Reorganize project folders (#6)

example/19_binary_elementwise/elementwise_add_4d.cpp

@@ -0,0 +1,125 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#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_dynamic_vector_dims_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 ABDataType = F16;
+using CDataType  = F16;
+
+using Add = ck::tensor_operation::element_wise::Add;
+
+using DeviceElementwiseAddInstance =
+    ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
+                                                        ck::Tuple<CDataType>,
+                                                        Add,
+                                                        4,
+                                                        64,
+                                                        2,
+                                                        128,
+                                                        2,
+                                                        2,
+                                                        ck::Sequence<1, 0>,
+                                                        ck::Sequence<2, 2>,
+                                                        ck::Sequence<2>>;
+
+template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
+void host_elementwise4D(HostTensorC& C,
+                        const HostTensorA& A,
+                        const HostTensorB& B,
+                        const std::vector<std::size_t>& shape,
+                        Functor functor)
+{
+    using ctype = ck::remove_reference_t<decltype(C(0, 0, 0, 0))>;
+
+    for(std::size_t n = 0; n < shape[0]; ++n)
+        for(std::size_t c = 0; c < shape[1]; ++c)
+            for(std::size_t h = 0; h < shape[2]; ++h)
+                for(std::size_t w = 0; w < shape[3]; ++w)
+                {
+                    auto a_val  = A(n, c, h, w);
+                    auto b_val  = B(n, c, h, w);
+                    ctype c_val = 0;
+                    functor(c_val, a_val, b_val);
+                    C(n, c, h, w) = c_val;
+                }
+}
+
+int main()
+{
+    bool do_verification = true;
+    bool time_kernel     = false;
+
+    std::vector<std::size_t> nchw = {4, 16, 32, 32};
+
+    Tensor<ABDataType> a(nchw);
+    Tensor<ABDataType> b(nchw);
+    Tensor<CDataType> c(nchw);
+
+    a.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
+    b.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
+
+    DeviceMem a_device_buf(sizeof(ABDataType) * a.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(ABDataType) * b.mDesc.GetElementSpaceSize());
+    DeviceMem c_device_buf(sizeof(CDataType) * c.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a.mData.data());
+    b_device_buf.ToDevice(b.mData.data());
+
+    std::array<const void*, 2> input = {a_device_buf.GetDeviceBuffer(),
+                                        b_device_buf.GetDeviceBuffer()};
+    std::array<void*, 1> output      = {c_device_buf.GetDeviceBuffer()};
+
+    std::array<ck::index_t, 4> abc_lengths;
+    std::array<ck::index_t, 4> a_strides;
+    std::array<ck::index_t, 4> b_strides;
+    std::array<ck::index_t, 4> c_strides;
+
+    ck::ranges::copy(nchw, abc_lengths.begin());
+    ck::ranges::copy(a.mDesc.GetStrides(), a_strides.begin());
+    ck::ranges::copy(b.mDesc.GetStrides(), b_strides.begin());
+    ck::ranges::copy(c.mDesc.GetStrides(), c_strides.begin());
+
+    auto broadcastAdd = DeviceElementwiseAddInstance{};
+    auto argument     = broadcastAdd.MakeArgumentPointer(
+        abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
+
+    if(!broadcastAdd.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "The runtime parameters seems not supported by the device instance, exiting!");
+    };
+
+    auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
+    float ave_time =
+        broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+
+    std::cout << "Perf: " << ave_time << " ms" << std::endl;
+
+    bool pass = true;
+    if(do_verification)
+    {
+        c_device_buf.FromDevice(c.mData.data());
+        Tensor<CDataType> host_c(nchw);
+
+        host_elementwise4D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
+            host_c, a, b, nchw, Add{});
+
+        pass &= ck::utils::check_err(c, host_c, "Error: Incorrect results c", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}