Merge branch 'dyn_batch_pass' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_test_runner

src/compile_src.cpp

@@ -70,9 +70,6 @@ std::vector<char> src_compiler::compile(const std::vector<src_file>& srcs) const
     if(not fs::exists(out_path))
         MIGRAPHX_THROW("Output file missing: " + out);
 
-    if(process)
-        out_path = process(out_path);
-
     return read_buffer(out_path.string());
 }
 

src/driver/main.cpp

@@ -326,8 +326,7 @@ struct compiler
     loader l;
     program_params parameters;
     compiler_target ct;
-    bool offload_copy  = false;
-    bool fast_math     = true;
+    compile_options co;
     precision quantize = precision::fp32;
 
     std::vector<std::string> fill0;
@@ -337,19 +336,26 @@ struct compiler
         l.parse(ap);
         parameters.parse(ap);
         ct.parse(ap);
-        ap(offload_copy,
+        ap(co.offload_copy,
            {"--enable-offload-copy"},
            ap.help("Enable implicit offload copying"),
            ap.set_value(true));
-        ap(fast_math,
+        ap(co.fast_math,
            {"--disable-fast-math"},
            ap.help("Disable fast math optimization"),
            ap.set_value(false));
+        ap(co.exhaustive_tune,
+           {"--exhaustive-tune"},
+           ap.help("Exhastively search for best tuning parameters for kernels"),
+           ap.set_value(true));
         ap(quantize, {"--fp16"}, ap.help("Quantize for fp16"), ap.set_value(precision::fp16));
         ap(quantize, {"--int8"}, ap.help("Quantize for int8"), ap.set_value(precision::int8));
     }
 
-    auto params(const program& p) { return parameters.generate(p, ct.get_target(), offload_copy); }
+    auto params(const program& p)
+    {
+        return parameters.generate(p, ct.get_target(), co.offload_copy);
+    }
 
     program compile()
     {
@@ -366,10 +372,7 @@ struct compiler
         {
             quantize_int8(p, t, {params(p)});
         }
-        compile_options options;
-        options.offload_copy = offload_copy;
-        options.fast_math    = fast_math;
-        p.compile(t, options);
+        p.compile(t, co);
         l.save(p);
         return p;
     }
@@ -402,60 +405,41 @@ struct params : command<params>
 
 struct verify : command<verify>
 {
-    loader l;
-    program_params parameters;
-    compiler_target ct;
+    compiler c;
     double tolerance     = 80;
     bool per_instruction = false;
     bool reduce          = false;
-    bool offload_copy    = false;
-    bool fast_math       = true;
-    precision quantize   = precision::fp32;
     void parse(argument_parser& ap)
     {
-        l.parse(ap);
-        parameters.parse(ap);
-        ct.parse(ap);
-        ap(offload_copy,
-           {"--enable-offload-copy"},
-           ap.help("Enable implicit offload copying"),
-           ap.set_value(true));
-        ap(fast_math,
-           {"--disable-fast-math"},
-           ap.help("Disable fast math optimization"),
-           ap.set_value(false));
+        c.parse(ap);
         ap(tolerance, {"--tolerance"}, ap.help("Tolerance for errors"));
         ap(per_instruction,
            {"-i", "--per-instruction"},
            ap.help("Verify each instruction"),
            ap.set_value(true));
         ap(reduce, {"-r", "--reduce"}, ap.help("Reduce program and verify"), ap.set_value(true));
-        ap(quantize, {"--fp16"}, ap.help("Quantize for fp16"), ap.set_value(precision::fp16));
     }
 
     void run()
     {
-        auto p = l.load();
-        l.save(p);
+        auto p = c.l.load();
+        c.l.save(p);
         std::cout << p << std::endl;
 
-        compile_options options;
-        options.offload_copy = offload_copy;
-        options.fast_math    = fast_math;
-        auto t               = ct.get_target();
-        auto m               = parameters.generate(p, t, true);
+        auto t = c.ct.get_target();
+        auto m = c.parameters.generate(p, t, true);
 
         if(per_instruction)
         {
-            verify_instructions(p, t, options, quantize, tolerance);
+            verify_instructions(p, t, c.co, c.quantize, tolerance);
         }
         else if(reduce)
         {
-            verify_reduced_program(p, t, options, quantize, m, tolerance);
+            verify_reduced_program(p, t, c.co, c.quantize, m, tolerance);
         }
         else
         {
-            verify_program(l.file, p, t, options, quantize, m, tolerance);
+            verify_program(c.l.file, p, t, c.co, c.quantize, m, tolerance);
         }
     }
 };

src/driver/perf.cpp

@@ -108,8 +108,6 @@ target get_target(bool gpu)
         return make_target("cpu");
 }
 
-void compile_program(program& p, bool gpu) { p.compile(get_target(gpu)); }
-
 } // namespace  MIGRAPHX_INLINE_NS
 } // namespace driver
 } // namespace migraphx

src/driver/perf.hpp

@@ -37,7 +37,6 @@ parameter_map create_param_map(const program& p, const target& t, bool offload =
 parameter_map fill_param_map(parameter_map& m, const program& p, bool gpu);
 parameter_map create_param_map(const program& p, bool gpu = true);
 target get_target(bool gpu);
-void compile_program(program& p, bool gpu = true);
 
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace driver

src/include/migraphx/check_shapes.hpp

@@ -87,7 +87,7 @@ struct check_shapes
     }
 
     /*!
-     * Check if the number of shape objects is equal to atleast one of the
+     * Require the number of shape objects to equal to one of the
      * given sizes.
      * \param ns template parameter pack of sizes to check against
      */
@@ -100,6 +100,23 @@ struct check_shapes
         return *this;
     }
 
+    /*!
+     * Require the number of shape objects to equal at least a given amount.  Use this
+     * method for ops that can take any number (variadic) of inputs.
+     * \param n min. number of shapes
+     */
+    const check_shapes& has_at_least(std::size_t n) const
+    {
+        if(this->size() < n)
+            MIGRAPHX_THROW(prefix() + "Wrong number of arguments: expected at least " +
+                           to_string(n) + " but given " + std::to_string(size()));
+        return *this;
+    }
+
+    /*!
+     * Require all shapes to have the same number of elements.
+     * \param n  number of
+     */
     const check_shapes& nelements(std::size_t n) const
     {
         if(not this->all_of([&](const shape& s) { return s.elements() == n; }))

src/include/migraphx/compile_options.hpp

@@ -32,8 +32,9 @@ inline namespace MIGRAPHX_INLINE_NS {
 
 struct compile_options
 {
-    bool offload_copy = false;
-    bool fast_math    = true;
+    bool offload_copy    = false;
+    bool fast_math       = true;
+    bool exhaustive_tune = false;
     tracer trace{};
 };
 

src/include/migraphx/context.hpp

@@ -66,6 +66,7 @@ any_ptr get_queue_context(T&)
 {
     return {};
 }
+
 template <class T>
 void wait_for_context(T&, any_ptr)
 {
@@ -302,7 +303,7 @@ struct context
             PrivateDetailTypeErasedT value,
             typename std::enable_if<not std::is_reference<PrivateDetailTypeErasedU>::value,
                                     int>::type* = nullptr) noexcept
-            : private_detail_te_value(value)
+            : private_detail_te_value(std::move(value))
         {
         }
 
@@ -412,6 +413,7 @@ inline const ValueType& any_cast(const context& x)
 #endif
 
 inline void migraphx_to_value(value& v, const context& ctx) { v = ctx.to_value(); }
+
 inline void migraphx_from_value(const value& v, context& ctx) { ctx.from_value(v); }
 
 #endif

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/match/layernorm.hpp

@@ -36,22 +36,46 @@ template <class F>
 struct layernorm_matcher
 {
     F f;
+
+    auto last_axis() const
+    {
+        return make_basic_pred_matcher([](instruction_ref ins) {
+            auto v = ins->get_operator().to_value();
+            if(not v.contains("axes"))
+                return false;
+            auto axes = v["axes"].to_vector<std::size_t>();
+            if(axes.size() != 1)
+                return false;
+            return axes.front() == ins->inputs().front()->get_shape().lens().size() - 1;
+        });
+    }
+
+    auto reduce_mean() const { return f("reduce_mean")(last_axis()); }
+
     auto x_minus_mean() const
     {
-        return f("sub")(arg(0)(any().bind("x")), arg(1)(skip_broadcasts(f("reduce_mean"))));
+        return f("sub")(arg(0)(any().bind("x")), arg(1)(skip_broadcasts(reduce_mean())));
     }
 
     auto variance() const
     {
-        return f("reduce_mean")(arg(0)(f("pow")(arg(0)(x_minus_mean()), arg(1)(has_value(2.0f)))));
+        return reduce_mean()(arg(0)(any_of(
+            f("pow")(arg(0)(x_minus_mean()), arg(1)(has_value(2.0f))),
+            f("mul")(arg(0)(x_minus_mean()), arg(1)(x_minus_mean())),
+            f("sqdiff")(either_arg(0, 1)(any().bind("x"), skip_broadcasts(reduce_mean()))))));
     }
 
-    auto layernorm_onnx() const
+    auto sqrt_add_eps(const std::string& name) const
     {
-        return f("div")(arg(0)(x_minus_mean()),
+        auto add_eps = f("add")(either_arg(0, 1)(variance(), is_constant().bind("eps")));
+        return skip_broadcasts(f(name)(arg(0)(any_of(add_eps, variance()))));
+    }
 
-                        arg(1)(skip_broadcasts(f("sqrt")(arg(0)(
-                            f("add")(either_arg(0, 1)(variance(), is_constant().bind("eps"))))))));
+    auto layernorm_onnx() const
+    {
+        auto div_sqrt  = f("div")(arg(0)(x_minus_mean()), arg(1)(sqrt_add_eps("sqrt")));
+        auto mul_rsqrt = f("mul")(either_arg(0, 1)(x_minus_mean(), sqrt_add_eps("rsqrt")));
+        return any(any_of(div_sqrt, mul_rsqrt));
     }
 
     auto matcher() const { return layernorm_onnx(); }

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/module.hpp

@@ -54,6 +54,10 @@ using ins_dep_map   = std::unordered_map<instruction_ref, std::unordered_set<ins
  */
 struct module
 {
+    // used by replace_allocate pass
+    // allocate memory in this module rather than using output parmaeters
+    bool use_local_alloc = false;
+
     module(const std::string& name = "");
 
     // move constructor

src/include/migraphx/op/concat.hpp

@@ -26,6 +26,7 @@
 
 #include <array>
 #include <migraphx/check_shapes.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/streamutils.hpp>
 #include <migraphx/literal.hpp>
@@ -73,49 +74,87 @@ struct concat
         }
         return offsets;
     }
+
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        if(inputs.empty())
+        // inputs can contain 1 or more shapes (variadic).  compute_shape_op ensures there must
+        // be at least 1.
+        check_shapes{inputs, *this, true}.same_ndims().same_type();
+
+        if(std::none_of(inputs.begin(), inputs.end(), [&](const shape& s) { return s.dynamic(); }))
         {
-            MIGRAPHX_THROW("CONCAT: Number of input tensors should exceed 0");
+            // Static input shapes
+            const auto& first_shape_lens = inputs.front().lens();
+            const auto& type             = inputs.front().type();
+            for(std::size_t ll = 0; ll < first_shape_lens.size(); ll++)
+            {
+                if(ll != axis)
+                {
+                    if(not std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
+                           return s.lens()[ll] == first_shape_lens[ll];
+                       }))
+                    {
+                        MIGRAPHX_THROW("CONCAT: all input dimensions should match along axis " +
+                                       std::to_string(ll));
+                    }
+                }
+            }
+            std::size_t new_dim_axis = 0;
+            for(const auto& input : inputs)
+            {
+                const auto& lens = input.lens();
+                new_dim_axis += lens[axis];
+            }
+            std::vector<std::size_t> new_lens = first_shape_lens;
+            new_lens[axis]                    = new_dim_axis;
+            return shape::from_permutation(type, new_lens, find_permutation(inputs));
         }
-
-        const auto& first_shape_lens = inputs.front().lens();
-        const auto& type             = inputs.front().type();
-        for(std::size_t l = 0; l < first_shape_lens.size(); l++)
+        else if(std::all_of(
+                    inputs.begin(), inputs.end(), [&](const shape& s) { return s.dynamic(); }))
         {
-            if(l != axis)
+            // Dynamic input shapes
+            for(std::size_t index = 0; index < inputs[0].ndim(); index++)
             {
-                if(not std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
-                       return s.lens()[l] == first_shape_lens[l];
-                   }))
+                if(index != axis)
                 {
-                    MIGRAPHX_THROW("CONCAT: Non-axis dimensions should match");
+                    if(not std::all_of(inputs.begin(), inputs.end(), [&](const shape& s) {
+                           return s.dyn_dims()[index] == inputs[0].dyn_dims()[index];
+                       }))
+                        MIGRAPHX_THROW("CONCAT: all input dimensions should match in axis " +
+                                       std::to_string(index));
                 }
             }
+            std::size_t new_min = 0;
+            std::size_t new_max = 0;
+            for(const auto& input : inputs)
+            {
+                auto ddim = input.dyn_dims()[axis];
+                new_min += ddim.min;
+                new_max += ddim.max;
+            }
+
+            auto new_dims  = inputs[0].dyn_dims();
+            new_dims[axis] = migraphx::shape::dynamic_dimension{new_min, new_max, 0};
+            return {inputs[0].type(), new_dims};
         }
-        std::size_t new_dim_axis = 0;
-        for(const auto& input : inputs)
+        else
         {
-            const auto& lens = input.lens();
-            new_dim_axis += lens[axis];
+            MIGRAPHX_THROW("CONCAT: Cannot mix static and dynamic input shapes.");
         }
-        std::vector<std::size_t> new_lens;
-        std::copy(first_shape_lens.begin(), first_shape_lens.end(), std::back_inserter(new_lens));
-        new_lens[axis] = new_dim_axis;
-        return shape::from_permutation(type, new_lens, find_permutation(inputs));
     }
-    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};
-        std::vector<std::size_t> coffsets = compute_offsets(output_shape, args);
+        argument result{dyn_out.computed_shape};
+        std::vector<std::size_t> coffsets = compute_offsets(dyn_out.computed_shape, args);
         for(std::size_t l = 0; l < args.size(); l++)
         {
             auto argl = args[l];
             visit_all(result, argl)([&](auto output, auto input) {
-                auto slice_shape =
-                    shape{output_shape.type(), input.get_shape().lens(), output_shape.strides()};
-                auto slice = make_view(slice_shape, output.data() + coffsets[l]);
+                auto slice_shape = shape{dyn_out.computed_shape.type(),
+                                         input.get_shape().lens(),
+                                         dyn_out.computed_shape.strides()};
+                auto slice       = make_view(slice_shape, output.data() + coffsets[l]);
                 std::copy(input.begin(), input.end(), slice.begin());
             });
         }

src/include/migraphx/op/gather.hpp

@@ -26,6 +26,7 @@
 
 #include <array>
 #include <migraphx/check_shapes.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/streamutils.hpp>
 #include <migraphx/literal.hpp>
@@ -61,35 +62,59 @@ struct gather
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(2);
-        auto lens = inputs[0].lens();
-        auto type = inputs[0].type();
-        lens.erase(lens.begin() + axis);
-        if(not inputs[1].scalar())
+        check_shapes{inputs, *this, true}.has(2);
+        shape data    = inputs[0];
+        shape indices = inputs[1];
+        auto type     = data.type();
+        // If index_dims is dynamic, convert the data to dynamic too.
+        if(indices.dynamic())
         {
-            auto ind_lens = inputs[1].lens();
-            lens.insert(lens.begin() + axis, ind_lens.begin(), ind_lens.end());
+            data = data.to_dynamic();
         }
-
-        // for scalar output
-        if(lens.empty())
+        if(data.dynamic())
         {
-            return {type};
+            auto dims = data.dyn_dims();
+            dims.erase(dims.begin() + axis);
+
+            if(not indices.scalar())
+            {
+                auto index_dims = indices.to_dynamic().dyn_dims();
+                dims.insert(dims.begin() + axis, index_dims.begin(), index_dims.end());
+            }
+            return {type, dims};
         }
+        else
+        {
+            // Both data and indices are static.  indices may be scalar
+            auto lens = data.lens();
+            lens.erase(lens.begin() + axis);
 
-        return {type, lens};
+            if(not indices.scalar())
+            {
+                auto ind_lens = indices.lens();
+                lens.insert(lens.begin() + axis, ind_lens.begin(), ind_lens.end());
+            }
+
+            // for scalar output
+            if(lens.empty())
+            {
+                return {type};
+            }
+
+            return {type, 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};
         // negative axis means counting dimensions from back
         auto lens                 = args[0].get_shape().lens();
         std::size_t axis_dim_size = lens[axis];
         // max dimension in axis
         visit_all(result, args[0])([&](auto output, auto data) {
             args[1].visit([&](auto indices) {
-                if(output_shape.scalar())
+                if(dyn_out.computed_shape.scalar())
                 {
                     auto in_index = indices.front();
                     in_index      = (in_index < 0) ? in_index + axis_dim_size : in_index;

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/normalize_attribute.hpp

@@ -31,18 +31,30 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
-// different attributes
-// 1) use_input(default)/use_output
-// 2) use_rank(default)/use_len
-// 3) clip_min(default)/not_clip_min
-//   3.1) include_min(default)/exclude_min
-// 4) clip_max(default)/not_clip_max
-//   4.1) exclude_max(default)/include_max
-// 5) normalize padding
+/**
+ * `normalize_attribute` settings:
+ * Note that default options are not included as enums.
+ * 1. `use_input` (default) vs. `use_output`:
+ *  Affects the rank of the attribute.
+ *  `use_input -> lens.size()`, `use_output -> lens.size() + vec.size()`.
+ * 2. use_rank (default) vs use_len:
+ *  `use_rank` sets the max value/index of the attribute as the rank of lens.
+ *  `use_lens` sets the max value/index as the corresponding value in lens at the axes index.
+ * 3. `clip_min` vs. `not_clip_min` (default):
+ *  Clip values less than the minimum to the minimum or not.
+ * 4. `include_min` vs. `exclude_min` (default):
+ *  Include or exclude the minimum value/index for range checking and clipping.
+ * 5. `clip_max` vs. `not_clip_max` (default):
+ *  Clip values greater than the maximum or not.
+ * 6. `include_max` vs. `exclude_max` (default):
+ *  Include or exclude the maximum value/index for range checking and clipping.
+ * 7. `normalize_padding`:
+ *  To normalize the padding to `2*(pad ndim)` dimensions.
+ */
 enum class normalize_attribute
 {
-    use_len,
     use_output,
+    use_len,
     clip_max,
     clip_min,
     include_max,

src/include/migraphx/op/reverse.hpp

@@ -28,6 +28,7 @@
 #include <vector>
 #include <cmath>
 #include <utility>
+#include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
@@ -60,6 +61,7 @@ struct reverse
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
+        check_shapes{inputs, *this}.has(1);
         return inputs[0].with_lens(inputs[0].lens());
     }
 

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/include/migraphx/op/select_module.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * 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
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+#ifndef MIGRAPHX_GUARD_OPERATORS_SELECT_MODULE_HPP
+#define MIGRAPHX_GUARD_OPERATORS_SELECT_MODULE_HPP
+
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/module.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct select_module
+{
+    shape output_dyn_shapes;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.output_dyn_shapes, "output_dyn_shapes"));
+    }
+
+    std::string name() const { return "select_module"; }
+
+    shape compute_shape(const std::vector<shape>&, const std::vector<module_ref>&) const
+    {
+        return shape{output_dyn_shapes};
+    }
+
+    argument compute(const shape&,
+                     const std::vector<argument>& args,
+                     const std::vector<module_ref>& submodule_list,
+                     const std::function<std::vector<argument>(
+                         module_ref&, const std::unordered_map<std::string, argument>&)>& run) const
+    {
+        // find submodule with input parameter shapes exactly the same as the input arguments
+        // assuming arguments are in the same order as the input parameters
+        auto module_iter =
+            std::find_if(submodule_list.cbegin(), submodule_list.cend(), [&](module_ref mr) {
+                auto param_names = mr->get_parameter_names();
+                assert(param_names.size() <= args.size());
+                return std::equal(param_names.cbegin(),
+                                  param_names.cend(),
+                                  args.cbegin(),
+                                  [&](auto p_name, auto a) {
+                                      return a.get_shape() == mr->get_parameter_shape(p_name);
+                                  });
+            });
+
+        if(module_iter == submodule_list.end())
+        {
+            MIGRAPHX_THROW("SELECT_MODULE: no compatible submodules found for given input shapes");
+        }
+        auto* module_to_run = *module_iter;
+        std::unordered_map<std::string, argument> params;
+
+        // add input parameters
+        auto param_names = module_to_run->get_parameter_names();
+        assert(param_names.size() <= args.size());
+        std::transform(param_names.begin(),
+                       param_names.end(),
+                       args.begin(),
+                       std::inserter(params, params.end()),
+                       [](auto&& name, auto&& a) { return std::make_pair(name, a); });
+
+        auto results = run(module_to_run, params);
+        return argument{results};
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/slice.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
@@ -27,6 +27,7 @@
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
 
@@ -46,6 +47,10 @@ struct slice
         return pack(f(self.axes, "axes"), f(self.starts, "starts"), f(self.ends, "ends"));
     }
 
+    /**
+     * Ensure that attribute vectors axes, starts, and ends are all the same size and values are in
+     * limits.
+     */
     value attributes() const
     {
         value normalize     = value::object{};
@@ -65,14 +70,6 @@ struct slice
 
     std::string name() const { return "slice"; }
 
-    auto fix_index(const std::vector<std::size_t>& lens, std::size_t axis, int64_t index) const
-    {
-        int64_t r = std::min(index, static_cast<int64_t>(lens[axis]));
-        if(r < 0)
-            r += lens[axis];
-        return std::size_t(r);
-    }
-
     auto compute_offset(const shape& s) const
     {
         const std::vector<std::size_t>& lens    = s.lens();
@@ -83,14 +80,14 @@ struct slice
             for(std::size_t i = 0; i < axes.size(); i++)
             {
                 auto axis = axes[i];
-                offset += fix_index(lens, axis, starts[i]) * strides[axis];
+                offset += starts[i] * strides[axis];
             }
         }
         else
         {
             for(std::size_t axis = 0; axis < lens.size(); axis++)
             {
-                offset += fix_index(lens, axis, starts[axis]) * strides[axis];
+                offset += starts[axis] * strides[axis];
             }
         }
         return offset;
@@ -98,37 +95,81 @@ struct slice
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        auto input_shape        = inputs[0];
-        auto t                  = input_shape.type();
-        const auto& old_lens    = input_shape.lens();
-        const auto& old_strides = input_shape.strides();
+        check_shapes{inputs, *this, true}.has(1);
+        auto input_shape = inputs[0];
+        auto t           = input_shape.type();
 
-        if(std::any_of(
-               axes.begin(), axes.end(), [&](auto i) { return (i >= old_lens.size() and i < 0); }))
+        // TODO:  When support for dynamic shapes is added to normalize_attributes,
+        //  remove this restriction.
+        if(input_shape.dynamic() and std::any_of(axes.begin(), axes.end(), [&](auto axis) {
+               return not input_shape.dyn_dims()[axis].is_fixed();
+           }))
         {
-            MIGRAPHX_THROW("SLICE: input axis " + to_string_range(axes) + " out of range");
+            MIGRAPHX_THROW("SLICE: slicing is not allowed on non-fixed dynamic input axis ");
         }
 
-        if(starts.size() != axes.size() or axes.size() != ends.size())
+        // For a static shape, old_lens will be adjusted to a new size
+        // for those axes that are sliced.
+        // For dynamic shape, the adjusted old_lens become the new max values,
+        // while updating the old mins and opts if possible.
+        std::vector<std::size_t> new_mins;
+        std::vector<std::size_t> new_opts;
+        std::vector<std::size_t> old_lens;
+        std::vector<std::size_t> old_strides;
+        if(input_shape.dynamic())
+        {
+            old_lens = input_shape.max_lens();
+            new_mins = input_shape.min_lens();
+            new_opts = input_shape.opt_lens();
+        }
+        else
         {
-            MIGRAPHX_THROW("SLICE: inconsistent sizes");
+            old_lens = input_shape.lens();
+            // For static shape (including during eval step after a dynamic input) the strides are
+            // indexed into the pre-slice array, so they are larger than the apparent size of the
+            // resulting shape.
+            old_strides = input_shape.strides();
         }
 
         std::vector<std::size_t> new_lens = old_lens;
         for(std::size_t i = 0; i < axes.size(); i++)
         {
-            auto axis = axes[i];
-            new_lens[axis] =
-                fix_index(old_lens, axis, ends[i]) - fix_index(old_lens, axis, starts[i]);
+            auto axis            = axes[i];
+            size_t sliced_length = ends[i] - starts[i];
+            // A Numpy indexing convention: a slice size larger than the actual dimension
+            // is legal and the "ends" value is clipped to the axis size
+            new_lens[axis] = std::min(new_lens[axis], sliced_length);
+            if(input_shape.dynamic())
+            {
+                // TODO: when non-fixed shape slicing is allowed, this will be different than
+                // sliced_length, making use of TBD start/end values.
+                std::size_t sliced_min_length = ends[i] - starts[i];
+                // if the slice size is smaller than maxes but larger than mins
+                new_mins[axis] = std::min(sliced_min_length, new_mins[axis]);
+
+                auto sliced_opt_length = ends[i] - starts[i];
+                if(new_opts[axis] != 0)
+                    new_opts[axis] = sliced_opt_length;
+                if(new_opts[axis] < new_mins[axis] or new_opts[axis] > new_lens[axis])
+                    new_opts[axis] = 0;
+            }
+        }
+        if(input_shape.dynamic())
+        {
+            return shape{t, new_mins, new_lens, new_opts};
+        }
+        else
+        {
+            return shape{t, new_lens, old_strides};
         }
-        return shape{t, new_lens, old_strides};
     }
 
-    argument compute(shape output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        auto input  = args[0];
-        auto offset = compute_offset(input.get_shape()) * output_shape.type_size();
-        return {std::move(output_shape), [=] { return input.data() + offset; }};
+        auto input = args[0];
+
+        auto offset = compute_offset(input.get_shape()) * dyn_out.computed_shape.type_size();
+        return {dyn_out.computed_shape, [=] { return input.data() + offset; }};
     }
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };