add conv optim

src/include/migraphx/convolution.hpp

+/*
+ * 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.
+ */
+#ifndef MIGRAPHX_GUARD_RTGLIB_CONVOLUTION_HPP
+#define MIGRAPHX_GUARD_RTGLIB_CONVOLUTION_HPP
+
+#include <migraphx/config.hpp>
+#include <migraphx/dfor.hpp>
+#include <migraphx/par_for.hpp>
+#include <migraphx/shape_for_each.hpp>
+#include <migraphx/tensor_view.hpp>
+#include <vector>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+template <class Output, class T, class Padding, class Stride>
+void convolution(Output output, T input, T weights, Padding padding, Stride stride, int group)
+{
+    auto output_shape = output.get_shape();
+    auto in_lens      = input.get_shape().lens();
+
+    auto wei_lens = weights.get_shape().lens();
+    auto wei_n    = wei_lens[0];
+    auto wei_c    = wei_lens[1];
+    std::vector<std::size_t> win_size(wei_lens.begin() + 1, wei_lens.end());
+
+    par_for(output_shape.elements(), [&](auto i) {
+        auto idx_o = output_shape.multi(i);
+        auto w     = idx_o[1];
+        auto n_dim = idx_o.size();
+
+        std::vector<std::ptrdiff_t> win_start;
+        for(std::size_t dim = 2; dim < n_dim; ++dim)
+        {
+            auto d_2 = dim - 2;
+            win_start.push_back(std::ptrdiff_t(idx_o[dim] * stride[d_2]) -
+                                std::ptrdiff_t(padding[d_2]));
+        }
+        const auto group_id = w / (wei_n / group);
+
+        shape win_shape{output_shape.type(), win_size};
+
+        double acc = 0.0;
+        shape_for_each(win_shape, [&](auto idx_win) {
+            auto k           = idx_win[0];
+            const auto in_ch = group_id * wei_c + k;
+            std::vector<std::ptrdiff_t> idx(idx_o.begin(), idx_o.end());
+            idx[1] = in_ch;
+            std::transform(idx_win.begin() + 1,
+                           idx_win.end(),
+                           win_start.begin(),
+                           idx.begin() + 2,
+                           [](std::ptrdiff_t ii, std::ptrdiff_t jj) { return ii + jj; });
+            std::vector<std::ptrdiff_t> idx_wei(idx_o.size());
+            idx_wei[0] = w;
+            std::copy(idx_win.begin(), idx_win.end(), idx_wei.begin() + 1);
+            if(std::all_of(idx.begin() + 2, idx.end(), [&](auto ii) { return ii >= 0; }) and
+               std::equal(idx.begin(),
+                          idx.end(),
+                          in_lens.begin(),
+                          in_lens.end(),
+                          std::less<std::ptrdiff_t>{}))
+            {
+                acc += input(idx.begin(), idx.end()) * weights(idx_wei.begin(), idx_wei.end());
+            }
+        });
+
+        output[i] = acc;
+    });
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/convolution.hpp

@@ -24,9 +24,12 @@
 #ifndef MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_HPP
 #define MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_HPP
 
+#include <migraphx/argument.hpp>
 #include <migraphx/op/common.hpp>
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/convolution.hpp>
+#include <migraphx/pad_calc.hpp>
 #include <migraphx/value.hpp>
 #include <cmath>
 #include <utility>
@@ -201,6 +204,37 @@ struct convolution
         check_attribute_size();
         return stride.size();
     }
+
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        std::vector<std::size_t> new_padding;
+        if(padding_mode != op::padding_mode_t::default_)
+        {
+            auto input_lens   = args[0].get_shape().lens();
+            auto weights_lens = args[1].get_shape().lens();
+            new_padding =
+                padding_mode == op::same_upper
+                    ? calc_dyn_auto_pad(input_lens, weights_lens, stride, dilation, true)
+                    : calc_dyn_auto_pad(input_lens, weights_lens, stride, dilation, false);
+            output_shape = compute_padded_shape(
+                args[0].get_shape(), args[1].get_shape(), new_padding, stride, dilation);
+        }
+        else
+        {
+            new_padding = padding;
+            if(output_shape.dynamic())
+            {
+                output_shape =
+                    normalize_compute_shape({args.at(0).get_shape(), args.at(1).get_shape()});
+            }
+        }
+
+        argument result{output_shape};
+        visit_all(result, args[0], args[1])([&](auto output, auto input, auto weights) {
+            migraphx::convolution(output, input, weights, new_padding, stride, group);
+        });
+        return result;
+    }
 };
 
 } // namespace op

src/include/migraphx/op/quant_convolution.hpp

@@ -25,8 +25,10 @@
 #define MIGRAPHX_GUARD_OPERATORS_QUANT_CONVOLUTION_HPP
 
 #include <migraphx/op/common.hpp>
+#include <migraphx/argument.hpp>
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/convolution.hpp>
 #include <migraphx/value.hpp>
 #include <cmath>
 #include <utility>
@@ -114,6 +116,17 @@ struct quant_convolution
         check_attribute_size();
         return stride.size();
     }
+
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        argument result{output_shape};
+        result.visit([&](auto output) {
+            visit_all(args[0], args[1])([&](auto input, auto weights) {
+                migraphx::convolution(output, input, weights, padding, stride, group);
+            });
+        });
+        return result;
+    }
 };
 
 } // namespace op

src/simplify_algebra.cpp

@@ -52,8 +52,9 @@ auto op_lit_broadcast(std::string op, std::string x, std::string y)
 
 auto conv_const_weights()
 {
-    return match::name("convolution")(match::used_once(),
-                                      match::args(match::any(), match::is_constant().bind("w")));
+    return match::name("convolution")(
+        match::used_once(),
+        match::args(match::none_of(match::is_constant()), match::is_constant().bind("w")));
 }
 
 auto reduction() { return match::name_contains("reduce"); }
@@ -268,6 +269,32 @@ struct find_dot_add
     }
 };
 
+struct find_conv_add
+{
+    auto matcher() const
+    {
+        auto add = match::name("add")(
+            match::either_arg(0, 1)(match::any().bind("x"),
+                                    match::any_of(match::is_constant()).bind("a")),
+            match::used_once());
+        return match::name("convolution")(match::used_once(),
+                                          match::args(add, match::is_constant().bind("w")));
+    }
+
+    void apply(module& m, const match::matcher_result& r) const
+    {
+        auto ins   = r.result;
+        auto a_ins = r.instructions["a"];
+        auto x_ins = r.instructions["x"];
+        auto w_ins = r.instructions["w"];
+
+        auto conv1 = m.insert_instruction(ins, ins->get_operator(), a_ins, w_ins);
+        auto conv2 = m.insert_instruction(ins, ins->get_operator(), x_ins, w_ins);
+
+        m.replace_instruction(ins, make_op("add"), conv1, conv2);
+    }
+};
+
 struct find_add_lit_broadcast
 {
     auto matcher() const
@@ -1239,6 +1266,7 @@ void simplify_algebra::apply(module& m) const
                             find_neg_unit_ops{},
                             find_zero_ops{},
                             find_dot_add{},
+                            find_conv_add{},
                             find_div_const{},
                             find_sub_const{},
                             find_rsqrt{},

src/targets/gpu/include/migraphx/gpu/convolution.hpp

@@ -21,8 +21,8 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_RTGLIB_CONVOLUTION_HPP
-#define MIGRAPHX_GUARD_RTGLIB_CONVOLUTION_HPP
+#ifndef MIGRAPHX_GUARD_RTGLIB_GPU_CONVOLUTION_HPP
+#define MIGRAPHX_GUARD_RTGLIB_GPU_CONVOLUTION_HPP
 
 #include <migraphx/shape.hpp>
 #include <migraphx/generate.hpp>

src/targets/ref/lowering.cpp

@@ -132,109 +132,6 @@ auto visit_quantize(T&& x, Ts&&... xs)
     };
 }
 
-template <class Op>
-struct ref_convolution : auto_register_op<ref_convolution<Op>>
-{
-    ref_convolution() = default;
-
-    ref_convolution(Op pop) : op(std::move(pop)) {}
-
-    Op op;
-
-    template <class Self, class F>
-    static auto reflect(Self& self, F f)
-    {
-        return migraphx::reflect(self.op, f);
-    }
-
-    std::string name() const { return "ref::" + op.name(); }
-    shape compute_shape(const std::vector<shape>& inputs) const
-    {
-        return op.normalize_compute_shape(inputs);
-    }
-
-    argument compute(context&, shape output_shape, std::vector<argument> args) const
-    {
-        std::vector<std::size_t> padding;
-        if(op.padding_mode != op::padding_mode_t::default_)
-        {
-            auto input_lens   = args[0].get_shape().lens();
-            auto weights_lens = args[1].get_shape().lens();
-            padding =
-                op.padding_mode == op::same_upper
-                    ? calc_dyn_auto_pad(input_lens, weights_lens, op.stride, op.dilation, true)
-                    : calc_dyn_auto_pad(input_lens, weights_lens, op.stride, op.dilation, false);
-            output_shape = compute_padded_shape(
-                args[0].get_shape(), args[1].get_shape(), padding, op.stride, op.dilation);
-        }
-        else
-        {
-            padding = op.padding;
-            if(output_shape.dynamic())
-            {
-                output_shape =
-                    op.normalize_compute_shape({args.at(0).get_shape(), args.at(1).get_shape()});
-            }
-        }
-
-        argument result{output_shape};
-        visit_quantize(result, args[0], args[1])([&](auto output, auto input, auto weights) {
-            auto in_lens = input.get_shape().lens();
-
-            auto wei_lens = weights.get_shape().lens();
-            auto wei_n    = wei_lens[0];
-            auto wei_c    = wei_lens[1];
-            std::vector<std::size_t> win_size(wei_lens.begin() + 1, wei_lens.end());
-
-            par_for(output_shape.elements(), [&](auto i) {
-                auto idx_o = output_shape.multi(i);
-                auto w     = idx_o[1];
-                auto n_dim = idx_o.size();
-
-                std::vector<std::ptrdiff_t> win_start;
-                for(std::size_t dim = 2; dim < n_dim; ++dim)
-                {
-                    auto d_2 = dim - 2;
-                    win_start.push_back(std::ptrdiff_t(idx_o[dim] * op.stride[d_2]) -
-                                        std::ptrdiff_t(padding[d_2]));
-                }
-                const auto group_id = w / (wei_n / op.group);
-
-                shape win_shape{output_shape.type(), win_size};
-
-                double acc = 0.0;
-                shape_for_each(win_shape, [&](auto idx_win) {
-                    auto k           = idx_win[0];
-                    const auto in_ch = group_id * wei_c + k;
-                    std::vector<std::ptrdiff_t> idx(idx_o.begin(), idx_o.end());
-                    idx[1] = in_ch;
-                    std::transform(idx_win.begin() + 1,
-                                   idx_win.end(),
-                                   win_start.begin(),
-                                   idx.begin() + 2,
-                                   [](std::ptrdiff_t ii, std::ptrdiff_t jj) { return ii + jj; });
-                    std::vector<std::ptrdiff_t> idx_wei(idx_o.size());
-                    idx_wei[0] = w;
-                    std::copy(idx_win.begin(), idx_win.end(), idx_wei.begin() + 1);
-                    if(std::all_of(idx.begin() + 2, idx.end(), [&](auto ii) { return ii >= 0; }) and
-                       std::equal(idx.begin(),
-                                  idx.end(),
-                                  in_lens.begin(),
-                                  in_lens.end(),
-                                  std::less<std::ptrdiff_t>{}))
-                    {
-                        acc +=
-                            input(idx.begin(), idx.end()) * weights(idx_wei.begin(), idx_wei.end());
-                    }
-                });
-
-                output[i] = acc;
-            });
-        });
-        return result;
-    }
-};
-
 struct ref_im2col
 {
     op::im2col op;
@@ -564,11 +461,8 @@ struct ref_apply
 
     void init()
     {
-        apply_map["convolution"] = extend_op<ref_convolution<op::convolution>, op::convolution>();
-        apply_map["dot"]         = extend_op<ref_gemm, op::dot>();
-        apply_map["quant_dot"]   = extend_op<ref_quant_gemm, op::quant_dot>();
-        apply_map["quant_convolution"] =
-            extend_op<ref_convolution<op::quant_convolution>, op::quant_convolution>();
+        apply_map["dot"]        = extend_op<ref_gemm, op::dot>();
+        apply_map["quant_dot"]  = extend_op<ref_quant_gemm, op::quant_dot>();
         apply_map["im2col"]     = extend_op<ref_im2col, op::im2col>();
         apply_map["logsoftmax"] = extend_op<ref_softmax<op::logsoftmax>, op::logsoftmax>();
         apply_map["lrn"]        = extend_op<ref_lrn, op::lrn>();