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

src/include/migraphx/iterator.hpp

@@ -31,9 +31,9 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
 template <class Iterator, class EndIterator>
-auto is_end(rank<2>, Iterator it, EndIterator) -> decltype(!it._M_dereferenceable())
+auto is_end(rank<2>, Iterator it, EndIterator) -> decltype(not it._M_dereferenceable())
 {
-    return !it._M_dereferenceable();
+    return not it._M_dereferenceable();
 }
 
 template <class Iterator, class EndIterator>

src/include/migraphx/make_op.hpp

@@ -27,6 +27,8 @@
 #include <migraphx/config.hpp>
 #include <migraphx/operation.hpp>
 #include <migraphx/value.hpp>
+#include <migraphx/json.hpp>
+#include <migraphx/convert_to_json.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -46,6 +48,8 @@ operation make_op(const std::string& name, const Value& v)
     return make_op_from_value(name, v);
 }
 
+operation make_json_op(const std::string& name, const std::string& s);
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/include/migraphx/marker.hpp

@@ -181,7 +181,7 @@ struct marker
         template <typename PrivateDetailTypeErasedU = PrivateDetailTypeErasedT>
         private_detail_te_handle_type(
             PrivateDetailTypeErasedT value,
-            typename std::enable_if<!std::is_reference<PrivateDetailTypeErasedU>::value,
+            typename std::enable_if<not std::is_reference<PrivateDetailTypeErasedU>::value,
                                     int>::type* = nullptr) noexcept
             : private_detail_te_value(std::move(value))
         {
@@ -233,7 +233,7 @@ struct marker
     private_detail_te_handle_base_type& private_detail_te_get_handle()
     {
         assert(private_detail_te_handle_mem_var != nullptr);
-        if(!private_detail_te_handle_mem_var.unique())
+        if(not private_detail_te_handle_mem_var.unique())
             private_detail_te_handle_mem_var = private_detail_te_handle_mem_var->clone();
         return *private_detail_te_handle_mem_var;
     }

src/include/migraphx/match/gelu_erf.hpp

@@ -38,11 +38,11 @@ struct gelu_erf_matcher
     F f;
     auto erf_fn() const
     {
-        return f("erf")(
-            used_once(),
-            arg(0)(used_once(),
-                   f("mul")(either_arg(0, 1)(none_of(has_value(M_SQRT1_2, 1e-3)).bind("x"),
-                                             has_value(M_SQRT1_2, 1e-3)))));
+        auto mul_1_sqrt_2 = f("mul")(either_arg(0, 1)(none_of(has_value(M_SQRT1_2, 1e-3)).bind("x"),
+                                                      has_value(M_SQRT1_2, 1e-3)));
+        auto div_sqrt_2 =
+            f("div")(args(none_of(has_value(M_SQRT2, 1e-3)).bind("x"), has_value(M_SQRT2, 1e-3)));
+        return f("erf")(used_once(), arg(0)(used_once(), any_of(mul_1_sqrt_2, div_sqrt_2)));
     }
 
     auto add_erf() const

src/include/migraphx/match/layernorm.hpp

@@ -50,8 +50,8 @@ struct layernorm_matcher
     {
         return f("div")(arg(0)(x_minus_mean()),
 
-                        arg(1)(skip_broadcasts(f("sqrt")(
-                            arg(0)(f("add")(either_arg(0, 1)(variance(), has_value(1e-12f))))))));
+                        arg(1)(skip_broadcasts(f("sqrt")(arg(0)(
+                            f("add")(either_arg(0, 1)(variance(), is_constant().bind("eps"))))))));
     }
 
     auto matcher() const { return layernorm_onnx(); }

src/include/migraphx/matcher.hpp

@@ -564,6 +564,11 @@ MIGRAPHX_BASIC_MATCHER(is_unused, const matcher_context& ctx, instruction_ref in
     return nullopt;
 }
 
+MIGRAPHX_PRED_MATCHER(broadcast, instruction_ref ins)
+{
+    return contains({"broadcast", "multibroadcast"}, ins->name());
+}
+
 template <class... Ms>
 auto skip(Ms... ms)
 {
@@ -813,8 +818,7 @@ inline auto has_attribute(const std::string& name)
 template <class... Ms>
 auto pointwise(Ms... ms)
 {
-    return match::has_attribute("pointwise")(match::any_of(match::nargs(1), match::nargs(2)),
-                                             ms...);
+    return match::has_attribute("pointwise")(ms...);
 }
 
 } // namespace match

src/include/migraphx/module.hpp

@@ -219,7 +219,7 @@ struct module
 
     friend std::ostream& operator<<(std::ostream& os, const module& m);
     friend bool operator==(const module& x, const module& y);
-    friend bool operator!=(const module& x, const module& y) { return !(x == y); }
+    friend bool operator!=(const module& x, const module& y) { return not(x == y); }
 
     private:
     void assign(const module& m);

src/include/migraphx/onnx.hpp

@@ -35,17 +35,13 @@ struct onnx_options
 {
     /// Old way to set default fixed dimension size
     std::size_t default_dim_value = 0;
-    /*!
-     * Default dynamic dimension size (if both default_dim_value and default_dyn_dim_value
-     * set parser throws)
-     */
+    /// Default dynamic dimension size (if both default_dim_value and default_dyn_dim_value set
+    /// parser throws)
     shape::dynamic_dimension default_dyn_dim_value = {1, 1, 0};
     /// Explicitly specify the dims of an input
     std::unordered_map<std::string, std::vector<std::size_t>> map_input_dims = {};
-    /*!
-     * Explicitly specify dynamic dims of an input (if both map_input_dims and
-     * map_dyn_input_dims set parser throws)
-     */
+    /// Explicitly specify dynamic dims of an input (if both map_input_dims and map_dyn_input_dims
+    /// set parser throws)
     std::unordered_map<std::string, std::vector<shape::dynamic_dimension>> map_dyn_input_dims = {};
     /// Continue parsing onnx file if an unknown operator is found
     bool skip_unknown_operators = false;
@@ -53,6 +49,8 @@ struct onnx_options
     bool print_program_on_error = false;
     /// Max iter num for the loop operator
     int64_t max_loop_iterations = 10;
+    /// Use dynamic output for operators when available
+    bool use_dyn_output = false;
 };
 
 /// Create a program from an onnx file

src/include/migraphx/op/binary.hpp

@@ -28,6 +28,7 @@
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/value.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -60,10 +61,19 @@ struct binary : op_name<Derived>
     value attributes() const { return base_attributes(); }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, static_cast<const Derived&>(*this)}.has(2).same_type().same_dims();
+        check_shapes{inputs, static_cast<const Derived&>(*this), true}
+            .has(2)
+            .same_type()
+            .same_dims();
         auto s0 = inputs.at(0);
         auto s1 = inputs.at(1);
-        if(s0 == s1 and s0.packed())
+        if(s0.dynamic() or s1.dynamic())
+        {
+            if(s0 == s1)
+                return s0;
+            MIGRAPHX_THROW("BINARY: " + point_function() + ": fixed-dyn shape for inputs");
+        }
+        else if(s0 == s1 and s0.packed())
         {
             return s0;
         }
@@ -81,9 +91,9 @@ struct binary : op_name<Derived>
         }
     }
 
-    argument compute(const shape& output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
+        argument result{dyn_out.computed_shape};
         visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
             std::transform(input1.begin(),
                            input1.end(),

src/include/migraphx/op/broadcast.hpp

@@ -27,23 +27,30 @@
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
-/// The broadcast operator performs the numpy-style broadcasting of an axis of a given tensor. This
-/// is achieved primarily by setting the stride of the broadcasted axis to zero. Linear indicies are
-/// computed from multi-indicies by computing the inner product on the multi-index with the strides.
-/// For example, if we have a tensor A(2,3) it has lengths of (2,3) and strides of (3,1). If we want
-/// to compute the linear offset that corresponds to the element on the 2nd row (i = 1) and 3rd
-/// column (j = 2), we compute the following inner product (1,2) dot (3, 1) = 1*3 + 2*1 = 5. It is
-/// obvious from there that we can negate the effects of a given axis by setting the stride of that
-/// axis to zero.
+/**
+ * 1 input version:
+ * Broadcasts a tensor from the original shape to the broadcast_lens by setting the stride of
+ * broadcasted dimensions to zero. `axis` attribute for a 1D input shape is the output dimension
+ * that stays the same. ex: broadcasting shape [1024] -> [4, 1024, 3] has axis = 1 For higher rank
+ * input shapes, axis is an offset parameter for the broadcasting. Such that this operator would
+ * work in the opposite direction of NumPy broadcasting. ex: broadcasting shape [2, 2] -> [2, 2, 3]
+ * with axis = 0
+ *
+ * 2 input version:
+ * Broadcast the first input 1D shape into the second input shape based on the axis parameter.
+ * Handles broadcasting a 1D static shape into a higher rank dynamic shape.
+ * broadcast_lens is not used
+ */
 struct broadcast
 {
-    uint64_t axis = 0;
-    std::vector<std::size_t> broadcast_lens;
+    uint64_t axis                           = 0;
+    std::vector<std::size_t> broadcast_lens = {};
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
@@ -54,37 +61,88 @@ struct broadcast
     std::string name() const { return "broadcast"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        auto input = inputs.at(0);
-        auto t     = input.type();
-
-        std::vector<size_t> bcast_strides(broadcast_lens.size(), 0);
-        // the broacast op is deprecated now, so not handling the negative
-        // value of axis anymore
-        if(axis >= broadcast_lens.size())
+        check_shapes{inputs, *this, true}.has(1, 2);
+        auto s0 = inputs.at(0);
+        auto t  = s0.type();
+        if(inputs.size() == 1)
         {
-            MIGRAPHX_THROW("BROADCAST : axis is out of range");
-        }
+            // the ONNX broadcast op is deprecated now, so not handling the negative
+            // value of axis anymore
+            if(axis >= broadcast_lens.size())
+            {
+                MIGRAPHX_THROW("BROADCAST : axis " + migraphx::to_string(axis) +
+                               " is out of range");
+            }
+            if(broadcast_lens.size() - axis < s0.lens().size())
+            {
+                MIGRAPHX_THROW("BROADCAST: (broadcast ndims - axis) is less than s0 ndims");
+            }
+            if(not std::equal(s0.lens().begin(), s0.lens().end(), broadcast_lens.begin() + axis))
+            {
+                MIGRAPHX_THROW("BROADCAST: when broadcasting, succeeding sizes must match");
+            }
 
-        if(broadcast_lens.size() - axis < input.lens().size())
-        {
-            MIGRAPHX_THROW("BROADCAST: (broadcast ndims - axis) is less than input ndims");
+            std::vector<size_t> bcast_strides(broadcast_lens.size(), 0);
+            std::copy(s0.strides().begin(), s0.strides().end(), bcast_strides.begin() + axis);
+            shape output{t, broadcast_lens, std::move(bcast_strides)};
+            if(output.elements() < s0.elements())
+            {
+                // don't think this can occur?
+                MIGRAPHX_THROW("BROADCAST: output size must be greater than or equal to s0 size");
+            }
+            return output;
         }
-
-        if(!std::equal(input.lens().begin(), input.lens().end(), broadcast_lens.begin() + axis))
+        else
         {
-            MIGRAPHX_THROW("BROADCAST: when broadcasting, succeeding sizes must match");
-        }
-        std::copy(input.strides().begin(), input.strides().end(), bcast_strides.begin() + axis);
+            // two inputs
+            auto s1 = inputs.at(1);
+            if(s0.dynamic())
+            {
+                MIGRAPHX_THROW("BROADCAST_2in: s0 is a dynamic shape, does not handle broadcasting "
+                               "a dynamic shape");
+            }
+            if(s0.ndim() != 1)
+            {
+                MIGRAPHX_THROW("BROADCAST_2in: s0 has ndim " + migraphx::to_string(s0.ndim()) +
+                               ", only handle ndim = 1");
+            }
+            if(axis >= s1.ndim())
+            {
+                MIGRAPHX_THROW("BROADCAST_2in: axis " + migraphx::to_string(axis) +
+                               " is out of range");
+            }
+            if(s1.dynamic())
+            {
+                s0 = s0.to_dynamic();
+                if(s0.dyn_dims()[0] != s1.dyn_dims()[axis])
+                {
+                    MIGRAPHX_THROW("BROADCAST_2in: s0 length doesn't match with dynamic s1 axis "
+                                   "dimension length (" +
+                                   migraphx::to_string(s0.dyn_dims()[0]) +
+                                   " != " + migraphx::to_string(s1.dyn_dims()[axis]) + ")");
+                }
+                return s1;
+            }
 
-        shape output{t, broadcast_lens, std::move(bcast_strides)};
-        if(output.elements() < input.elements())
-            MIGRAPHX_THROW("BROADCAST: output size must be greater than or equal to input size");
-        return output;
+            if(s0.lens()[0] != s1.lens()[axis])
+            {
+                MIGRAPHX_THROW("BROADCAST_2in: s0 length doesn't match with static s1 axis "
+                               "dimension length (" +
+                               migraphx::to_string(s0.dyn_dims()[0]) +
+                               " != " + migraphx::to_string(s1.dyn_dims()[axis]) + ")");
+            }
+            std::vector<size_t> bcast_strides(s1.ndim(), 0);
+            std::copy(s0.strides().begin(), s0.strides().end(), bcast_strides.begin() + axis);
+            shape output{t, s1.lens(), std::move(bcast_strides)};
+            return output;
+        }
     }
-    argument compute(shape output_shape, std::vector<argument> args) const
+
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        return args[0].reshape(output_shape);
+        return args[0].reshape(dyn_out.computed_shape);
     }
+
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };
 

src/include/migraphx/op/common.hpp

@@ -33,11 +33,11 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+// Padding mode is default_ for fixed shape padding.
+// same_lower and same_upper used for dynamic padding.
 enum padding_mode_t
 {
     default_, // NOLINT
-    same,
-    valid,
     same_lower,
     same_upper
 };

src/include/migraphx/op/concat.hpp

@@ -86,7 +86,7 @@ struct concat
         {
             if(l != axis)
             {
-                if(!std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
+                if(not std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
                        return s.lens()[l] == first_shape_lens[l];
                    }))
                 {

src/include/migraphx/op/convert.hpp

@@ -44,8 +44,16 @@ struct convert : unary<convert>
 
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        return {target_type, inputs.at(0).lens(), inputs.at(0).strides()};
+        check_shapes{inputs, *this, true}.has(1);
+        auto input = inputs.at(0);
+        if(input.dynamic())
+        {
+            return {target_type, input.dyn_dims()};
+        }
+        else
+        {
+            return {target_type, input.lens(), input.strides()};
+        }
     }
 
     std::string point_op() const

src/include/migraphx/op/convolution.hpp

@@ -41,9 +41,8 @@ struct convolution
     std::vector<std::size_t> stride   = {1, 1};
     std::vector<std::size_t> dilation = {1, 1};
 
-    int group                      = 1;
-    padding_mode_t padding_mode    = default_;
-    bool use_dynamic_same_auto_pad = false;
+    int group                   = 1;
+    padding_mode_t padding_mode = default_;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
@@ -52,16 +51,15 @@ struct convolution
                     f(self.stride, "stride"),
                     f(self.dilation, "dilation"),
                     f(self.group, "group"),
-                    f(self.padding_mode, "padding_mode"),
-                    f(self.use_dynamic_same_auto_pad, "use_dynamic_same_auto_pad"));
+                    f(self.padding_mode, "padding_mode"));
     }
 
     std::string name() const { return "convolution"; }
 
     void check_attribute_size() const
     {
-        if(not((padding.size() == stride.size() or (padding.size() / 2) == stride.size()) and
-               stride.size() == dilation.size()))
+        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
+           stride.size() != dilation.size())
         {
             MIGRAPHX_THROW("CONVOLUTION: inconsistent attribute sizes");
         }
@@ -76,7 +74,8 @@ struct convolution
         // num of dims of input and attribute should match
         const auto input_size   = inputs[0].max_lens().size();
         const auto padding_size = padding.size();
-        if(not(input_size == padding_size / 2 + 2 or input_size == padding_size + 2))
+
+        if(input_size != padding_size / 2 + 2 && input_size != padding_size + 2)
         {
             MIGRAPHX_THROW("CONVOLUTION: input and attribute size mismatch!");
         }
@@ -93,13 +92,6 @@ struct convolution
            x_shape.lens().at(1) != (w_shape.lens().at(1) * group))
             MIGRAPHX_THROW("CONVOLUTION: mismatched channel numbers");
 
-        std::vector<op::padding_mode_t> dyn_pad_modes = {op::padding_mode_t::same_upper,
-                                                         op::padding_mode_t::same_lower};
-        if(use_dynamic_same_auto_pad and not contains(dyn_pad_modes, padding_mode))
-        {
-            MIGRAPHX_THROW("CONVOLUTION: use_dynamic_same_auto_pad set with invalid padding mode");
-        }
-
         if(x_shape.dynamic() or w_shape.dynamic())
         {
             return dynamic_compute_shape(x_shape, w_shape);
@@ -161,7 +153,7 @@ struct convolution
         dynamic_shape_push_back(w_shape);
 
         const size_t num_spatial_dims = x_shape.max_lens().size() - 2;
-        if(use_dynamic_same_auto_pad)
+        if(padding_mode != default_)
         {
             for(std::size_t i = 0; i < num_spatial_dims; ++i)
             {

src/include/migraphx/op/deconvolution.hpp

@@ -61,8 +61,8 @@ struct deconvolution
 
     void check_attribute_size() const
     {
-        if(not((padding.size() == stride.size() or (padding.size() / 2) == stride.size()) and
-               stride.size() == dilation.size()))
+        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
+           stride.size() != dilation.size())
         {
             MIGRAPHX_THROW("deconvolution: inconsistent attribute sizes");
         }

src/include/migraphx/op/dot.hpp

@@ -43,13 +43,14 @@ struct dot
         const shape& b = inputs.at(1);
         auto t         = a.type();
 
-        if(!std::all_of(inputs.begin(), inputs.end(), [](auto s) { return s.lens().size() >= 2; }))
+        if(not std::all_of(
+               inputs.begin(), inputs.end(), [](auto s) { return s.lens().size() >= 2; }))
         {
             MIGRAPHX_THROW("DOT: dot only accept 2 or more dims operands");
         }
 
         // only handle the case that the batch size of a and b are the same
-        if(!std::equal(
+        if(not std::equal(
                a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2, b.lens().rend()))
         {
             MIGRAPHX_THROW("DOT: batch size of A and B mismatch: {" + to_string_range(a.lens()) +

src/include/migraphx/op/elu.hpp

@@ -32,14 +32,13 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
-struct elu
+struct elu : unary<elu>
 {
-    std::string name() const { return "elu"; }
     float alpha = 1;
-    shape compute_shape(std::vector<shape> inputs) const
+
+    std::string point_op() const
     {
-        check_shapes{inputs, *this}.has(1);
-        return inputs.front();
+        return "${function:where}(${0} > 0, ${0}, ${alpha} * (${function:exp}(${0}) - 1))";
     }
 
     template <class Self, class F>
@@ -47,6 +46,11 @@ struct elu
     {
         return pack(f(self.alpha, "alpha"));
     }
+
+    auto apply() const
+    {
+        return [&](auto x) { return x > 0 ? x : alpha * std::expm1(x); };
+    }
 };
 
 } // namespace op

src/include/migraphx/op/fmod.hpp

@@ -24,17 +24,8 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_FMOD_HPP
 #define MIGRAPHX_GUARD_OPERATORS_FMOD_HPP
 
-#include <array>
 #include <migraphx/op/binary.hpp>
-#include <migraphx/check_shapes.hpp>
-#include <migraphx/stringutils.hpp>
-#include <migraphx/streamutils.hpp>
-#include <migraphx/literal.hpp>
-#include <migraphx/shape_for_each.hpp>
-#include <migraphx/config.hpp>
 #include <cmath>
-#include <utility>
-#include <type_traits>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -49,7 +40,6 @@ struct fmod : binary<fmod>
         a["commutative"] = false;
         return a;
     }
-    std::string point_function() const { return "fmod"; }
     auto apply() const
     {
         return [](auto x, auto y) { return std::fmod(x, y); };

src/include/migraphx/op/gather.hpp

@@ -65,7 +65,7 @@ struct gather
         auto lens = inputs[0].lens();
         auto type = inputs[0].type();
         lens.erase(lens.begin() + axis);
-        if(!inputs[1].scalar())
+        if(not inputs[1].scalar())
         {
             auto ind_lens = inputs[1].lens();
             lens.insert(lens.begin() + axis, ind_lens.begin(), ind_lens.end());

src/include/migraphx/op/leaky_relu.hpp

@@ -26,12 +26,13 @@
 
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/op/unary.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
-struct leaky_relu
+struct leaky_relu : unary<leaky_relu>
 {
     float alpha = 0.01;
 
@@ -41,11 +42,13 @@ struct leaky_relu
         return pack(f(self.alpha, "alpha"));
     }
 
+    std::string point_op() const { return "${function:where}(${0} > 0, ${0}, ${alpha} * ${0})"; }
+
     std::string name() const { return "leaky_relu"; }
-    shape compute_shape(std::vector<shape> inputs) const
+
+    auto apply() const
     {
-        check_shapes{inputs, *this}.has(1);
-        return inputs.front();
+        return [&](auto x) { return x > 0 ? x : x * alpha; };
     }
 };