3, 1])
+
+    xt = np.ones((2, 3, 1)).astype(np.float)
+    xt_tensor = helper.make_tensor(name='xt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=xt.shape,
+                                   vals=xt.flatten().astype(np.float32))
+
+    yt = np.random.randn(2, 3, 1).astype(np.float)
+    yt_tensor = helper.make_tensor(name='yt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=yt.shape,
+                                   vals=yt.flatten().astype(np.float32))
+
+    then_add_node = onnx.helper.make_node('Add',
+                                          inputs=['x', 'xt'],
+                                          outputs=['then_out'])
+
+    then_add_node2 = onnx.helper.make_node('Add',
+                                           inputs=['x', 'x'],
+                                           outputs=['then_out2'])
+
+    else_mul_node = onnx.helper.make_node('Mul',
+                                          inputs=['y', 'yt'],
+                                          outputs=['else_out'])
+
+    else_sub_node = onnx.helper.make_node('Sub',
+                                          inputs=['y', 'yt'],
+                                          outputs=['else_out2'])
+
+    then_body = onnx.helper.make_graph([then_add_node, then_add_node2],
+                                       'then_body', [], [then_out, then_out2])
+
+    else_body = onnx.helper.make_graph([else_mul_node, else_sub_node],
+                                       'else_body', [], [else_out, else_out2])
+
+    cond_tensor = onnx.helper.make_tensor_value_info("cond",
+                                                     onnx.TensorProto.BOOL,
+                                                     [1])
+
+    res1 = onnx.helper.make_tensor_value_info('res1', TensorProto.FLOAT, [])
+    res2 = onnx.helper.make_tensor_value_info('res2', TensorProto.FLOAT, [])
+
+    node = onnx.helper.make_node('If',
+                                 inputs=['cond'],
+                                 outputs=['res1', 'res2'],
+                                 then_branch=then_body,
+                                 else_branch=else_body)
+
+    return ([node], [x, y, cond_tensor], [res1, res2], [xt_tensor, yt_tensor])
+
+
 @onnx_test()
 def if_literal_test():
     then_out = onnx.helper.make_tensor_value_info('then_out',
@@ -2807,6 +3015,59 @@ def if_then_test():
     x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3])
     y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
 
+    then_out = onnx.helper.make_tensor_value_info('then_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3])
+    else_out = onnx.helper.make_tensor_value_info('else_out',
+                                                  onnx.TensorProto.FLOAT,
+                                                  [2, 3])
+
+    xt = np.ones((2, 3)).astype(np.float)
+    xt_tensor = helper.make_tensor(name='xt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=xt.shape,
+                                   vals=xt.flatten().astype(np.float32))
+
+    yt = np.random.randn(2, 3).astype(np.float)
+    yt_tensor = helper.make_tensor(name='yt',
+                                   data_type=TensorProto.FLOAT,
+                                   dims=yt.shape,
+                                   vals=yt.flatten().astype(np.float32))
+
+    then_add_node = onnx.helper.make_node('Add',
+                                          inputs=['x', 'xt'],
+                                          outputs=['then_out'])
+
+    else_mul_node = onnx.helper.make_node('Mul',
+                                          inputs=['y', 'yt'],
+                                          outputs=['else_out'])
+
+    then_body = onnx.helper.make_graph([then_add_node], 'then_body', [],
+                                       [then_out])
+
+    else_body = onnx.helper.make_graph([else_mul_node], 'else_body', [],
+                                       [else_out])
+
+    cond_tensor = onnx.helper.make_tensor_value_info("cond",
+                                                     onnx.TensorProto.BOOL,
+                                                     [1])
+
+    res = onnx.helper.make_tensor_value_info('res', TensorProto.FLOAT, [])
+
+    node = onnx.helper.make_node('If',
+                                 inputs=['cond'],
+                                 outputs=['res'],
+                                 then_branch=then_body,
+                                 else_branch=else_body)
+
+    return ([node], [x, y, cond_tensor], [res], [xt_tensor, yt_tensor])
+
+
+@onnx_test()
+def if_then_test_inlined():
+    x = onnx.helper.make_tensor_value_info('x', onnx.TensorProto.FLOAT, [2, 3])
+    y = onnx.helper.make_tensor_value_info('y', onnx.TensorProto.FLOAT, [2, 3])
+
     then_out = onnx.helper.make_tensor_value_info('then_out',
                                                   onnx.TensorProto.FLOAT,
                                                   [2, 3])
@@ -5707,6 +5968,24 @@ def scatternd_test():
     return ([node], [data, indices, updates], [output])
 
 
+@onnx_test()
+def scatternd_dyn_test():
+    data = helper.make_tensor_value_info('data', TensorProto.FLOAT,
+                                         [None, 2, 2])
+    indices = helper.make_tensor_value_info('indices', TensorProto.INT64,
+                                            [None, 1, 2])
+    updates = helper.make_tensor_value_info('updates', TensorProto.FLOAT,
+                                            [None, 1, 2])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
+                                           [None, 2, 2])
+
+    node = onnx.helper.make_node('ScatterND',
+                                 inputs=['data', 'indices', 'updates'],
+                                 outputs=['output'])
+
+    return ([node], [data, indices, updates], [output])
+
+
 @onnx_test()
 def selu_test():
     x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [2, 3])