Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into rnn_optimization

src/include/migraphx/op/batch_norm.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_BATCH_NORM_HPP
+#define MIGRAPHX_GUARD_OPERATORS_BATCH_NORM_HPP
+
+#include <array>
+#include <migraphx/op/common.hpp>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct batch_norm_inference
+{
+    float epsilon  = 1.0e-6f;
+    float momentum = 0.9f;
+
+    std::string name() const { return "batch_norm_inference"; }
+
+    enum bn_infer_mode_t
+    {
+        per_activation,
+        spatial,
+    };
+
+    bn_infer_mode_t bn_mode = spatial;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(
+            f(self.epsilon, "epsilon"), f(self.momentum, "momentum"), f(self.bn_mode, "bn_mode"));
+    }
+
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(5);
+        check_shapes{inputs.data(), inputs.data() + 1, *this}.only_dims(4);
+        check_shapes{inputs.data() + 1, inputs.data() + inputs.size(), *this}.same_shape().elements(
+            inputs.front().lens()[1]);
+        return inputs.front();
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/binary.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_BINARY_HPP
+#define MIGRAPHX_GUARD_OPERATORS_BINARY_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct binary
+{
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs}.has(2).same_type().same_dims();
+        auto t    = inputs.at(0).type();
+        auto lens = inputs.at(0).lens();
+        return {t, lens};
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/broadcast.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_BROADCAST_HPP
+#define MIGRAPHX_GUARD_OPERATORS_BROADCAST_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+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.
+struct broadcast
+{
+    uint64_t axis = 0;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.axis, "axis"));
+    }
+
+    shape broadcast_shape;
+    std::string name() const { return "broadcast"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        auto t     = inputs.at(0).type();
+        auto input = inputs.at(0);
+
+        std::vector<size_t> bcast_strides(broadcast_shape.lens().size(), 0);
+
+        if(std::all_of(broadcast_shape.lens().cbegin(), broadcast_shape.lens().cend(), [&](auto x) {
+               return x == 1;
+           }))
+        {
+            if(axis != 0)
+                MIGRAPHX_THROW("when broadcasting tensor of size 1, axis should be 0");
+            return {t, broadcast_shape.lens(), std::move(bcast_strides)};
+        }
+        else
+        {
+            assert(broadcast_shape.lens().size() - axis >= input.lens().size());
+            if(!std::equal(
+                   input.lens().begin(), input.lens().end(), broadcast_shape.lens().begin() + axis))
+                MIGRAPHX_THROW("when broadcasting success sizes must match");
+            std::copy(input.strides().begin(), input.strides().end(), bcast_strides.begin() + axis);
+            return {t, broadcast_shape.lens(), std::move(bcast_strides)};
+        }
+    }
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        return {std::move(output_shape), std::move(args.at(0).data)};
+    }
+    int output_alias(const std::vector<shape>&) const { return 0; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/common.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_COMMON_HPP
+#define MIGRAPHX_GUARD_OPERATORS_COMMON_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+enum padding_mode_t
+{
+    default_, // NOLINT
+    same,
+    valid
+};
+
+// indicate rnn computation direction
+enum class rnn_direction
+{
+    forward,
+    reverse,
+    bidirectional,
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/concat.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_CONCAT_HPP
+#define MIGRAPHX_GUARD_OPERATORS_CONCAT_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct concat
+{
+    std::size_t axis = 0;
+    std::string name() const { return "concat"; }
+    std::vector<std::size_t> compute_offsets(const shape& output_shape,
+                                             const std::vector<argument>& args) const
+    {
+        std::vector<std::size_t> offsets;
+        std::vector<std::size_t> offset(args[0].get_shape().lens().size(), 0);
+        offset[axis] = 0;
+        for(const auto& arg : args)
+        {
+            offsets.push_back(output_shape.index(offset));
+            offset[axis] += arg.get_shape().lens()[axis];
+        }
+        return offsets;
+    }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        if(inputs.empty())
+        {
+            MIGRAPHX_THROW("Number of input tensors should exceed 0");
+        }
+
+        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++)
+        {
+            if(l != axis)
+            {
+                if(!std::all_of(inputs.begin(), inputs.end(), [&](auto s) {
+                       return s.lens()[l] == first_shape_lens[l];
+                   }))
+                {
+                    MIGRAPHX_THROW("Non-axis dimensions should match");
+                }
+            }
+        }
+        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;
+        std::copy(first_shape_lens.begin(), first_shape_lens.end(), std::back_inserter(new_lens));
+        new_lens[axis] = new_dim_axis;
+        return {type, new_lens};
+    }
+    argument compute(const shape& output_shape, std::vector<argument> args) const
+    {
+        argument result{output_shape};
+        std::vector<std::size_t> coffsets = compute_offsets(output_shape, args);
+        for(std::size_t l = 0; l < args.size(); l++)
+        {
+            auto argl             = args[l];
+            std::size_t nelements = argl.get_shape().elements();
+            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]);
+                // cppcheck-suppress useStlAlgorithm
+                for(std::size_t i = 0; i < nelements; i++)
+                {
+                    slice[i] = input[i];
+                }
+            });
+        }
+        return result;
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/contiguous.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_CONTIGUOUS_HPP
+#define MIGRAPHX_GUARD_OPERATORS_CONTIGUOUS_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+/// The contiguous operator takes a non-standard input tensor and returns
+/// the same tensor but in standard form. For example, if input tensor A which has lens = (4,5)
+/// is first transposed, i.e. lens = (5,4), this tensor's data layout remained the same
+/// during the transpose operation; only it's shape lengths and strides were changed.
+/// This leaves the tensor in a non-standard form. The contiguous operator copies the
+/// underlying data such that resulting tensor is returned to a standard form.
+struct contiguous
+{
+    std::string name() const { return "contiguous"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(1);
+        auto lens = inputs.at(0).lens();
+        auto t    = inputs.at(0).type();
+        return {t, lens};
+    }
+    argument compute(const shape& output_shape, std::vector<argument> args) const
+    {
+        assert(output_shape.standard());
+        argument result{output_shape};
+        visit_all(result, args[0])([&](auto output, auto input) {
+            shape_for_each(output.get_shape(), [&](const auto& idx) {
+                output(idx.begin(), idx.end()) = input(idx.begin(), idx.end());
+            });
+        });
+        return result;
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/convolution.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_HPP
+#define MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_HPP
+
+#include <array>
+#include <migraphx/op/common.hpp>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+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}};
+
+    padding_mode_t padding_mode = default_;
+    int group                   = 1;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.padding, "padding"),
+                    f(self.stride, "stride"),
+                    f(self.dilation, "dilation"),
+                    f(self.padding_mode, "padding_mode"),
+                    f(self.group, "group"));
+    }
+
+    std::string name() const { return "convolution"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(2).same_type().same_ndims().only_dims(4);
+
+        const shape& input   = inputs.at(0);
+        const shape& weights = inputs.at(1);
+        auto t               = input.type();
+        if(padding_mode == default_)
+        {
+            return {t,
+                    {
+                        input.lens()[0],
+                        weights.lens()[0],
+                        std::size_t(std::max<std::ptrdiff_t>(
+                            1,
+                            (input.lens()[2] - (1 + dilation[0] * (weights.lens()[2] - 1)) +
+                             2 * padding[0]) /
+                                    stride[0] +
+                                1)),
+                        std::size_t(std::max<std::ptrdiff_t>(
+                            1,
+                            (input.lens()[3] - (1 + dilation[1] * (weights.lens()[3] - 1)) +
+                             2 * padding[1]) /
+                                    stride[1] +
+                                1)),
+                    }};
+        }
+        else if(padding_mode == same)
+        {
+            return {t,
+                    {input.lens()[0],
+                     weights.lens()[0],
+                     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],
+                 weights.lens()[0],
+                 static_cast<std::size_t>(std::ceil(
+                     static_cast<double>(input.lens()[2] - weights.lens()[2] + 1) / stride[0])),
+                 static_cast<std::size_t>(std::ceil(
+                     static_cast<double>(input.lens()[3] - weights.lens()[3] + 1) / stride[1]))}};
+        }
+        else
+        {
+            MIGRAPHX_THROW("Invalid padding mode");
+        }
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/cos.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_COS_HPP
+#define MIGRAPHX_GUARD_OPERATORS_COS_HPP
+
+#include <array>
+#include <migraphx/op/unary.hpp>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct cos : unary
+{
+    std::string name() const { return "cos"; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/cosh.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_COSH_HPP
+#define MIGRAPHX_GUARD_OPERATORS_COSH_HPP
+
+#include <array>
+#include <migraphx/op/unary.hpp>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct cosh : unary
+{
+    std::string name() const { return "cosh"; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/div.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_DIV_HPP
+#define MIGRAPHX_GUARD_OPERATORS_DIV_HPP
+
+#include <array>
+#include <migraphx/op/binary.hpp>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct div : binary
+{
+    std::string name() const { return "div"; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/dot.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_DOT_HPP
+#define MIGRAPHX_GUARD_OPERATORS_DOT_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct dot
+{
+    float alpha = 1.0;
+    float beta  = 1.0;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.alpha, "alpha"), f(self.beta, "beta"));
+    }
+
+    std::string name() const { return "dot"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.same_type();
+        const shape& a = inputs.at(0);
+        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; }))
+        {
+            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(
+               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()) +
+                           "} x {" + to_string_range(b.lens()) + "}");
+        }
+
+        std::size_t dim_0 = a.lens().size() - 2;
+        std::size_t dim_1 = a.lens().size() - 1;
+        if(a.lens()[dim_1] != b.lens()[dim_0])
+        {
+            MIGRAPHX_THROW("DOT: inner dimensions do not match: {" + to_string_range(a.lens()) +
+                           "} x {" + to_string_range(b.lens()) + "}");
+        }
+
+        auto out_lens   = a.lens();
+        out_lens[dim_1] = b.lens()[dim_1];
+        if(inputs.size() == 3 && out_lens != inputs.at(2).lens())
+        {
+            MIGRAPHX_THROW("DOT: dimension mismatch, operand C: {" +
+                           to_string_range(inputs.at(2).lens()) +
+                           "}, cannot add to operand A * B: {" + to_string_range(out_lens) + "}");
+        }
+
+        return {t, out_lens};
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/elu.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_ELU_HPP
+#define MIGRAPHX_GUARD_OPERATORS_ELU_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct elu
+{
+    std::string name() const { return "elu"; }
+    float alpha;
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(1);
+        return inputs.front();
+    }
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.alpha, "alpha"));
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/exp.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_EXP_HPP
+#define MIGRAPHX_GUARD_OPERATORS_EXP_HPP
+
+#include <array>
+#include <migraphx/op/unary.hpp>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct exp : unary
+{
+    std::string name() const { return "exp"; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/flatten.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_FLATTEN_HPP
+#define MIGRAPHX_GUARD_OPERATORS_FLATTEN_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct flatten
+{
+    uint64_t axis = 0;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.axis, "axis"));
+    }
+
+    std::string name() const { return "flatten"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs}.has(1);
+        auto&& lens = inputs.front().lens();
+
+        if(axis > lens.size())
+        {
+            MIGRAPHX_THROW("axis for flatten must be less than tensor rank");
+        }
+        auto x =
+            std::accumulate(lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<>{});
+        auto y =
+            std::accumulate(lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
+        return {inputs.at(0).type(), {x, y}};
+    }
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        return {std::move(output_shape), std::move(args.front().data)};
+    }
+    int output_alias(const std::vector<shape>&) const { return 0; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/gather.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_GATHER_HPP
+#define MIGRAPHX_GUARD_OPERATORS_GATHER_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct gather
+{
+    int axis = 0;
+    std::string name() const { return "gather"; }
+
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(2);
+        auto lens = inputs[0].lens();
+        int n_dim = static_cast<int>(lens.size());
+        if(axis >= n_dim || axis < -n_dim)
+        {
+            MIGRAPHX_THROW("Gather: axis is out of range.");
+        }
+
+        // negative axis means counting dimensions from back
+        int axis_index = (axis < 0) ? (n_dim + axis) : axis;
+
+        auto type = inputs[0].type();
+        lens.erase(lens.begin() + axis_index);
+        if(!inputs[1].scalar())
+        {
+            auto ind_lens = inputs[1].lens();
+            lens.insert(lens.begin() + axis_index, 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 result{output_shape};
+        // negative axis means counting dimensions from back
+        int axis_index =
+            (axis < 0) ? static_cast<int>(args[0].get_shape().lens().size() + axis) : axis;
+
+        // max dimension in axis
+        visit_all(result, args[0])([&](auto output, auto data) {
+            args[1].visit([&](auto indices) {
+                if(output_shape.scalar())
+                {
+                    output[0] = data[indices.front()];
+                }
+                else
+                {
+                    auto out_lens        = data.get_shape().lens();
+                    out_lens[axis_index] = indices.get_shape().elements();
+                    migraphx::shape out_comp_shape{data.get_shape().type(), out_lens};
+                    shape_for_each(out_comp_shape, [&](const auto& out_idx) {
+                        auto data_idx        = out_idx;
+                        data_idx[axis_index] = indices[data_idx[axis_index]];
+                        output[out_comp_shape.index(out_idx.begin(), out_idx.end())] =
+                            data(data_idx.begin(), data_idx.end());
+                    });
+                }
+            });
+        });
+
+        return result;
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/gru.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_GRU_HPP
+#define MIGRAPHX_GUARD_OPERATORS_GRU_HPP
+
+#include <array>
+#include <migraphx/op/common.hpp>
+#include <migraphx/op/tanh.hpp>
+#include <migraphx/op/sigmoid.hpp>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct gru
+{
+    std::size_t hidden_size = 1;
+    std::vector<operation> actv_funcs{sigmoid{}, tanh{}};
+    rnn_direction direction = rnn_direction::forward;
+    float clip              = 0.0f;
+    int linear_before_reset = 0;
+
+    std::string name() const { return "gru"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        auto in_dims     = inputs[0].lens();
+        auto hidden_dims = inputs[2].lens();
+        if(hidden_size != hidden_dims[2])
+        {
+            MIGRAPHX_THROW("GRU: hidden size mismatch in attribute and input");
+        }
+
+        std::size_t num_directions = 1;
+        if(direction == rnn_direction::bidirectional)
+        {
+            num_directions = 2;
+        }
+
+        if(num_directions != hidden_dims[0])
+        {
+            MIGRAPHX_THROW("GRU: num_direction does not match the direction attribute");
+        }
+
+        std::vector<std::size_t> out_dims(in_dims);
+        out_dims.insert(out_dims.begin() + 1, num_directions);
+        out_dims.back() = hidden_size;
+
+        return {inputs[0].type(), out_dims};
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/identity.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_IDENTITY_HPP
+#define MIGRAPHX_GUARD_OPERATORS_IDENTITY_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct identity
+{
+    std::string name() const { return "identity"; }
+    shape compute_shape(std::vector<shape> inputs) const { return inputs.at(0); }
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        return {std::move(output_shape), std::move(args.at(0).data)};
+    }
+    int output_alias(const std::vector<shape>&) const { return 0; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/im2col.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_IM2COL_HPP
+#define MIGRAPHX_GUARD_OPERATORS_IM2COL_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+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}};
+
+    padding_mode_t padding_mode = default_;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.padding, "padding"),
+                    f(self.stride, "stride"),
+                    f(self.dilation, "dilation"),
+                    f(self.padding_mode, "padding_mode"));
+    }
+
+    std::string name() const { return "im2col"; }
+
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        auto input          = inputs[0];
+        auto weights        = inputs[1];
+        auto batch_size     = input.lens()[0];
+        auto input_channels = weights.lens()[1];
+        auto kernel_height  = weights.lens()[2];
+        auto kernel_width   = weights.lens()[3];
+        check_shapes{inputs, *this}.has(2);
+        if(batch_size != 1)
+            MIGRAPHX_THROW("im2col only support batch_size 1");
+        auto output_height = std::size_t(std::max<std::ptrdiff_t>(
+            1,
+            (input.lens()[2] - (1 + dilation[0] * (kernel_height - 1)) + 2 * padding[0]) /
+                    stride[0] +
+                1));
+        auto output_width  = std::size_t(std::max<std::ptrdiff_t>(
+            1,
+            (input.lens()[3] - (1 + dilation[1] * (kernel_width - 1)) + 2 * padding[1]) /
+                    stride[1] +
+                1));
+        auto channels_col  = kernel_height * kernel_width * input_channels;
+        return {input.type(), {output_height * output_width, channels_col}};
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/leaky_relu.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_LEAKY_RELU_HPP
+#define MIGRAPHX_GUARD_OPERATORS_LEAKY_RELU_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct leaky_relu
+{
+    std::string name() const { return "leaky_relu"; }
+    float alpha;
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(1);
+        return inputs.front();
+    }
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.alpha, "alpha"));
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/load.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_LOAD_HPP
+#define MIGRAPHX_GUARD_OPERATORS_LOAD_HPP
+
+#include <array>
+#include <migraphx/operation.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>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct load
+{
+    shape s;
+    std::size_t offset = 0;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.s, "shape"), f(self.offset, "offset"));
+    }
+
+    std::string name() const { return "load"; }
+    shape compute_shape(const std::vector<shape>& inputs) const
+    {
+        check_shapes{inputs}.has(1);
+        return s;
+    }
+    argument compute(const shape&, const std::vector<argument>& args) const
+    {
+        if((offset + s.bytes()) > args[0].get_shape().bytes())
+            MIGRAPHX_THROW("Load access is out of bounds");
+        return {s, args[0].data() + offset};
+    }
+    int output_alias(const std::vector<shape>&) const { return 0; }
+
+    friend std::ostream& operator<<(std::ostream& os, const load& op)
+    {
+        os << op.name() << "[";
+        os << "offset=" << op.offset << ",";
+        os << "end=" << (op.offset + op.s.bytes()) << "]";
+        return os;
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif