elementwise op (#238)

* Add elementwise operation kernel and example * Add comment * Add template argument of dim . Prepare to support multiple dimension * Rename example * Support 1 dimension * Add static assert * Add comment * Extract pad * Remove redundant argument * Support any dimension for elementwise operation * Remove line * Let it be the multiple number of CU * Move thread per block to the parameter of constructor * rename threadPerBlock with blockSize * Support double * rename kernel function name * remove redundant include header * Refine type * Need to the final dimension * Refine variable name * Refine type * Use index_t instead of int in API Co-authored-by: rocking <chunylai@amd.com>
2026-05-02 12:41:26 +00:00 · 2022-05-19 12:34:35 +08:00
parent 9f71ff48e2
commit aafc3ac27a
10 changed files with 759 additions and 0 deletions
--- a/example/19_binary_elementwise/elementwise_add_4d.cpp
+++ b/example/19_binary_elementwise/elementwise_add_4d.cpp
@@ -0,0 +1,113 @@
+#include <iostream>
+#include <cstdlib>
+#include "check_err.hpp"
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "host_utility.hpp"
+
+#include "device_tensor.hpp"
+#include "binary_element_wise_operation.hpp"
+#include "device_binary_elementwise.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using ABDataType             = F16;
+using CDataType              = F16;
+using EltwiseComputeDataType = F32;
+
+using Add = ck::tensor_operation::binary_element_wise::Add;
+
+using DeviceElementwiseAddInstance = ck::tensor_operation::device::
+    DeviceBinaryElementwise<ABDataType, ABDataType, CDataType, EltwiseComputeDataType, Add, 4, 8>;
+
+template <typename HostTensorA,
+          typename HostTensorB,
+          typename HostTensorC,
+          typename ComputeDataType,
+          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)
+                {
+                    ComputeDataType a_val = static_cast<ComputeDataType>(A(n, c, h, w));
+                    ComputeDataType b_val = static_cast<ComputeDataType>(B(n, c, h, w));
+                    ComputeDataType c_val = 0;
+                    functor(c_val, a_val, b_val);
+                    C(n, c, h, w) = static_cast<ctype>(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_m(nchw);
+    Tensor<ABDataType> b_m(nchw);
+    Tensor<ABDataType> c_m(nchw);
+
+    a_m.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
+    b_m.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
+
+    DeviceMem a_m_device_buf(sizeof(ABDataType) * a_m.mDesc.GetElementSpace());
+    DeviceMem b_m_device_buf(sizeof(ABDataType) * b_m.mDesc.GetElementSpace());
+    DeviceMem c_m_device_buf(sizeof(CDataType) * c_m.mDesc.GetElementSpace());
+
+    a_m_device_buf.ToDevice(a_m.mData.data());
+    b_m_device_buf.ToDevice(b_m.mData.data());
+
+    auto broadcastAdd = DeviceElementwiseAddInstance{};
+    auto argument     = broadcastAdd.MakeArgumentPointer(
+        a_m_device_buf.GetDeviceBuffer(),
+        b_m_device_buf.GetDeviceBuffer(),
+        c_m_device_buf.GetDeviceBuffer(),
+        ck::convert_vector_element_type<std::size_t, ck::index_t>(nchw),
+        ck::convert_vector_element_type<std::size_t, ck::index_t>(a_m.mDesc.GetStrides()),
+        ck::convert_vector_element_type<std::size_t, ck::index_t>(b_m.mDesc.GetStrides()),
+        ck::convert_vector_element_type<std::size_t, ck::index_t>(c_m.mDesc.GetStrides()),
+        Add{});
+
+    if(!broadcastAdd.IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error("The runtime parameters seems not supported by the "
+                                 "DeviceBinaryElementwise_2D 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_m_device_buf.FromDevice(c_m.mData.data());
+        Tensor<CDataType> host_c_m(nchw);
+
+        host_elementwise4D<Tensor<ABDataType>,
+                           Tensor<ABDataType>,
+                           Tensor<CDataType>,
+                           EltwiseComputeDataType,
+                           Add>(host_c_m, a_m, b_m, nchw, Add{});
+
+        pass &= ck::utils::check_err(
+            c_m.mData, host_c_m.mData, "Error: Incorrect results d1", 1e-3, 1e-3);
+    }
+
+    return pass ? 0 : 1;
+}