Hip tensor permute (#1002)

* adding files for F32 example

* adding functioning implementation with scalar multiplication and unary operator support

* added fp 16 type check in unary square

* updating scalar multiplication as an operator

* functioning version with scalar operator

* changing strides for col major

* updated column major implementation

* working column major implementation

* cleaned up comments, rearranged/renamed files

example/44_elementwise_permute/CMakeLists.txt

@@ -1,4 +1,8 @@
 add_example_executable(example_elementwise_permute_4D_fp16 elementwise_permute_4D_fp16.cpp)
 add_example_executable(example_elementwise_permute_4D_fp16_2d elementwise_permute_4D_fp16_2d.cpp)
+add_example_executable(example_elementwise_permute_4D_fp32_row elementwise_permute_4D_fp32_row.cpp)
+add_example_executable(example_elementwise_permute_4D_fp16_row elementwise_permute_4D_fp16_row.cpp)
+add_example_executable(example_elementwise_permute_4D_fp32_col elementwise_permute_4D_fp32_col.cpp)
+add_example_executable(example_elementwise_permute_4D_fp16_col elementwise_permute_4D_fp16_col.cpp)
 add_example_executable(example_elementwise_permute elementwise_permute.cpp)
 add_example_executable(example_elementwise_permute_3d elementwise_permute_3d.cpp)

example/44_elementwise_permute/elementwise_permute_4D_fp16_col.cpp

@@ -0,0 +1,149 @@
+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F16;
+using BDataType = F16;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using UnaryOp     = ck::tensor_operation::element_wise::UnarySquare;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // ElementwiseOp
+                                                        UnaryOp,              // UnaryOp
+                                                        Scale,                // Scalar
+                                                        4,                    // NumDim
+                                                        8,                    // MPerThread
+                                                        ck::Sequence<1>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
+void host_elementwise4D(HostTensorB& B_nhwc,
+                        const HostTensorA& A_nchw,
+                        FunctorA functor_a,
+                        FunctorB functor_b,
+                        float scale)
+{
+    std::size_t N = A_nchw.mDesc.GetLengths()[0];
+    std::size_t C = A_nchw.mDesc.GetLengths()[1];
+    std::size_t H = A_nchw.mDesc.GetLengths()[2];
+    std::size_t W = A_nchw.mDesc.GetLengths()[3];
+    for(std::size_t w = 0; w < W; ++w)
+        for(std::size_t h = 0; h < H; ++h)
+            for(std::size_t c = 0; c < C; ++c)
+                for(std::size_t n = 0; n < N; ++n)
+                {
+                    ADataType tmp_val;
+                    // auto a_val = A_nchw(n, c, h, w);
+                    auto a_val = A_nchw.mData[(n) + (c * N) + (h * C * N) + (w * H * C * N)];
+                    functor_b(tmp_val, a_val);
+                    // functor_a(B_nhwc(n, h, w, c), scale * tmp_val);
+                    functor_a(B_nhwc.mData[(n) + (c * W * H * N) + (h * N) + (w * H * N)],
+                              scale * tmp_val);
+                }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    std::vector<std::size_t> nchw = {4, 2, 1, 8};
+    std::vector<std::size_t> nhwc = {4, 1, 8, 2};
+    Tensor<ADataType> a(nchw);
+    Tensor<BDataType> b(nhwc);
+    float scale = 1.f;
+    auto i      = 0;
+    for(std::size_t w = 0; w < a.mDesc.GetLengths()[3]; ++w)
+        for(std::size_t h = 0; h < a.mDesc.GetLengths()[2]; ++h)
+            for(std::size_t c = 0; c < a.mDesc.GetLengths()[1]; ++c)
+                for(std::size_t n = 0; n < a.mDesc.GetLengths()[0]; ++n)
+                {
+                    a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
+                            (h * nchw[3]) + w] = i;
+                    i++;
+                }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 4> ab_lengths;
+
+    std::array<ck::index_t, 4> a_strides = {1,
+                                            static_cast<int>(nchw[0]),
+                                            static_cast<int>(nchw[0] * nchw[1]),
+                                            static_cast<int>(nchw[0] * nchw[1] * nchw[2])};
+
+    std::array<ck::index_t, 4> b_strides = {1,
+                                            static_cast<int>(nhwc[0] * nhwc[1] * nhwc[2]),
+                                            static_cast<int>(nhwc[0]),
+                                            static_cast<int>(nhwc[0] * nhwc[1])};
+    ck::ranges::copy(nchw, ab_lengths.begin());
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(ab_lengths,
+                                                         {a_strides},
+                                                         {b_strides},
+                                                         input,
+                                                         output,
+                                                         PassThrough{},
+                                                         UnaryOp{},
+                                                         Scale{scale});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (nchw): " << a.mDesc << std::endl;
+    std::cout << "B (nhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
+
+    std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
+                            sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(nhwc);
+        host_elementwise4D(host_b, a, PassThrough{}, UnaryOp{}, scale);
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}

example/44_elementwise_permute/elementwise_permute_4D_fp16_row.cpp

@@ -0,0 +1,132 @@
+#include <iostream>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ADataType = F16;
+using BDataType = F16;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using UnaryOp     = ck::tensor_operation::element_wise::UnarySquare;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+using DeviceElementwisePermuteInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ADataType>, // InDataTypeTuple
+                                                        ck::Tuple<BDataType>, // OutDataTypeTuple
+                                                        PassThrough,          // ElementwiseOp
+                                                        UnaryOp,              // UnaryOp
+                                                        Scale,                // Scalar
+                                                        4,                    // NumDim
+                                                        8,                    // MPerThread
+                                                        ck::Sequence<8>,  // InScalarPerVectorSeq
+                                                        ck::Sequence<1>>; // OutScalarPerVectorSeq
+
+template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
+void host_elementwise4D(HostTensorB& B_nhwc,
+                        const HostTensorA& A_nchw,
+                        FunctorA functor_a,
+                        FunctorB functor_b,
+                        float scale)
+{
+    for(std::size_t n = 0; n < A_nchw.mDesc.GetLengths()[0]; ++n)
+        for(std::size_t c = 0; c < A_nchw.mDesc.GetLengths()[1]; ++c)
+            for(std::size_t h = 0; h < A_nchw.mDesc.GetLengths()[2]; ++h)
+                for(std::size_t w = 0; w < A_nchw.mDesc.GetLengths()[3]; ++w)
+                {
+                    ADataType tmp_val;
+                    auto a_val = A_nchw(n, c, h, w);
+                    functor_b(tmp_val, a_val);
+                    functor_a(B_nhwc(n, h, w, c), scale * tmp_val);
+                }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = true;
+
+    std::vector<std::size_t> nchw = {16, 128, 32, 64};
+    std::vector<std::size_t> nhwc = {16, 32, 64, 128};
+    Tensor<ADataType> a(nchw);
+    Tensor<BDataType> b(nhwc);
+    float scale = 2.f;
+    a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+
+    std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {b_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 4> ab_lengths;
+    std::array<ck::index_t, 4> a_strides = {static_cast<int>(nchw[1] * nchw[2] * nchw[3]),
+                                            static_cast<int>(nchw[2] * nchw[3]),
+                                            static_cast<int>(nchw[3]),
+                                            1};
+    std::array<ck::index_t, 4> b_strides = {static_cast<int>(nhwc[1] * nhwc[2] * nhwc[3]),
+                                            1,
+                                            static_cast<int>(nhwc[2] * nhwc[3]),
+                                            static_cast<int>(nhwc[3])};
+
+    ck::ranges::copy(nchw, ab_lengths.begin());
+
+    auto broadcastPermute = DeviceElementwisePermuteInstance{};
+    auto argument         = broadcastPermute.MakeArgumentPointer(ab_lengths,
+                                                         {a_strides},
+                                                         {b_strides},
+                                                         input,
+                                                         output,
+                                                         PassThrough{},
+                                                         UnaryOp{},
+                                                         Scale{scale});
+
+    if(!broadcastPermute.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    std::cout << "A (nchw): " << a.mDesc << std::endl;
+    std::cout << "B (nhwc): " << b.mDesc << std::endl;
+
+    auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
+    float ave_time =
+        broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+    std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
+
+    std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
+                            sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    bool pass = true;
+
+    if(do_verification)
+    {
+        b_device_buf.FromDevice(b.mData.data());
+        Tensor<BDataType> host_b(nhwc);
+        host_elementwise4D(host_b, a, PassThrough{}, UnaryOp{}, scale);
+
+        pass &=
+            ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}