Merge

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/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/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; };
     }
 };
 

src/include/migraphx/op/pooling.hpp

@@ -64,8 +64,8 @@ struct pooling
 
     void check_attribute_size() const
     {
-        if(not((padding.size() == stride.size() or (padding.size() / 2) == stride.size()) and
-               stride.size() == lengths.size()))
+        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
+           stride.size() != lengths.size())
         {
             MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
         }
@@ -83,7 +83,7 @@ struct pooling
         size_t kdims      = input_lens.size() - 2;
         auto input_size   = inputs[0].lens().size();
         auto padding_size = padding.size();
-        if(not(input_size == padding_size / 2 + 2 or input_size == padding_size + 2))
+        if(input_size != padding_size / 2 + 2 and input_size != padding_size + 2)
         {
             MIGRAPHX_THROW("POOLING: input and attribute size mismatch!");
         }

src/include/migraphx/op/quant_convolution.hpp

@@ -43,7 +43,6 @@ struct quant_convolution
 
     padding_mode_t padding_mode = default_;
     int group                   = 1;
-    bool use_dynamic_same_auto_pad = false;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
@@ -52,8 +51,7 @@ struct quant_convolution
                     f(self.stride, "stride"),
                     f(self.dilation, "dilation"),
                     f(self.padding_mode, "padding_mode"),
-                    f(self.group, "group"),
-                    f(self.use_dynamic_same_auto_pad, "use_dynamic_same_auto_pad"));
+                    f(self.group, "group"));
     }
 
     value attributes() const
@@ -65,8 +63,8 @@ struct quant_convolution
 
     void check_attribute_size() const
     {
-        if(not((padding.size() == stride.size() or (padding.size() / 2) == stride.size()) and
-               stride.size() == dilation.size()))
+        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
+           stride.size() != dilation.size())
         {
             MIGRAPHX_THROW("QUANT_CONVOLUTION: inconsistent attribute sizes");
         }

src/include/migraphx/op/unary.hpp

@@ -30,6 +30,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/value.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -62,9 +63,9 @@ struct unary : op_name<Derived>
     value attributes() const { return base_attributes(); }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, static_cast<const Derived&>(*this)}.has(1);
+        check_shapes{inputs, static_cast<const Derived&>(*this), true}.has(1);
         auto s = inputs.at(0);
-        if(s.scalar())
+        if(s.dynamic() or s.scalar())
         {
             return s;
         }
@@ -78,9 +79,9 @@ struct unary : op_name<Derived>
         }
     }
 
-    argument compute(const shape& output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
+        argument result{dyn_out.computed_shape};
         result.visit([&](auto output) {
             args[0].visit([&](auto input) {
                 std::transform(input.begin(),

src/include/migraphx/operation.hpp

@@ -32,6 +32,8 @@
 #include <utility>
 #include <unordered_map>
 #include <migraphx/reflect.hpp>
+#include <migraphx/dyn_output.hpp>
+#include <migraphx/functional.hpp>
 #include <migraphx/streamutils.hpp>
 #include <migraphx/normalize_attributes.hpp>
 #include <migraphx/argument.hpp>
@@ -199,9 +201,12 @@ auto compute_op(rank<1>,
                 context& ctx,
                 const shape& output_shape,
                 const std::vector<argument>& input)
-    -> decltype(x.compute(auto_any_cast(ctx), output_shape, input))
+    -> decltype(x.compute(auto_any_cast(ctx),
+                          make_compute_output_shape(pack(x, output_shape, input)),
+                          input))
 {
-    return x.compute(auto_any_cast(ctx), output_shape, input);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -220,9 +225,9 @@ compute_op(const T& x, context& ctx, const shape& output_shape, const std::vecto
 
 template <class T>
 auto compute_op(rank<1>, const T& x, const shape& output_shape, const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input))
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input))
 {
-    return x.compute(output_shape, input);
+    return x.compute(make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -244,9 +249,11 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -278,9 +285,17 @@ auto compute_op(rank<4>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(auto_any_cast(ctx), output, inputs, module_args, f))
+                F f) -> decltype(x.compute(auto_any_cast(ctx),
+                                           make_compute_output_shape(pack(x, output, inputs)),
+                                           inputs,
+                                           module_args,
+                                           f))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs, module_args, f);
+    return x.compute(auto_any_cast(ctx),
+                     make_compute_output_shape(pack(x, output, inputs)),
+                     inputs,
+                     module_args,
+                     f);
 }
 
 template <class T, class F>
@@ -290,9 +305,11 @@ auto compute_op(rank<3>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -302,9 +319,10 @@ auto compute_op(rank<2>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(output, inputs))
+                F)
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs))
 {
-    return x.compute(output, inputs);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -314,9 +332,12 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(auto_any_cast(ctx), output, inputs))
+                F) -> decltype(x.compute(auto_any_cast(ctx),
+                                         make_compute_output_shape(pack(x, output, inputs)),
+                                         inputs))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -348,7 +369,8 @@ auto is_context_free_op(rank<1>,
                         const T& x,
                         const shape& output_shape,
                         const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input), std::true_type{});
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input),
+                std::true_type{});
 
 template <class T>
 auto is_context_free_op(rank<0>, const T&, const shape&, const std::vector<argument>&)

src/include/migraphx/operators.hpp

@@ -35,7 +35,6 @@
 #include <migraphx/op/as_shape.hpp>
 #include <migraphx/op/atan.hpp>
 #include <migraphx/op/atanh.hpp>
-#include <migraphx/op/batch_norm_inference.hpp>
 #include <migraphx/op/binary.hpp>
 #include <migraphx/op/broadcast.hpp>
 #include <migraphx/op/capture.hpp>

src/include/migraphx/pad_calc.hpp

@@ -24,9 +24,10 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
 #define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
 
-#include <migraphx/config.hpp>
 #include <cstdint>
 #include <vector>
+#include <migraphx/config.hpp>
+#include <migraphx/shape.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -42,18 +43,21 @@ void calculate_padding(int64_t idx,
 /*!
  * Calculate the padding for auto_padding. Used for dynamic shapes
  * where the padding calculation must be done at evaluation time.
- * \param tensor_lens input tensor image shape
- * \param k_lens weights kernel shape
- * \param strides strides for the kernel
- * \param dilations dilations for the kernel
- * \param use_upper put odd padding on upper or lower side
  * \return padding in the form of {x0_begin, x1_begin, ... x0_end , x1_end, ...}
  */
-std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
-                                           std::vector<std::size_t> k_lens,
-                                           std::vector<std::size_t> strides,
-                                           std::vector<std::size_t> dilations,
-                                           bool use_upper = true);
+std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
+                                           const std::vector<std::size_t>& wei_lens,
+                                           const std::vector<std::size_t>& strides,
+                                           const std::vector<std::size_t>& dilations,
+                                           bool use_upper);
+
+// Used for dynamic auto padding of convolution operators since padding needs to be computed at
+// evaulation time.
+shape compute_padded_shape(const shape& input,
+                           const shape& weights,
+                           const std::vector<std::size_t>& padding,
+                           const std::vector<std::size_t>& stride,
+                           const std::vector<std::size_t>& dilation);
 
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/include/migraphx/reflect.hpp

@@ -56,11 +56,11 @@ auto reflect_impl(rank<0>, T&, Selector)
 }
 
 template <class T>
-auto reflectable_impl(rank<1>, T&& x)
+auto reflectable_impl(rank<1>, const T& x)
     -> decltype(T::reflect(x, reflect_placeholder{}), std::true_type{});
 
 template <class T>
-auto reflectable_impl(rank<0>, T &&) -> decltype(std::false_type{});
+auto reflectable_impl(rank<0>, const T&) -> decltype(std::false_type{});
 
 template <class T>
 struct remove_rvalue_reference
@@ -111,8 +111,18 @@ auto reflect(T& x, Selector f)
 template <class T>
 auto reflect_tie(T& x)
 {
-    return reflect(x, [](auto&& y, auto&&...) { return detail::wrap<decltype(y)>(y); })(
-        [](auto&&... xs) { return detail::auto_tuple(xs.get()...); });
+    return reflect(x, [](auto&& y, auto&&...) {
+        // cppcheck-suppress UnnecessaryElseStatement
+        if constexpr(is_reflectable<decltype(y)>{})
+        {
+            auto t = reflect_tie(y);
+            return detail::wrap<decltype(t)>(t);
+        }
+        else
+        {
+            return detail::wrap<decltype(y)>(y);
+        }
+    })([](auto&&... xs) { return detail::auto_tuple(xs.get()...); });
 }
 
 template <class T, class F>

src/include/migraphx/streamutils.hpp

@@ -26,7 +26,9 @@
 
 #include <ostream>
 #include <algorithm>
+#include <migraphx/reflect.hpp>
 #include <migraphx/rank.hpp>
+#include <migraphx/requires.hpp>
 #include <migraphx/config.hpp>
 #include <vector>
 
@@ -83,6 +85,20 @@ auto stream_write_value_impl(rank<0>, std::ostream& os, const Range& r)
     os << "}";
 }
 
+template <class T, MIGRAPHX_REQUIRES(is_reflectable<T>{})>
+void stream_write_value_impl(rank<0>, std::ostream& os, const T& x)
+{
+    char delim = '{';
+    reflect_each(x, [&](auto&& y, auto name) {
+        os << delim;
+        os << name << "=";
+        stream_write_value_impl(rank<2>{}, os, y);
+        delim = ',';
+    });
+    if(delim == ',')
+        os << "}";
+}
+
 } // namespace detail
 
 template <class T>

src/load_save.cpp

@@ -25,7 +25,6 @@
 #include <migraphx/file_buffer.hpp>
 #include <migraphx/json.hpp>
 #include <migraphx/msgpack.hpp>
-#include <migraphx/file_buffer.hpp>
 #include <fstream>
 
 namespace migraphx {

src/module.cpp

@@ -34,7 +34,6 @@
 #include <migraphx/pass_manager.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/register_target.hpp>
-#include <migraphx/make_op.hpp>
 #include <migraphx/json.hpp>
 #include <iostream>
 #include <sstream>

src/onnx/conv.cpp

@@ -30,7 +30,7 @@ namespace onnx {
 
 void recalc_conv_attributes(value& v, size_t kdims)
 {
-    if(not(v["padding"].size() == kdims or v["padding"].size() == kdims * 2))
+    if(v["padding"].size() != kdims and v["padding"].size() != kdims * 2)
     {
         v["padding"].resize(kdims);
         std::fill_n(v["padding"].begin(), kdims, 0);

src/onnx/parse_batchnorm.cpp

@@ -54,18 +54,19 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
             MIGRAPHX_THROW("PARSE_BATCHNORM: argument scale, bias, mean, or var rank != 1");
         }
 
-        if(x_lens.size() == 1)
+        auto x_rank = x_lens.size();
+        if(x_rank == 1 or x_rank == 2)
         {
             auto rt      = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
             auto eps     = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
-            auto n0   = info.add_broadcastable_binary_op("sub", args[0], args[3]);
-            auto d0   = info.add_broadcastable_binary_op("add", args[4], eps);
-            auto d1   = info.add_broadcastable_binary_op("pow", d0, rt);
-            auto div0 = info.add_broadcastable_binary_op("div", n0, d1);
+            auto numer   = info.add_broadcastable_binary_op("sub", args[0], args[3]);
+            auto var_eps = info.add_broadcastable_binary_op("add", args[4], eps);
+            auto denom   = info.add_broadcastable_binary_op("pow", var_eps, rt);
+            auto div0    = info.add_broadcastable_binary_op("div", numer, denom);
             auto r0      = info.add_broadcastable_binary_op("mul", div0, args[1]);
             return info.add_broadcastable_binary_op("add", r0, args[2]);
         }
-        else if(x_lens.size() > 2)
+        else if(x_rank > 2)
         {
             // unsqueeze tensors of shape (C) to broadcast correctly
             std::vector<int64_t> unsqueeze_axes(x_lens.size() - 2);
@@ -89,7 +90,7 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
         }
         else
         {
-            // num dims either 0 or 2
+            // rank == 0
             MIGRAPHX_THROW("PARSE_BATCHNORM: rank " + std::to_string(x_lens.size()) +
                            " input tensor, unhandled data format");
         }

src/onnx/parse_convolution.cpp

@@ -125,11 +125,9 @@ struct parse_convolution : op_parser<parse_convolution>
                     values["padding_mode"] = is_same_upper
                                                  ? to_value(op::padding_mode_t::same_upper)
                                                  : to_value(op::padding_mode_t::same_lower);
-                    values["use_dynamic_same_auto_pad"] = true;
                 }
                 else
                 {
-                    values["padding_mode"] = to_value(op::padding_mode_t::same);
                     // kernel shape will be fixed, so max_lens() == min_len() for kernel lengths
                     auto weight_lens = weights->get_shape().max_lens();
                     std::vector<std::size_t> k_lens(weight_lens.begin() + 2, weight_lens.end());

src/onnx/parse_deconvolution.cpp

@@ -95,6 +95,8 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
             check_attr_sizes(
                 kdims, values["dilation"].size(), "PARSE_CONV_TRANSPOSE: inconsistent dilations");
         }
+
+        // TODO: auto padding needs to be implemented for this parser and operator
         if(contains(info.attributes, "auto_pad"))
         {
             auto s = info.attributes["auto_pad"].s();
@@ -106,7 +108,9 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
 
             if(s.find("SAME") != std::string::npos)
             {
-                values["padding_mode"] = to_value(op::padding_mode_t::same);
+                bool is_same_upper     = (s.find("SAME_UPPER") != std::string::npos);
+                values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
+                                                       : to_value(op::padding_mode_t::same_lower);
             }
         }
 

src/pad_calc.cpp

@@ -52,19 +52,21 @@ void calculate_padding(int64_t idx,
     }
 }
 
-std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
-                                           std::vector<std::size_t> k_lens,
-                                           std::vector<std::size_t> strides,
-                                           std::vector<std::size_t> dilations,
+std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
+                                           const std::vector<std::size_t>& wei_lens,
+                                           const std::vector<std::size_t>& strides,
+                                           const std::vector<std::size_t>& dilations,
                                            bool use_upper)
 {
     std::vector<std::size_t> padding;
-    padding.resize(2 * k_lens.size());
-    for(std::size_t i = 0; i < padding.size() / 2; i++)
+    assert(input_lens.size() >= 3);
+    std::size_t num_spatial_dims = input_lens.size() - 2;
+    padding.resize(2 * num_spatial_dims);
+    for(std::size_t i = 0; i < num_spatial_dims; i++)
     {
-        std::ptrdiff_t input_dim      = tensor_lens[i];
+        std::ptrdiff_t input_dim      = input_lens[i + 2];
         std::ptrdiff_t stride         = strides[i];
-        std::ptrdiff_t weight_dim     = k_lens[i];
+        std::ptrdiff_t weight_dim     = wei_lens[i + 2];
         std::ptrdiff_t dilation       = dilations[i];
         std::ptrdiff_t output_dim     = (input_dim + stride - 1) / stride; // round up result
         std::ptrdiff_t new_weight_dim = weight_dim + (weight_dim - 1) * (dilation - 1);
@@ -86,5 +88,28 @@ std::vector<std::size_t> calc_dyn_auto_pad(std::vector<std::size_t> tensor_lens,
     return padding;
 }
 
+shape compute_padded_shape(const shape& input,
+                           const shape& weights,
+                           const std::vector<std::size_t>& padding,
+                           const std::vector<std::size_t>& stride,
+                           const std::vector<std::size_t>& dilation)
+{
+    const size_t num_spatial_dims = input.lens().size() - 2;
+
+    std::vector<size_t> output_lens{input.lens()[0], weights.lens()[0]};
+    // calculate the output shape of the convolution: ((W - K + 2P) / S) + 1
+    for(size_t i = 0; i < num_spatial_dims; ++i)
+    {
+        auto padding_factor = padding[i] + padding[i + num_spatial_dims];
+        output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(
+            1,
+            (input.lens()[i + 2] - (1 + dilation[i] * (weights.lens()[i + 2] - 1)) +
+             padding_factor) /
+                    stride[i] +
+                1)));
+    }
+    return input.with_lens(output_lens);
+}
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/rewrite_batchnorm.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/rewrite_batchnorm.hpp>
-#include <migraphx/program.hpp>
-#include <migraphx/instruction.hpp>
-#include <migraphx/op/batch_norm_inference.hpp>
-#include <migraphx/op/broadcast.hpp>
-#include <migraphx/op/add.hpp>
-#include <migraphx/op/mul.hpp>
-#include <migraphx/iterator_for.hpp>
-#include <migraphx/ranges.hpp>
-#include <migraphx/make_op.hpp>
-
-#include <migraphx/dfor.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-
-void rewrite_batchnorm::apply(module& m) const
-{
-    for(auto ins : iterator_for(m))
-    {
-        if(ins->name() != "batch_norm_inference")
-            continue;
-        // Get scale, bias, mean, variance from inputs
-        auto gamma    = ins->inputs()[1]->eval();
-        auto bias     = ins->inputs()[2]->eval();
-        auto mean     = ins->inputs()[3]->eval();
-        auto variance = ins->inputs()[4]->eval();
-        if(any_of({gamma, bias, mean, variance}, [](auto arg) { return arg.empty(); }))
-            continue;
-
-        std::vector<std::size_t> lens = ins->inputs()[1]->get_shape().lens();
-        shape s{ins->get_shape().type(), lens};
-        // Get epsilon
-        auto bn_op   = any_cast<op::batch_norm_inference>(ins->get_operator());
-        auto epsilon = bn_op.epsilon;
-
-        argument a{s};
-        argument b{s};
-        visit_all(gamma, bias, mean, variance, a, b)(
-            [&](auto gamma2, auto bias2, auto mean2, auto variance2, auto a2, auto b2) {
-                dfor(a.get_shape().elements())(
-                    [&](std::size_t c) { a2[c] = gamma2[c] / std::sqrt(variance2[c] + epsilon); });
-                dfor(b.get_shape().elements())([&](std::size_t c) {
-                    b2[c] = bias2[c] - (gamma2[c] * mean2[c] / std::sqrt(variance2[c] + epsilon));
-                });
-            });
-
-        auto broadcast   = op::broadcast{1, ins->get_shape().lens()};
-        auto a_ins       = m.add_literal({a.get_shape(), a.data()});
-        auto a_broadcast = m.insert_instruction(ins, broadcast, a_ins);
-        auto mul   = m.insert_instruction(ins, make_op("mul"), ins->inputs().front(), a_broadcast);
-        auto b_ins = m.add_literal({b.get_shape(), b.data()});
-        auto b_broadcast = m.insert_instruction(ins, broadcast, b_ins);
-        auto add         = m.insert_instruction(ins, make_op("add"), mul, b_broadcast);
-        m.replace_instruction(ins, add);
-    }
-}
-
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/rewrite_rnn.cpp

@@ -46,9 +46,6 @@
 #include <migraphx/iterator_for.hpp>
 #include <migraphx/dfor.hpp>
 #include <migraphx/ranges.hpp>
-#include <migraphx/op/common.hpp>
-#include <migraphx/op/rnn_var_sl_last_output.hpp>
-#include <migraphx/op/rnn_variable_seq_lens.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {