merge from develop

src/include/migraphx/operators.hpp

@@ -16,6 +16,13 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+enum padding_mode_t
+{
+    default_, // NOLINT
+    same,
+    valid
+};
+
 struct not_computable
 {
     argument compute(const shape&, const std::vector<argument>&) const
@@ -58,12 +65,7 @@ struct convolution
     std::array<std::size_t, 2> padding  = {{0, 0}};
     std::array<std::size_t, 2> stride   = {{1, 1}};
     std::array<std::size_t, 2> dilation = {{1, 1}};
-    enum padding_mode_t
-    {
-        default_, // NOLINT
-        same,
-        valid
-    };
+
     padding_mode_t padding_mode = default_;
     int group                   = 1;
 
@@ -138,12 +140,7 @@ struct im2col
     std::array<std::size_t, 2> padding  = {{0, 0}};
     std::array<std::size_t, 2> stride   = {{1, 1}};
     std::array<std::size_t, 2> dilation = {{1, 1}};
-    enum padding_mode_t
-    {
-        default_, // NOLINT
-        same,
-        valid
-    };
+
     padding_mode_t padding_mode = default_;
 
     template <class Self, class F>
@@ -189,12 +186,14 @@ struct pooling
     std::array<std::size_t, 2> padding = {{0, 0}};
     std::array<std::size_t, 2> stride  = {{1, 1}};
     std::array<std::size_t, 2> lengths = {{1, 1}};
+    padding_mode_t padding_mode        = default_;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
         return pack(f(self.mode, "mode"),
                     f(self.padding, "padding"),
+                    f(self.padding, "padding_mode"),
                     f(self.stride, "stride"),
                     f(self.lengths, "lengths"));
     }
@@ -211,7 +210,10 @@ struct pooling
         assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
         assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
 
-        return {t,
+        if(padding_mode == default_)
+        {
+            return {
+                t,
                 {
                     input.lens()[0],
                     input.lens()[1],
@@ -226,6 +228,39 @@ struct pooling
                                                   static_cast<float>(stride[1]))) +
                             1)),
                 }};
+        }
+        else if(padding_mode == same)
+        {
+            return {t,
+                    {input.lens()[0],
+                     input.lens()[1],
+                     static_cast<std::size_t>(
+                         std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
+                     static_cast<std::size_t>(
+                         std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
+        }
+        else if(padding_mode == valid)
+        {
+            return {t,
+                    {
+                        input.lens()[0],
+                        input.lens()[1],
+                        std::size_t(std::max<std::ptrdiff_t>(
+                            1,
+                            std::ptrdiff_t(std::floor((input.lens()[2] - lengths[0]) /
+                                                      static_cast<float>(stride[0]))) +
+                                1)),
+                        std::size_t(std::max<std::ptrdiff_t>(
+                            1,
+                            std::ptrdiff_t(std::floor((input.lens()[3] - lengths[1]) /
+                                                      static_cast<float>(stride[1]))) +
+                                1)),
+                    }};
+        }
+        else
+        {
+            MIGRAPHX_THROW("Invalid padding mode");
+        }
     }
 };
 
@@ -614,6 +649,42 @@ struct reshape
     int output_alias(const std::vector<shape>&) const { return 0; }
 };
 
+struct pad
+{
+    std::vector<int64_t> pads;
+    float value = 0.0f;
+    enum pad_op_mode_t
+    {
+        constant_pad,
+        reflect_pad,
+        edge_pad
+    };
+    pad_op_mode_t mode = constant_pad;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.mode, "mode"), f(self.pads, "pads"), f(self.value, "value"));
+    }
+
+    std::string name() const { return "pad"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(1);
+        auto&& idims = inputs.front().lens();
+        std::vector<std::size_t> rdims(idims.begin(), idims.end());
+        std::size_t num_dims = rdims.size();
+
+        for(std::size_t i = 0; i < num_dims; i++)
+        {
+            rdims[i] += pads[i] + pads[i + num_dims];
+        }
+
+        shape s{inputs.front().type(), rdims};
+        return s;
+    }
+};
+
 struct as_shape
 {
     shape s;

src/onnx/onnx.cpp

@@ -87,6 +87,7 @@ struct onnx_parser
         add_mem_op("ConstantFill", &onnx_parser::parse_constant_fill);
         add_mem_op("Transpose", &onnx_parser::parse_transpose);
         add_mem_op("RNN", &onnx_parser::parse_rnn);
+        add_mem_op("Pad", &onnx_parser::parse_pad);
 
         // init the activation function map
         init_actv_func();
@@ -228,24 +229,30 @@ struct onnx_parser
     parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
     {
         op::convolution op;
+        auto l0 = args[0];
         if(contains(attributes, "pads"))
         {
             if(contains(attributes, "auto_pad"))
             {
                 MIGRAPHX_THROW("auto_pad and padding cannot be specified simultaneously");
             }
-            std::vector<std::size_t> padding(4);
-            copy(attributes["pads"].ints(), padding.begin());
+            std::vector<std::int64_t> padding;
+            copy(attributes["pads"].ints(), std::back_inserter(padding));
             if(padding.size() != 4)
             {
                 MIGRAPHX_THROW("padding should have 4 values");
             }
             if(padding[0] != padding[2] || padding[1] != padding[3])
             {
-                MIGRAPHX_THROW("migraphx does not support asymetric padding");
+                // insert zeros for pad op (args[0] has 4 dims)
+                padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
+                l0      = prog.add_instruction(op::pad{padding}, l0);
+            }
+            else
+            {
+                op.padding[0] = padding[0];
+                op.padding[1] = padding[1];
             }
-            op.padding[0] = padding[0];
-            op.padding[1] = padding[1];
         }
         if(contains(attributes, "strides"))
         {
@@ -265,7 +272,7 @@ struct onnx_parser
 
             if(s.find("SAME") != std::string::npos)
             {
-                op.padding_mode = op::convolution::same;
+                op.padding_mode = op::padding_mode_t::same;
             }
         }
         if(contains(attributes, "group"))
@@ -279,7 +286,7 @@ struct onnx_parser
             auto l2       = prog.add_instruction(op::broadcast{axis, l1->get_shape()}, args[2]);
             return prog.add_instruction(op::add{}, l1, l2);
         }
-        return prog.add_instruction(op, args);
+        return prog.add_instruction(op, l0, args[1]);
     }
 
     instruction_ref parse_pooling(const std::string& name,
@@ -287,6 +294,7 @@ struct onnx_parser
                                   std::vector<instruction_ref> args)
     {
         op::pooling op{ends_with(name, "MaxPool") ? "max" : "average"};
+        auto l0 = args[0];
         if(starts_with(name, "Global"))
         {
             auto lens  = args.front()->get_shape().lens();
@@ -294,18 +302,23 @@ struct onnx_parser
         }
         if(contains(attributes, "pads"))
         {
-            std::vector<std::size_t> padding(4);
-            copy(attributes["pads"].ints(), padding.begin());
+            std::vector<std::int64_t> padding;
+            copy(attributes["pads"].ints(), std::back_inserter(padding));
             if(padding.size() != 4)
             {
                 MIGRAPHX_THROW("padding should have 4 values");
             }
             if(padding[0] != padding[2] || padding[1] != padding[3])
             {
-                MIGRAPHX_THROW("migraphx does not support asymetric padding");
+                // insert zeros for pad op (args[0] has 4 dims)
+                padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
+                l0      = prog.add_instruction(op::pad{padding}, l0);
+            }
+            else
+            {
+                op.padding[0] = padding[0];
+                op.padding[1] = padding[1];
             }
-            op.padding[0] = padding[0];
-            op.padding[1] = padding[1];
         }
         if(contains(attributes, "strides"))
         {
@@ -318,13 +331,14 @@ struct onnx_parser
         if(contains(attributes, "auto_pad"))
         {
             auto s = attributes["auto_pad"].s();
-            if(to_upper(s) != "NOTSET")
+            if(s.find("SAME_UPPER") == std::string::npos)
             {
-                MIGRAPHX_THROW("auto_pad is not supported for pooling");
+                MIGRAPHX_THROW("auto_pad only supports SAME_UPPER for pooling");
             }
+            op.padding_mode = op::padding_mode_t::same;
         }
 
-        return prog.add_instruction(op, std::move(args));
+        return prog.add_instruction(op, l0);
     }
 
     instruction_ref
@@ -562,6 +576,28 @@ struct onnx_parser
         return prog.add_instruction(migraphx::op::transpose{perm}, args.front());
     }
 
+    instruction_ref
+    parse_pad(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
+    {
+        std::vector<int64_t> pads{};
+        float value = 0.0f;
+        if(contains(attributes, "pads"))
+        {
+            auto&& pad_vals = attributes["pads"].ints();
+            pads            = std::vector<int64_t>(pad_vals.begin(), pad_vals.end());
+        }
+        if(contains(attributes, "value"))
+        {
+            value = parse_value(attributes.at("value")).at<float>();
+        }
+        if(contains(attributes, "mode"))
+        {
+            auto mode = attributes.at("mode").s();
+            if(mode != "constant")
+                MIGRAPHX_THROW("migraphx currently only supports constant padding");
+        }
+        return prog.add_instruction(migraphx::op::pad{pads, value}, args.front());
+    }
     // Use a literal instruction to replace the shape since, output of
     // shape operator are literals in migraphx
     instruction_ref

src/targets/cpu/lowering.cpp

@@ -298,6 +298,32 @@ struct cpu_contiguous
     }
 };
 
+struct cpu_pad
+{
+    op::pad op;
+    std::string name() const { return "cpu::contiguous"; }
+    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
+    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
+    {
+        assert(output_shape.standard());
+        argument result{output_shape};
+        result.visit([&](auto output) { std::fill(output.begin(), output.end(), op.value); });
+
+        visit_all(result, args[0])([&](auto output, auto input) {
+            shape_for_each(input.get_shape(), [&](const auto& idx) {
+                std::vector<std::size_t> new_idx(idx.size());
+                std::transform(
+                    idx.begin(), idx.end(), op.pads.begin(), new_idx.begin(), [](auto i, auto j) {
+                        return i + j;
+                    });
+                output(new_idx.begin(), new_idx.end()) = input(idx.begin(), idx.end());
+            });
+        });
+
+        return result;
+    }
+};
+
 struct cpu_concat
 {
     op::concat op;
@@ -663,6 +689,7 @@ struct cpu_apply
         apply_map["batch_norm_inference"] =
             extend_op<cpu_batch_norm_inference, op::batch_norm_inference>();
         apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>();
+        apply_map["pad"]        = extend_op<cpu_pad, op::pad>();
         apply_map["concat"]     = extend_op<cpu_concat, op::concat>();
         apply_map["gather"]     = extend_op<cpu_gather, op::gather>();
         apply_map["leaky_relu"] = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>();

src/targets/gpu/CMakeLists.txt

@@ -28,6 +28,7 @@ add_library(migraphx_device
     device/contiguous.cpp
     device/mul.cpp
     device/concat.cpp
+    device/pad.cpp
     device/gather.cpp
 )
 set_target_properties(migraphx_device PROPERTIES EXPORT_NAME device)
@@ -57,6 +58,7 @@ add_library(migraphx_gpu
     sigmoid.cpp
     abs.cpp
     elu.cpp
+    pad.cpp
     gather.cpp
 )
 set_target_properties(migraphx_gpu PROPERTIES EXPORT_NAME gpu)

src/targets/gpu/device/include/migraphx/gpu/device/nary.hpp

@@ -313,6 +313,12 @@ void nary_impl(hipStream_t stream, F f, argument result, Arguments... args)
         nary_nonstandard_impl(stream, f, result, args...);
 }
 
+template <class F>
+void nary_impl(hipStream_t stream, F f, argument result)
+{
+    nary_standard_impl(stream, f, result);
+}
+
 template <class... Arguments>
 auto nary_nonstandard(hipStream_t stream, argument result, Arguments... args)
 {

src/targets/gpu/device/pad.cpp

+#include <migraphx/shape.hpp>
+#include <migraphx/argument.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+#include <migraphx/gpu/device/pad.hpp>
+#include <migraphx/gpu/device/tensor.hpp>
+#include <migraphx/gpu/device/launch.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+argument
+pad(hipStream_t stream, argument result, argument arg1, float value, std::vector<std::int64_t> pads)
+{
+    std::size_t nelements = arg1.get_shape().elements();
+
+    nary(stream, result)([=] { return value; });
+    visit_all(result, arg1)([&](auto output, auto input) {
+        visit_tensor_size(result.get_shape().lens().size(), [&](auto ndim) {
+            std::size_t offsets[ndim];
+            std::copy(pads.begin(), pads.begin() + ndim, offsets);
+            auto* outptr      = output.data();
+            const auto* inptr = input.data();
+            hip_tensor_descriptor<ndim> desc_input(input.get_shape());
+            hip_tensor_descriptor<ndim> desc_output(output.get_shape());
+            gs_launch(stream, nelements)([=](auto i) {
+                auto idx = desc_input.multi(i);
+                for(std::size_t j = 0; j < ndim; j++)
+                {
+                    idx[j] += offsets[j];
+                }
+                outptr[desc_output.linear(idx)] = inptr[i];
+            });
+        });
+    });
+    return result;
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/include/migraphx/gpu/device/pad.hpp

+
+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_PAD_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_PAD_HPP
+
+#include <migraphx/argument.hpp>
+#include <migraphx/config.hpp>
+#include <hip/hip_runtime_api.h>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+argument pad(hipStream_t stream,
+             argument result,
+             argument arg1,
+             float value,
+             std::vector<std::int64_t> pads);
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_PAD_HPP
+#define MIGRAPHX_GUARD_RTGLIB_PAD_HPP
+
+#include <migraphx/gpu/lowering.hpp>
+#include <migraphx/manage_ptr.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/operators.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/shape_for_each.hpp>
+#include <migraphx/config.hpp>
+#include <migraphx/gpu/miopen.hpp>
+#include <migraphx/gpu/hip.hpp>
+#include <migraphx/dfor.hpp>
+#include <migraphx/gpu/device/pad.hpp>
+#include <migraphx/gpu/device/add.hpp>
+#include <migraphx/iterator_for.hpp>
+#include <migraphx/gpu/rocblas.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <utility>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct hip_pad
+{
+    op::pad op;
+
+    std::string name() const { return "gpu::pad"; }
+    shape compute_shape(std::vector<shape> inputs) const;
+    argument
+    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
+    int output_alias(const std::vector<shape>& shapes) const { return shapes.size() - 1; }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/targets/gpu/lowering.cpp

@@ -40,6 +40,7 @@
 #include <migraphx/gpu/pooling.hpp>
 #include <migraphx/gpu/gemm.hpp>
 #include <migraphx/gpu/concat.hpp>
+#include <migraphx/gpu/pad.hpp>
 #include <migraphx/gpu/gather.hpp>
 #include <utility>
 #include <functional>
@@ -94,6 +95,8 @@ struct miopen_apply
         add_extend_op<hip_concat, op::concat>("concat");
         add_extend_op<miopen_softmax, op::softmax>("softmax");
         add_extend_op<hip_gather, op::gather>("gather");
+        add_extend_op<hip_pad, op::pad>("pad");
+
         add_convolution_op();
         add_pooling_op();
         add_batch_norm_inference_op();

src/targets/gpu/pad.cpp

+#include <migraphx/gpu/pad.hpp>
+#include <migraphx/operators.hpp>
+#include <migraphx/manage_ptr.hpp>
+#include <migraphx/gpu/miopen.hpp>
+#include <migraphx/gpu/device/pad.hpp>
+#include <utility>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+shape hip_pad::compute_shape(std::vector<shape> inputs) const
+{
+    inputs.pop_back();
+    return op.compute_shape(inputs);
+}
+
+argument hip_pad::compute(context& ctx, const shape&, const std::vector<argument>& args) const
+{
+    return device::pad(ctx.get_stream().get(), args.back(), args.front(), op.value, op.pads);
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

test/cpu_ops_test.cpp

@@ -1802,4 +1802,18 @@ TEST_CASE(rnn_bidirectional)
     }
 }
 
+TEST_CASE(pad_test)
+{
+    migraphx::program p;
+    migraphx::shape s{migraphx::shape::float_type, {2, 2}};
+    auto l0 = p.add_literal(migraphx::literal{s, {1, 2, 3, 4}});
+    p.add_instruction(migraphx::op::pad{{1, 1, 1, 1}}, l0);
+    p.compile(migraphx::cpu::target{});
+    auto result = p.eval({});
+    std::vector<float> results_vector(16);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold{0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4, 0, 0, 0, 0, 0};
+    EXPECT(migraphx::verify_range(results_vector, gold));
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/gpu/miopen.cpp

@@ -934,6 +934,41 @@ struct test_concat_relu
     }
 };
 
+struct test_pad
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        migraphx::shape s0{migraphx::shape::int32_type, {1, 96, 165, 165}};
+        std::vector<int64_t> pads0 = {0, 0, 0, 0, 0, 0, 1, 1};
+        std::vector<int64_t> pads1 = {0, 0, 0, 0, 1, 1, 1, 1};
+        std::vector<int64_t> pads2 = {1, 1, 1, 1, 0, 0, 0, 0};
+        std::vector<int64_t> pads3 = {1, 0, 1, 0, 1, 0, 2, 0};
+        auto l0                    = p.add_parameter("x", s0);
+        p.add_instruction(migraphx::op::pad{pads0}, l0);
+        p.add_instruction(migraphx::op::pad{pads1}, l0);
+        p.add_instruction(migraphx::op::pad{pads2}, l0);
+        p.add_instruction(migraphx::op::pad{pads3}, l0);
+        return p;
+    }
+};
+
+struct test_pooling_autopad
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        migraphx::shape s0{migraphx::shape::float_type, {1, 3, 63, 63}};
+        auto l0 = p.add_parameter("x", s0);
+        migraphx::op::pooling op{"max"};
+        op.padding_mode = migraphx::op::padding_mode_t::same;
+        op.lengths      = {2, 2};
+        op.stride       = {2, 2};
+        p.add_instruction(op, l0);
+        return p;
+    }
+};
+
 struct test_gather
 {
     migraphx::program create_program() const
@@ -1391,10 +1426,12 @@ struct test_rnn_bidirectional10
 
 int main()
 {
+    verify_program<test_pooling_autopad>();
     verify_program<test_abs>();
     verify_program<test_concat>();
     verify_program<test_concat2>();
     verify_program<test_concat_relu>();
+    verify_program<test_pad>();
     verify_program<test_add>();
     verify_program<test_add_half>();
     verify_program<test_mul>();

test/onnx/onnx_test.cpp

@@ -718,4 +718,12 @@ TEST_CASE(group_conv_test)
     migraphx::parse_onnx("group_conv_test.onnx");
 }
 
+TEST_CASE(pad_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 2}});
+    p.add_instruction(migraphx::op::pad{{1, 1, 1, 1}}, l0);
+    migraphx::parse_onnx("pad_test.onnx");
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/onnx/pad_test.onnx

+pad-example:T
+
+
0
1"Pad*
+pads@
@
@
@

test-padZ
+
0
+

+
+b
+
1
+

+
+B
\ No newline at end of file