merge develop branch changes

src/targets/gpu/argmax.cpp

+#include <migraphx/gpu/argmax.hpp>
+#include <migraphx/gpu/device/argmax.hpp>
+#include <migraphx/gpu/context.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+shape hip_argmax::compute_shape(const std::vector<shape>& inputs) const
+{
+    check_shapes{inputs, *this}.has(2).standard();
+    return op.compute_shape({inputs.at(0)});
+}
+
+argument hip_argmax::compute(context& ctx, const shape&, const std::vector<argument>& args) const
+{
+    device::argmax(ctx.get_stream().get(), args.back(), args.front(), op.axis);
+    return args.back();
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/argmin.cpp

+#include <migraphx/gpu/argmin.hpp>
+#include <migraphx/gpu/device/argmin.hpp>
+#include <migraphx/gpu/context.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+shape hip_argmin::compute_shape(const std::vector<shape>& inputs) const
+{
+    check_shapes{inputs, *this}.has(2).standard();
+    return op.compute_shape({inputs.at(0)});
+}
+
+argument hip_argmin::compute(context& ctx, const shape&, const std::vector<argument>& args) const
+{
+    device::argmin(ctx.get_stream().get(), args.back(), args.front(), op.axis);
+    return args.back();
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/argmax.cpp

+#include <migraphx/shape.hpp>
+#include <migraphx/argument.hpp>
+#include <migraphx/gpu/device/argmax.hpp>
+#include <migraphx/gpu/device/tensor.hpp>
+#include <migraphx/gpu/device/launch.hpp>
+#include <migraphx/gpu/device/types.hpp>
+#include <migraphx/gpu/device/arg_op.hpp>
+#include <migraphx/gpu/hip.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void argmax(hipStream_t stream, const argument& result, const argument& arg, int64_t axis)
+{
+    arg_op(argmax_op{}, stream, result, arg, axis);
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/argmin.cpp

+#include <migraphx/shape.hpp>
+#include <migraphx/argument.hpp>
+#include <migraphx/gpu/device/argmin.hpp>
+#include <migraphx/gpu/device/tensor.hpp>
+#include <migraphx/gpu/device/launch.hpp>
+#include <migraphx/gpu/device/types.hpp>
+#include <migraphx/gpu/device/arg_op.hpp>
+#include <migraphx/gpu/hip.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void argmin(hipStream_t stream, const argument& result, const argument& arg, int64_t axis)
+{
+    arg_op(argmin_op{}, stream, result, arg, axis);
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/concat.cpp

@@ -10,22 +10,22 @@ namespace gpu {
 namespace device {
 
 argument concat(hipStream_t stream,
-                const migraphx::shape& output_shape,
+                const migraphx::shape&,
                 std::vector<migraphx::argument> args,
                 std::vector<std::size_t> offsets)
 {
-    for(std::size_t l = 0; l < args.size() - 1; l++)
+    auto ninputs = args.size() - 1;
+    for(std::size_t j = 0; j < ninputs; j++)
     {
-        auto argl             = args[l];
-        std::size_t nelements = argl.get_shape().elements();
-        visit_all(args.back(), argl)([&](auto output, auto input) {
-            visit_tensor_size(output_shape.lens().size(), [&](auto ndim) {
-                auto* outptr      = output.data() + offsets[l];
-                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) { outptr[desc_output.linear(desc_input.multi(i))] = inptr[i]; });
+        auto&& arg            = args[j];
+        std::size_t nelements = arg.get_shape().elements();
+        auto offset           = offsets[j];
+        shape arg_shape{arg.get_shape().type(), arg.get_shape().lens()};
+        hip_visit_all(args.back(), arg, arg_shape)([&](auto output, auto input, auto input_shape) {
+            gs_launch(stream, nelements)([=](auto i) {
+                auto input_idx              = input_shape.multi(i);
+                auto idx                    = output.get_shape().index(input_idx);
+                output.data()[idx + offset] = input[input_idx];
             });
         });
     }

src/targets/gpu/device/erf.cpp

+#include <migraphx/gpu/device/erf.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+#include <migraphx/gpu/device/types.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void erf(hipStream_t stream, const argument& result, const argument& arg)
+{
+    nary(stream, result, arg)([](auto x) { return ::erf(to_hip_type(x)); });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/gather.cpp

@@ -11,35 +11,30 @@ inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 namespace device {
 
-argument gather(hipStream_t stream,
-                const migraphx::shape& output_shape,
-                std::vector<migraphx::argument> args,
-                int axis)
+argument gather(hipStream_t stream, argument result, argument arg1, argument arg2, int axis)
 {
-    auto axis_index = (axis < 0) ? (axis + args[0].get_shape().lens().size()) : axis;
-    visit_all(args.back(), args[0])([&](auto output, auto input) {
-        std::size_t nelements = output_shape.elements();
-        args[1].visit([&](auto indices) {
-            const auto* indices_ptr = device_cast(indices.data());
-            auto* out_ptr           = device_cast(output.data());
-            const auto* in_ptr      = device_cast(input.data());
-            auto& input_shape       = args[0].get_shape();
-            auto lens               = input_shape.lens();
-            lens[axis_index]        = args[1].get_shape().elements();
-            migraphx::shape out_comp_shape{output_shape.type(), lens};
-            visit_tensor_size(out_comp_shape.lens().size(), [&](auto n_out_dim) {
-                hip_tensor_descriptor<n_out_dim> desc_input(input_shape);
-                hip_tensor_descriptor<n_out_dim> desc_output(out_comp_shape);
-                gs_launch(stream, nelements)([=](auto ii) {
-                    auto in_idx        = desc_output.multi(ii);
-                    in_idx[axis_index] = indices_ptr[in_idx[axis_index]];
-                    out_ptr[ii]        = in_ptr[desc_input.linear(in_idx)];
+    auto axis_index   = (axis < 0) ? (axis + arg1.get_shape().lens().size()) : axis;
+    auto& input_shape = arg1.get_shape();
+    auto lens         = input_shape.lens();
+    lens[axis_index]  = arg2.get_shape().elements();
+    shape out_comp_shape{result.get_shape().type(), lens};
+    std::size_t nelements = result.get_shape().elements();
+
+    visit_all(result, arg1)([&](auto output, auto input_v) {
+        hip_visit_views(input_v, out_comp_shape)([&](auto input, auto out_comp) {
+            arg2.visit([&](auto indices) {
+                const auto* indices_ptr = device_cast(indices.data());
+                auto* output_ptr        = device_cast(output.data());
+                gs_launch(stream, nelements)([=](auto i) {
+                    auto idx        = out_comp.multi(i);
+                    idx[axis_index] = indices_ptr[idx[axis_index]];
+                    output_ptr[i]   = input[idx];
                 });
             });
         });
     });
 
-    return args.back();
+    return result;
 }
 
 } // namespace device

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

+
+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_ARRAY_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_ARRAY_HPP
+
+#include <migraphx/gpu/device/types.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+template <class T, std::size_t N>
+struct hip_array
+{
+    T d[N];
+    MIGRAPHX_DEVICE_CONSTEXPR T& operator[](std::size_t i) { return d[i]; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T& operator[](std::size_t i) const { return d[i]; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T* data() { return d; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T* data() const { return d; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR std::integral_constant<std::size_t, N> size() const { return {}; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T* begin() { return d; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T* begin() const { return d; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T* end() { return d + size(); }
+    MIGRAPHX_DEVICE_CONSTEXPR const T* end() const { return d + size(); }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T dot(const hip_array& x) const
+    {
+        T result = 0;
+        for(std::size_t i = 0; i < N; i++)
+            result += x[i] * d[i];
+        return result;
+    }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T product() const
+    {
+        T result = 1;
+        for(std::size_t i = 0; i < N; i++)
+            result *= d[i];
+        return result;
+    }
+
+    friend MIGRAPHX_DEVICE_CONSTEXPR hip_array operator*(const hip_array& x, const hip_array& y)
+    {
+        hip_array result;
+        for(std::size_t i = 0; i < N; i++)
+            result[i] = x[i] * y[i];
+        return result;
+    }
+
+    friend MIGRAPHX_DEVICE_CONSTEXPR hip_array operator+(const hip_array& x, const hip_array& y)
+    {
+        hip_array result{};
+        for(std::size_t i = 0; i < N; i++)
+            result[i] = x[i] + y[i];
+        return result;
+    }
+};
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

@@ -11,9 +11,33 @@ namespace device {
 
 struct index
 {
-    std::size_t global;
-    std::size_t local;
-    std::size_t group;
+    std::size_t global = 0;
+    std::size_t local  = 0;
+    std::size_t group  = 0;
+
+    __device__ std::size_t nglobal() const { return blockDim.x * gridDim.x; } // NOLINT
+
+    __device__ std::size_t nlocal() const { return blockDim.x; } // NOLINT
+
+    template <class F>
+    __device__ void global_stride(std::size_t n, F f) const
+    {
+        const auto stride = nglobal();
+        for(std::size_t i = global; i < n; i += stride)
+        {
+            f(i);
+        }
+    }
+
+    template <class F>
+    __device__ void local_stride(std::size_t n, F f) const
+    {
+        const auto stride = nlocal();
+        for(std::size_t i = local; i < n; i += stride)
+        {
+            f(i);
+        }
+    }
 };
 
 template <class F>
@@ -35,18 +59,26 @@ inline auto launch(hipStream_t stream, std::size_t global, std::size_t local)
     };
 }
 
+template <class F>
+__host__ __device__ auto gs_invoke(F&& f, std::size_t i, index idx) -> decltype(f(i, idx))
+{
+    return f(i, idx);
+}
+
+template <class F>
+__host__ __device__ auto gs_invoke(F&& f, std::size_t i, index) -> decltype(f(i))
+{
+    return f(i);
+}
+
 inline auto gs_launch(hipStream_t stream, std::size_t n, std::size_t local = 1024)
 {
-    std::size_t groups  = 1 + n / local;
+    std::size_t groups  = (n + local - 1) / local;
     std::size_t nglobal = std::min<std::size_t>(256, groups) * local;
 
     return [=](auto f) {
-        launch(stream, nglobal, local)([=](auto idx) {
-            for(size_t i = idx.global; i < n; i += nglobal)
-            {
-                f(i);
-            }
-        });
+        launch(stream, nglobal, local)(
+            [=](auto idx) { idx.global_stride(n, [&](auto i) { gs_invoke(f, i, idx); }); });
     };
 }
 

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

+
+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_REDUCE_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_REDUCE_HPP
+
+#include <migraphx/gpu/device/launch.hpp>
+#include <migraphx/gpu/device/visit.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+struct sum
+{
+    template <class T, class U>
+    MIGRAPHX_DEVICE_CONSTEXPR auto operator()(T x, U y) const
+    {
+        return x + y;
+    }
+};
+
+struct id
+{
+    template <class T>
+    MIGRAPHX_DEVICE_CONSTEXPR auto operator()(T x) const
+    {
+        return x;
+    }
+};
+
+struct max
+{
+    template <class T, class U>
+    MIGRAPHX_DEVICE_CONSTEXPR auto operator()(T x, U y) const
+    {
+        return x > y ? x : y;
+    }
+};
+
+struct min
+{
+    template <class T, class U>
+    MIGRAPHX_DEVICE_CONSTEXPR auto operator()(T x, U y) const
+    {
+        return x < y ? x : y;
+    }
+};
+
+struct lowest
+{
+    template <class T>
+    operator T() const
+    {
+        return device_cast(std::numeric_limits<host_type<T>>::lowest());
+    }
+};
+
+struct highest
+{
+    template <class T>
+    operator T() const
+    {
+        return device_cast(std::numeric_limits<host_type<T>>::max());
+    }
+};
+
+#ifdef MIGRAPHX_NO_DPP
+template <std::size_t N, class Op, class T, class F>
+__device__ auto block_reduce(index idx, Op op, T init, std::size_t n, F f)
+{
+    using type = decltype(f(idx.local));
+    MIGRAPHX_DEVICE_SHARED type buffer[N];
+    type x = init;
+    idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
+    buffer[idx.local] = x;
+    __syncthreads();
+
+    for(std::size_t s = 1; s < idx.nlocal(); s *= 2)
+    {
+        const std::size_t index = 2 * s * idx.local;
+        if(index + s < idx.nlocal())
+        {
+            buffer[index] = op(buffer[index], buffer[index + s]);
+        }
+        __syncthreads();
+    }
+    return buffer[0];
+}
+#else
+constexpr unsigned int dpp_row_shr(unsigned int x) { return 0x110u | x; }
+
+constexpr unsigned int dpp_row_bcast(unsigned int x)
+{
+    unsigned int y = 0;
+    switch(x)
+    {
+    case 15: y = 0x142; break;
+    case 31: y = 0x143; break;
+    default: throw std::runtime_error("Unknown bcast");
+    }
+    return y;
+}
+
+template <unsigned int DppCtrl,
+          unsigned int RowMask  = 0xf,
+          unsigned int BankMask = 0xf,
+          bool BoundCtrl        = false,
+          class T>
+__device__ T dpp_mov(T& x)
+{
+    static const std::size_t n = sizeof(T) < 4 ? 1 : sizeof(T) / 4;
+    union type
+    {
+        uint32_t reg[n];
+        T data;
+    };
+    type output{};
+    type input{};
+    // cppcheck-suppress unreadVariable
+    input.data = x;
+    for(std::size_t i = 0; i < n; i++)
+    {
+        output.reg[i] = __llvm_amdgcn_move_dpp(input.reg[i], DppCtrl, RowMask, BankMask, BoundCtrl);
+    }
+    return output.data;
+}
+
+template <class T, class Op>
+__device__ void dpp_reduce(T& in, Op op)
+{
+    T out{};
+    out = dpp_mov<dpp_row_shr(1)>(in);
+    in  = op(in, out);
+    out = dpp_mov<dpp_row_shr(2)>(in);
+    in  = op(in, out);
+    out = dpp_mov<dpp_row_shr(4), 0xf, 0xe>(in);
+    in  = op(in, out);
+    out = dpp_mov<dpp_row_shr(8), 0xf, 0xc>(in);
+    in  = op(in, out);
+    out = dpp_mov<dpp_row_bcast(15), 0xa>(in);
+    in  = op(in, out);
+    out = dpp_mov<dpp_row_bcast(31), 0xc>(in);
+    in  = op(in, out);
+}
+
+__device__ inline void dpp_reduce(float& x, sum)
+{
+#ifdef MIGRAPHX_USE_CLANG_TIDY
+    (void)x;
+#else
+    __asm__ volatile("s_nop 4\n"
+                     "v_add_f32 %0 %0 %0 row_shr:1\n"
+                     "s_nop 1\n"
+                     "v_add_f32 %0 %0 %0 row_shr:2\n"
+                     "s_nop 1\n"
+                     "v_add_f32 %0 %0 %0 row_shr:4 bank_mask:0xe\n"
+                     "s_nop 1\n"
+                     "v_add_f32 %0 %0 %0 row_shr:8 bank_mask:0xc\n"
+                     "s_nop 1\n"
+                     "v_add_f32 %0 %0 %0 row_bcast:15 row_mask:0xa\n"
+                     "s_nop 1\n"
+                     "v_add_f32 %0 %0 %0 row_bcast:31 row_mask:0xc\n"
+                     "s_nop 1\n"
+                     : "=v"(x)
+                     : "0"(x));
+#endif
+}
+
+template <std::size_t N, class Op, class T, class F>
+__device__ auto block_reduce(index idx, Op op, T init, std::size_t n, F f)
+{
+    using type = decltype(f(idx.local));
+    MIGRAPHX_DEVICE_SHARED type buffer[N / 64];
+    type x = init;
+    idx.local_stride(n, [&](auto i) { x = op(x, f(i)); });
+    dpp_reduce(x, op);
+
+    const auto ldsidx = idx.local / 64;
+    if((idx.local % 64) == 63)
+    {
+        buffer[ldsidx] = x;
+    }
+    __syncthreads();
+
+    type y = init;
+    for(std::size_t i = 0; i < idx.nlocal() / 64; i++)
+    {
+        y = op(y, buffer[i]);
+    }
+    return y;
+}
+#endif
+constexpr std::size_t compute_block_size(std::size_t n, std::size_t max_block_size)
+{
+    size_t block_size = 64;
+    while(block_size < max_block_size and block_size < n)
+        block_size *= 2;
+    return block_size;
+}
+
+template <class Op, class T, class Input, class Output>
+void reduce(hipStream_t stream,
+            const argument& result,
+            const argument& arg,
+            Op op,
+            T init,
+            Input read_input,
+            Output read_output)
+{
+    auto&& output_shape = result.get_shape();
+    auto&& input_shape  = arg.get_shape();
+    std::vector<std::size_t> reduce_lens;
+    std::transform(output_shape.lens().begin(),
+                   output_shape.lens().end(),
+                   input_shape.lens().begin(),
+                   std::back_inserter(reduce_lens),
+                   [](auto x, auto y) -> std::size_t {
+                       if(x == y)
+                           return 1;
+                       else
+                           return y;
+                   });
+    shape reduce_slice{output_shape.type(), reduce_lens};
+    hip_visit_all(result, arg, reduce_slice)([&](auto output, auto input, auto reduce_shape) {
+        auto nelements = result.get_shape().elements();
+        auto relements = reduce_slice.elements();
+
+        const std::size_t max_block_size = 256;
+        const std::size_t block_size     = compute_block_size(relements, max_block_size);
+        gs_launch(stream, nelements * block_size, block_size)([=](auto i, auto idx) __device__ {
+            const auto out_idx = i / block_size;
+            auto base_idx      = output.get_shape().multi(out_idx);
+            auto r = block_reduce<max_block_size>(idx, op, init, relements, [&](auto j) __device__ {
+                auto reduce_idx = reduce_shape.multi(j);
+                return read_input(input[reduce_idx + base_idx]);
+            });
+            if(idx.local == 0)
+                output.data()[out_idx] = read_output(r);
+        });
+    });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

+
+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_SHAPE_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_SHAPE_HPP
+
+#include <migraphx/gpu/device/array.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+template <std::size_t N>
+struct hip_shape
+{
+    using hip_index                   = hip_array<std::size_t, N>;
+    hip_array<std::size_t, N> lens    = {};
+    hip_array<std::size_t, N> strides = {};
+    bool standard                     = false;
+
+    __device__ __host__ hip_shape() = default;
+
+    hip_shape(const shape& s) : standard(s.standard())
+    {
+        assert(s.lens().size() == N);
+        assert(s.strides().size() == N);
+        std::copy(s.lens().begin(), s.lens().end(), lens.begin());
+        std::copy(s.strides().begin(), s.strides().end(), strides.begin());
+    }
+
+    MIGRAPHX_DEVICE_CONSTEXPR std::size_t elements() const { return lens.product(); }
+
+    MIGRAPHX_DEVICE_CONSTEXPR std::size_t index(hip_index x) const { return x.dot(strides); }
+
+    MIGRAPHX_DEVICE_CONSTEXPR std::size_t index(std::initializer_list<std::size_t> x) const
+    {
+        std::size_t idx = 0;
+        for(std::size_t i = 0; i < x.size(); i++)
+            idx += *(x.begin() + i) * strides[i];
+        return idx;
+    }
+
+    MIGRAPHX_DEVICE_CONSTEXPR std::size_t index(std::size_t i) const
+    {
+        if(this->standard)
+            return i;
+        else
+        {
+            const std::size_t rank = this->lens.size();
+            std::size_t s          = 1;
+            std::size_t result     = 0;
+            for(std::size_t j = 0; j < this->lens.size(); j++)
+            {
+                const std::size_t k      = rank - j - 1;
+                const std::size_t stride = this->strides[k];
+                const std::size_t len    = this->lens[k];
+                const std::size_t slen   = s * len;
+                const std::size_t idx    = (i % slen) / s;
+                result += stride * idx;
+                s = slen;
+            }
+            return result;
+        }
+    }
+
+    MIGRAPHX_DEVICE_CONSTEXPR hip_index multi(std::size_t idx) const
+    {
+        hip_index result;
+        std::size_t tidx = idx;
+        for(std::size_t is = 0; is < result.size(); is++)
+        {
+            result[is] = tidx / strides[is];
+            tidx       = tidx % strides[is];
+        }
+        return result;
+    }
+
+    MIGRAPHX_DEVICE_CONSTEXPR hip_index carry(hip_index result) const
+    {
+        std::ptrdiff_t rem = 0;
+        for(std::ptrdiff_t i = result.size() - 1; i >= 0; i--)
+        {
+            auto z = result[i] + rem;
+            rem    = z - std::ptrdiff_t(lens[i]) + 1;
+            if(rem > 0)
+                z -= rem;
+            else
+                rem = 0;
+            result[i] = z;
+        }
+        return result;
+    }
+};
+
+template <std::size_t N>
+hip_shape<N> make_hip_shape(const shape& x)
+{
+    return x;
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

 #ifndef MIGRAPHX_GUARD_RTGLIB_DEAVICE_TENSOR_HPP
 #define MIGRAPHX_GUARD_RTGLIB_DEAVICE_TENSOR_HPP
 
-#include <hip/hip_runtime.h>
-#include <migraphx/functional.hpp>
-#include <migraphx/config.hpp>
+#include <migraphx/gpu/device/visit.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 namespace device {
 
-template <class F>
-void visit_tensor_size(std::size_t n, F f)
-{
-    switch(n)
-    {
-    case 1:
-    {
-        f(std::integral_constant<std::size_t, 1>{});
-        break;
-    }
-    case 2:
-    {
-        f(std::integral_constant<std::size_t, 2>{});
-        break;
-    }
-    case 3:
-    {
-        f(std::integral_constant<std::size_t, 3>{});
-        break;
-    }
-    case 4:
-    {
-        f(std::integral_constant<std::size_t, 4>{});
-        break;
-    }
-    case 5:
-    {
-        f(std::integral_constant<std::size_t, 5>{});
-        break;
-    }
-    default: throw std::runtime_error("Unknown tensor size");
-    }
-}
+template <std::size_t NDim>
+using hip_tensor_index = hip_array<std::size_t, NDim>;
 
-template <size_t NDim>
-struct hip_index
-{
-    size_t d[NDim];
-    __device__ __host__ size_t& operator[](size_t i) { return d[i]; }
-    __device__ __host__ size_t operator[](size_t i) const { return d[i]; }
-};
-
-template <size_t NDim>
+template <std::size_t NDim>
 struct hip_tensor_descriptor
 {
     __device__ __host__ hip_tensor_descriptor() = default;
@@ -63,12 +22,11 @@ struct hip_tensor_descriptor
         std::copy(s.strides().begin(), s.strides().end(), strides);
     }
 
-    __device__ __host__ hip_index<NDim> multi(size_t idx) const
+    __device__ __host__ hip_tensor_index<NDim> multi(std::size_t idx) const
     {
-        hip_index<NDim> result{};
-        size_t tidx = idx;
-
-        for(size_t is = 0; is < NDim; is++)
+        hip_tensor_index<NDim> result{};
+        std::size_t tidx = idx;
+        for(std::size_t is = 0; is < NDim; is++)
         {
             result[is] = tidx / strides[is];
             tidx       = tidx % strides[is];
@@ -76,16 +34,15 @@ struct hip_tensor_descriptor
 
         return result;
     }
-
-    __device__ __host__ size_t linear(hip_index<NDim> s) const
+    __device__ __host__ std::size_t linear(hip_tensor_index<NDim> s) const
     {
-        size_t idx = 0;
-        for(size_t i = 0; i < NDim; i++)
+        std::size_t idx = 0;
+        for(std::size_t i = 0; i < NDim; i++)
             idx += s[i] * strides[i];
         return idx;
     }
-    size_t lens[NDim]    = {};
-    size_t strides[NDim] = {};
+    std::size_t lens[NDim]    = {};
+    std::size_t strides[NDim] = {};
 };
 
 } // namespace device

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

+
+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_TENSOR_VIEW_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_TENSOR_VIEW_HPP
+
+#include <migraphx/gpu/device/shape.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+template <class T, std::size_t N>
+struct hip_tensor_view
+{
+    using value_type                      = T;
+    using hip_index                       = typename hip_shape<N>::hip_index;
+    __device__ __host__ hip_tensor_view() = default;
+    __host__ hip_tensor_view(tensor_view<T> x) : d(x.data()), s(x.get_shape()) {}
+    __host__ hip_tensor_view(T* x, const shape& ss) : d(x), s(ss) {}
+
+    MIGRAPHX_DEVICE_CONSTEXPR const hip_shape<N>& get_shape() const { return s; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR std::size_t size() const { return s.elements(); }
+
+    MIGRAPHX_DEVICE_CONSTEXPR value_type* data() const { return d; }
+
+    template <class U>
+    MIGRAPHX_DEVICE_CONSTEXPR value_type& operator[](U i) const
+    {
+        return d[s.index(i)];
+    }
+
+    MIGRAPHX_DEVICE_CONSTEXPR value_type* begin() const { return d; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR value_type* end() const { return d + size(); }
+
+    private:
+    value_type* d = nullptr;
+    hip_shape<N> s{};
+};
+
+template <std::size_t N, class T>
+hip_tensor_view<T, N> make_hip_view(const shape& s, T* x)
+{
+    return {x, s};
+}
+
+template <std::size_t N, class T>
+hip_tensor_view<T, N> make_hip_view(tensor_view<T> x)
+{
+    return {x};
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

@@ -8,14 +8,45 @@
 #ifndef MIGRAPHX_GUARD_RTGLIB_GPU_DEVICE_TYPES_HPP
 #define MIGRAPHX_GUARD_RTGLIB_GPU_DEVICE_TYPES_HPP
 
+#include <hip/hip_runtime.h>
 #include <migraphx/half.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/tensor_view.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 namespace device {
 
+#define MIGRAPHX_DEVICE_CONSTEXPR constexpr __device__ __host__ // NOLINT
+
+template <class T, std::size_t N>
+using vec = T __attribute__((ext_vector_type(N)));
+
+template <std::size_t N, class T>
+__device__ __host__ T* as_pointer(vec<T, N>* x)
+{
+    return reinterpret_cast<T*>(x);
+}
+
+template <std::size_t N, class T>
+__device__ __host__ vec<T, N>* as_vec(T* x)
+{
+    return reinterpret_cast<vec<T, N>*>(x);
+}
+
+template <std::size_t N, class T>
+tensor_view<vec<T, N>> as_vec(tensor_view<T> x)
+{
+    return {x.get_shape(), as_vec<N>(x.data())};
+}
+
+template <std::size_t N, class... Ts>
+auto pack_vec(Ts... xs)
+{
+    return [=](auto f, std::size_t n) { return f(as_vec<N>(xs)[n]...); };
+}
+
 using gpu_half = __fp16;
 
 namespace detail {
@@ -25,6 +56,12 @@ struct device_type
     using type = T;
 };
 
+template <class T, std::size_t N>
+struct device_type<vec<T, N>>
+{
+    using type = vec<typename device_type<T>::type, N>;
+};
+
 template <>
 struct device_type<half>
 {
@@ -38,7 +75,7 @@ struct host_type
 };
 
 template <>
-struct device_type<gpu_half>
+struct host_type<gpu_half>
 {
     using type = half;
 };
@@ -54,7 +91,7 @@ using device_type = typename detail::device_type<T>::type;
 template <class T>
 host_type<T> host_cast(T x)
 {
-    return reinterpret_cast<host_type<T>>(x);
+    return reinterpret_cast<const host_type<T>&>(x);
 }
 
 template <class T>
@@ -64,9 +101,9 @@ host_type<T>* host_cast(T* x)
 }
 
 template <class T>
-device_type<T> device_cast(T x)
+device_type<T> device_cast(const T& x)
 {
-    return reinterpret_cast<device_type<T>>(x);
+    return reinterpret_cast<const device_type<T>&>(x);
 }
 
 template <class T>
@@ -76,13 +113,19 @@ device_type<T>* device_cast(T* x)
 }
 
 template <class T>
-T to_hip_type(T x)
+tensor_view<device_type<T>> device_cast(tensor_view<T> x)
+{
+    return {x.get_shape(), reinterpret_cast<device_type<T>*>(x.data())};
+}
+
+template <class T>
+__device__ __host__ T to_hip_type(T x)
 {
     return x;
 }
 
 // Hip doens't support __fp16
-inline float to_hip_type(gpu_half x) { return x; }
+inline __device__ __host__ float to_hip_type(gpu_half x) { return x; }
 
 } // namespace device
 } // namespace gpu

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

+
+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_VECTOR_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_VECTOR_HPP
+
+#include <migraphx/gpu/device/types.hpp>
+#include <vector>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+template <class T, std::size_t N>
+struct hip_vector
+{
+    MIGRAPHX_DEVICE_CONSTEXPR hip_vector() = default;
+    MIGRAPHX_DEVICE_CONSTEXPR hip_vector(std::size_t s) : len(s) {}
+    template <class Iterator>
+    __device__ __host__ hip_vector(Iterator start, Iterator last)
+    {
+        auto it = std::copy(start, last, d);
+        len     = std::distance(d, it);
+    }
+
+    __device__ __host__ hip_vector(std::initializer_list<T> x)
+    {
+        std::copy(x.begin(), x.end(), d);
+        len = x.size();
+    }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T& operator[](std::size_t i) { return d[i]; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T& operator[](std::size_t i) const { return d[i]; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T& front() { return d[0]; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T& front() const { return d[0]; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T& back() { return d[size() - 1]; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T& back() const { return d[size() - 1]; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T* data() { return d; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T* data() const { return d; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR std::size_t size() const { return len; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T* begin() { return d; }
+    MIGRAPHX_DEVICE_CONSTEXPR const T* begin() const { return d; }
+
+    MIGRAPHX_DEVICE_CONSTEXPR T* end() { return d + size(); }
+    MIGRAPHX_DEVICE_CONSTEXPR const T* end() const { return d + size(); }
+
+    template <class U>
+    MIGRAPHX_DEVICE_CONSTEXPR void push_back(U&& x)
+    {
+        d[len] = static_cast<U&&>(x);
+        len++;
+    }
+
+    private:
+    T d[N]          = {};
+    std::size_t len = 0;
+};
+
+template <std::size_t N, class T>
+hip_vector<T, N> to_hip_vector(const std::vector<T>& x)
+{
+    hip_vector<T, N> result(x.size());
+    std::copy(x.begin(), x.end(), result.begin());
+    return result;
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

+
+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_VISIT_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_VISIT_HPP
+
+#include <migraphx/gpu/device/tensor_view.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+template <class F>
+void visit_tensor_size(std::size_t n, F f)
+{
+    switch(n)
+    {
+    case 1:
+    {
+        f(std::integral_constant<std::size_t, 1>{});
+        break;
+    }
+    case 2:
+    {
+        f(std::integral_constant<std::size_t, 2>{});
+        break;
+    }
+    case 3:
+    {
+        f(std::integral_constant<std::size_t, 3>{});
+        break;
+    }
+    case 4:
+    {
+        f(std::integral_constant<std::size_t, 4>{});
+        break;
+    }
+    case 5:
+    {
+        f(std::integral_constant<std::size_t, 5>{});
+        break;
+    }
+    default: throw std::runtime_error("Unknown tensor size");
+    }
+}
+
+inline shape get_shape(const shape& x) { return x; }
+
+template <class T>
+auto get_shape(const T& x) -> decltype(x.get_shape())
+{
+    return x.get_shape();
+}
+
+template <class V, class F, class... Ts>
+void hip_visit_all_impl(const shape& s, F f, V&& v, Ts&&... xs)
+{
+    std::initializer_list<migraphx::shape::type_t> types = {get_shape(xs).type()...};
+    if(!std::all_of(
+           types.begin(), types.end(), [&](migraphx::shape::type_t t) { return t == s.type(); }))
+        MIGRAPHX_THROW("Types must be the same");
+    std::initializer_list<std::size_t> ranks = {get_shape(xs).lens().size()...};
+    if(!std::all_of(
+           ranks.begin(), ranks.end(), [&](std::size_t r) { return r == s.lens().size(); }))
+        MIGRAPHX_THROW("Ranks must be the same");
+    visit_tensor_size(s.lens().size(),
+                      [&](auto ndim) { s.visit_type([&](auto as) { v(f(xs, ndim, as)...); }); });
+}
+
+template <class V, class F, class... Ts>
+void hip_visit_views_impl(const shape& s, F f, V&& v, Ts&&... xs)
+{
+    std::initializer_list<std::size_t> ranks = {get_shape(xs).lens().size()...};
+    if(!std::all_of(
+           ranks.begin(), ranks.end(), [&](std::size_t r) { return r == s.lens().size(); }))
+        MIGRAPHX_THROW("Ranks must be the same");
+    visit_tensor_size(s.lens().size(), [&](auto ndim) { v(f(xs, ndim)...); });
+}
+
+template <class F>
+struct hip_convert
+{
+    F f;
+    template <class RawData, class N, class As>
+    auto operator()(RawData x, N ndim, As as) const
+        -> decltype(make_hip_view<ndim>(x.get_shape(), f(as.from(x.data()))))
+    {
+        return make_hip_view<ndim>(x.get_shape(), f(as.from(x.data())));
+    }
+
+    template <class N, class As>
+    auto operator()(const shape& s, N ndim, As) const
+    {
+        return make_hip_shape<ndim>(s);
+    }
+};
+
+template <class F>
+hip_convert<F> make_hip_convert(F f)
+{
+    return {f};
+}
+
+template <class F>
+struct hip_convert_view
+{
+    F f;
+    template <class T, class N>
+    auto operator()(tensor_view<T> x, N ndim) const
+    {
+        return make_hip_view<ndim>(f(x));
+    }
+
+    template <class N>
+    auto operator()(const shape& s, N ndim) const
+    {
+        return make_hip_shape<ndim>(s);
+    }
+};
+
+template <class F>
+hip_convert_view<F> make_hip_convert_view(F f)
+{
+    return {f};
+}
+
+template <class T, class... Ts>
+auto hip_visit_all(T&& x, Ts&&... xs)
+{
+    return [&](auto f) {
+        hip_visit_all_impl(
+            get_shape(x), make_hip_convert([](auto* p) { return device_cast(p); }), f, x, xs...);
+    };
+}
+
+template <std::size_t N, class T, class... Ts>
+auto hip_vec_visit_all(T&& x, Ts&&... xs)
+{
+    return [&](auto f) {
+        hip_visit_all_impl(get_shape(x),
+                           make_hip_convert([](auto* p) { return as_vec<N>(device_cast(p)); }),
+                           f,
+                           x,
+                           xs...);
+    };
+}
+
+template <class T, class... Ts>
+auto hip_pointer_visit_all(T&& x, Ts&&... xs)
+{
+    return [&](auto f) { visit_all(x, xs...)([&](auto... vs) { f(device_cast(vs.data())...); }); };
+}
+
+template <class T, class... Ts>
+auto hip_visit_views(T&& x, Ts&&... xs)
+{
+    return [&](auto f) {
+        hip_visit_views_impl(get_shape(x),
+                             make_hip_convert_view([](auto v) { return device_cast(v); }),
+                             f,
+                             x,
+                             xs...);
+    };
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/targets/gpu/device/logsoftmax.cpp

 #include <migraphx/shape.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/gpu/device/logsoftmax.hpp>
+#include <migraphx/gpu/device/reduce.hpp>
 #include <migraphx/gpu/device/tensor.hpp>
 #include <migraphx/gpu/device/launch.hpp>
 #include <migraphx/gpu/device/types.hpp>
@@ -11,66 +12,45 @@ inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 namespace device {
 
-argument logsoftmax(hipStream_t stream,
-                    const migraphx::shape& output_shape,
-                    std::vector<migraphx::argument> args,
-                    int axis)
+void logsoftmax(hipStream_t stream, const argument& result, const argument& arg, int axis)
 {
-
-    auto lens         = output_shape.lens();
-    auto num_in_batch = lens[axis];
-    auto batch_lens   = lens;
-    batch_lens[axis]  = 1;
-    migraphx::shape batch_shape{output_shape.type(), batch_lens};
-
-    visit_all(args.back(), args.front())([&](auto output, auto input) {
-        const auto* input_ptr = device_cast(input.data());
-        auto* output_ptr      = device_cast(output.data());
-        visit_tensor_size(batch_shape.lens().size(), [&](auto n_dim) {
-            hip_tensor_descriptor<n_dim> desc_batch(batch_shape);
-            hip_tensor_descriptor<n_dim> desc_data(output_shape);
-
-            // each thread is for one item in the batch
-            gs_launch(stream, batch_shape.elements())([=](auto i) {
-                auto batch_idx = desc_batch.multi(i);
-                auto data_idx  = batch_idx;
-
-                // get max
-                auto batch_max = input_ptr[desc_data.linear(batch_idx)];
-                for(std::size_t j = 1; j < num_in_batch; ++j)
-                {
+    auto lens                  = result.get_shape().lens();
+    auto batch_lens            = lens;
+    std::size_t batch_item_num = lens[axis];
+    batch_lens[axis]           = 1;
+    migraphx::shape batch_shape{result.get_shape().type(), batch_lens};
+
+    hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
+        const std::size_t max_block_size = 256;
+        const std::size_t block_size     = compute_block_size(batch_item_num, max_block_size);
+        gs_launch(stream,
+                  batch_shape.elements() * block_size,
+                  block_size)([=](auto i, auto idx) __device__ {
+            auto data_idx = batch.multi(i / block_size);
+            using type    = device_type<std::remove_cv_t<typename decltype(input)::value_type>>;
+            type init     = lowest();
+
+            auto batch_max = block_reduce<max_block_size>(
+                idx, max{}, init, batch_item_num, [&](auto j) __device__ {
                     data_idx[axis] = j;
-                    size_t idx     = desc_data.linear(data_idx);
-                    batch_max      = std::max(to_hip_type(batch_max), to_hip_type(input_ptr[idx]));
-                }
+                    return input[data_idx];
+                });
 
-                for(std::size_t j = 0; j < num_in_batch; ++j)
-                {
-                    data_idx[axis]  = j;
-                    size_t idx      = desc_data.linear(data_idx);
-                    output_ptr[idx] = input_ptr[idx] - batch_max;
-                }
-
-                auto batch_sum = ::exp(to_hip_type(output_ptr[desc_data.linear(batch_idx)]));
-                for(std::size_t j = 1; j < num_in_batch; ++j)
-                {
+            auto batch_sum =
+                block_reduce<max_block_size>(idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
                     data_idx[axis] = j;
-                    size_t idx     = desc_data.linear(data_idx);
-                    batch_sum += ::exp(to_hip_type(output_ptr[idx]));
-                }
-                batch_sum = ::log(to_hip_type(batch_sum));
+                    auto val       = input[data_idx] - batch_max;
+                    return ::exp(to_hip_type(val));
+                });
 
-                for(std::size_t j = 0; j < num_in_batch; ++j)
-                {
-                    data_idx[axis] = j;
-                    size_t idx     = desc_data.linear(data_idx);
-                    output_ptr[idx] -= batch_sum;
-                }
+            auto log_batch_sum = ::log(to_hip_type(batch_sum)) + batch_max;
+
+            idx.local_stride(batch_item_num, [&](auto j) {
+                data_idx[axis]   = j;
+                output[data_idx] = input[data_idx] - log_batch_sum;
             });
         });
     });
-
-    return args.back();
 }
 
 } // namespace device

src/targets/gpu/device/pad.cpp

@@ -4,6 +4,7 @@
 #include <migraphx/gpu/device/pad.hpp>
 #include <migraphx/gpu/device/tensor.hpp>
 #include <migraphx/gpu/device/launch.hpp>
+#include <migraphx/float_equal.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -14,24 +15,26 @@ 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();
+    hip_visit_all(result, arg1)([&](auto output, auto input) {
+        using type      = typename decltype(output)::value_type;
+        using hip_index = typename decltype(output)::hip_index;
+        type device_val = value;
+        if(float_equal(value, std::numeric_limits<float>::lowest()))
+        {
+            device_val = device_cast(std::numeric_limits<type>::lowest());
+        }
+        gs_launch(stream,
+                  result.get_shape().elements())([=](auto i) { output.data()[i] = device_val; });
 
-    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];
-            });
+        hip_index offsets;
+        std::copy(pads.begin(), pads.begin() + offsets.size(), offsets.begin());
+        gs_launch(stream, nelements)([=](auto i) {
+            auto idx = input.get_shape().multi(i);
+            for(std::size_t j = 0; j < offsets.size(); j++)
+            {
+                idx[j] += offsets[j];
+            }
+            output[idx] = input.data()[i];
         });
     });
     return result;

src/targets/gpu/device/reduce_sum.cpp

+#include <migraphx/gpu/device/reduce_sum.hpp>
+#include <migraphx/gpu/device/reduce.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void reduce_sum(hipStream_t stream, const argument& result, const argument& arg)
+{
+    reduce(stream, result, arg, sum{}, 0, id{}, id{});
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx