rebased agaist develop

composable_kernel/include/tensor_operation/gridwise_gemm_xdlops_v2r3.hpp

@@ -8,6 +8,7 @@
 #include "blockwise_gemm_xdlops.hpp"
 #include "blockwise_tensor_slice_transfer_v4r1.hpp"
 #include "threadwise_tensor_slice_transfer_using_space_filling_curve.hpp"
+#include "gridwise_gemm_pipeline_v1.hpp"
 
 namespace ck {
 

example/CMakeLists.txt

@@ -49,3 +49,6 @@ target_link_libraries(conv2d_fwd_xdl_int8 PRIVATE host_tensor)
 target_link_libraries(conv3d_fwd_xdl PRIVATE host_tensor)
 target_link_libraries(convnd_fwd_xdl PRIVATE host_tensor)
 
+set(CONV2D_FWD_XDL_SFCURVE_SOURCE conv2d_fwd_xdl_sfcurve/conv2d_fwd_xdl_sfcurve.cpp)
+add_executable(conv2d_fwd_xdl_sfcurve ${CONV2D_FWD_XDL_SFCURVE_SOURCE})
+target_link_libraries(conv2d_fwd_xdl_sfcurve PRIVATE host_tensor)

example/conv2d_fwd_xdl_sfcurve/README.md

+# Instructions for ```conv2d_fwd_xdl``` Example
+
+## Docker script
+```bash
+docker run                                                                   \
+-it                                                                          \
+--rm                                                                         \
+--privileged                                                                 \
+--group-add sudo                                                             \
+-w /root/workspace                                                           \
+-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace                                \
+rocm/tensorflow:rocm4.3.1-tf2.6-dev                                          \
+/bin/bash
+```
+
+## Build ```conv2d_fwd_xdl```
+```bash
+mkdir build && cd build
+```
+
+```bash
+# Need to specify target ID, example below is gfx908
+cmake                                                                  \
+-D BUILD_DEV=OFF                                                       \
+-D CMAKE_BUILD_TYPE=Release                                            \
+-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 "   \
+-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                              \
+-D CMAKE_PREFIX_PATH=/opt/rocm                                         \
+..
+```
+
+```bash
+ make -j conv2d_fwd_xdl
+```
+
+## Run ```conv2d_fwd_xdl_int8```
+```bash
+#arg1: verification (0=no, 1=yes)
+#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
+#arg3: run kernel # of times (>1)
+#arg4 to 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx
+./example/conv2d_fwd_xdl_int8 0 1 5
+```
+
+Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
+```
+in_n_c_hi_wi: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
+wei_k_c_y_x: dim 4, lengths {256, 192, 3, 3}, strides {1728, 1, 576, 192}
+out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
+arg.a_grid_desc_k0_m_k1_{216, 165888, 8}
+arg.b_grid_desc_k0_n_k1_{216, 256, 8}
+arg.c_grid_desc_m_n_{ 165888, 256}
+launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
+Warm up
+Start running 5 times...
+Perf: 1.43206 ms, 102.486 TFlops, 232.947 GB/s
+```

example/conv2d_fwd_xdl_sfcurve/conv2d_fwd_xdl_sfcurve.cpp

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+#include "config.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "tensor_layout.hpp"
+#include "device_conv2d_fwd_xdl_nhwc_kyxc_nhwk.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_conv_fwd.hpp"
+#include "convolution_utility.hpp"
+
+// using InDataType  = int8_t;
+// using WeiDataType = int8_t;
+// using OutDataType = int8_t;
+// using AccDataType = int32_t;
+using InDataType  = ck::half_t;
+using WeiDataType = ck::half_t;
+using OutDataType = ck::half_t;
+using AccDataType = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InLayout  = ck::tensor_layout::convolution::NHWC;
+using WeiLayout = ck::tensor_layout::convolution::KYXC;
+using OutLayout = ck::tensor_layout::convolution::NHWK;
+
+using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
+using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
+using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto ConvFwdDefault =
+    ck::tensor_operation::device::ConvolutionForwardSpecialization_t::Default;
+
+using DeviceConvFwdInstance = ck::tensor_operation::device::
+    DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
+        InDataType,     // InDataType
+        WeiDataType,    // WeiDataType
+        OutDataType,    // OutDataType
+        AccDataType,    // AccDataType
+        PassThrough,    // InElementwiseOperation
+        PassThrough,    // WeiElementwiseOperation
+        PassThrough,    // OutElementwiseOperation
+        ConvFwdDefault, // ConvForwardSpecialization
+        256,            // BlockSize
+        128,            // MPerBlock
+        256,            // NPerBlock
+        4,              // K0PerBlock
+        8,             // K1
+        32,             // MPerXdl
+        32,             // NPerXdl
+        2,              // MXdlPerWave
+        4,              // NXdlPerWave
+        S<4, 64, 1>,    // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<1, 0, 2>,     // ABlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,     // ABlockTransferSrcAccessOrder
+        2,              // ABlockTransferSrcVectorDim
+        8,             // ABlockTransferSrcScalarPerVector
+        8,             // ABlockTransferDstScalarPerVector_K1
+        true,           // ABlockLdsAddExtraM
+        S<4, 64, 1>,    // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<1, 0, 2>,     // BBlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,     // BBlockTransferSrcAccessOrder
+        2,              // BBlockTransferSrcVectorDim
+        8,             // BBlockTransferSrcScalarPerVector
+        8,             // BBlockTransferDstScalarPerVector_K1
+        true,           // BBlockLdsAddExtraN
+        7,              // CThreadTransferSrcDstVectorDim
+        1>;             // CThreadTransferDstScalarPerVector
+
+using ReferenceConvFwdInstance = ck::tensor_operation::host::
+    ReferenceConvFwd<InDataType, WeiDataType, OutDataType, InElementOp, WeiElementOp, OutElementOp>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    int nrepeat          = 5;
+
+    // Conv shape
+    ck::index_t N               = 128;
+    ck::index_t K               = 256;
+    ck::index_t C               = 192;
+    ck::index_t Y               = 3;
+    ck::index_t X               = 3;
+    ck::index_t Hi              = 71;
+    ck::index_t Wi              = 71;
+    ck::index_t conv_stride_h   = 2;
+    ck::index_t conv_stride_w   = 2;
+    ck::index_t conv_dilation_h = 1;
+    ck::index_t conv_dilation_w = 1;
+    ck::index_t in_left_pad_h   = 1;
+    ck::index_t in_left_pad_w   = 1;
+    ck::index_t in_right_pad_h  = 1;
+    ck::index_t in_right_pad_w  = 1;
+
+    if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+    }
+    else if(argc == 19)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        nrepeat         = std::stoi(argv[3]);
+
+        N               = std::stoi(argv[4]);
+        K               = std::stoi(argv[5]);
+        C               = std::stoi(argv[6]);
+        Y               = std::stoi(argv[7]);
+        X               = std::stoi(argv[8]);
+        Hi              = std::stoi(argv[9]);
+        Wi              = std::stoi(argv[10]);
+        conv_stride_h   = std::stoi(argv[11]);
+        conv_stride_w   = std::stoi(argv[12]);
+        conv_dilation_h = std::stoi(argv[13]);
+        conv_dilation_w = std::stoi(argv[14]);
+        in_left_pad_h   = std::stoi(argv[15]);
+        in_left_pad_w   = std::stoi(argv[16]);
+        in_right_pad_h  = std::stoi(argv[17]);
+        in_right_pad_w  = std::stoi(argv[18]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg4 to 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
+               "RightPx\n");
+        exit(0);
+    }
+
+    const std::vector<ck::index_t> conv_filter_strides{conv_stride_h, conv_stride_w};
+    const std::vector<ck::index_t> conv_filter_dilations{conv_dilation_h, conv_dilation_w};
+    const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w};
+    const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w};
+    const auto output_spatial_lengths =
+        ck::tensor_operation::ConvolutionUtility::ComputeOutputSpatialLengths({Hi, Wi},
+                                                                              {Y, X},
+                                                                              conv_filter_strides,
+                                                                              conv_filter_dilations,
+                                                                              input_left_pads,
+                                                                              input_right_pads);
+
+    const ck::index_t Ho = output_spatial_lengths[0];
+    const ck::index_t Wo = output_spatial_lengths[1];
+
+    // tensor layout
+    auto f_host_tensor_descriptor = [](std::size_t N_,
+                                       std::size_t C_,
+                                       std::size_t H,
+                                       std::size_t W,
+                                       auto layout) {
+        if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
+                     ck::is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
+                     ck::is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
+        {
+            return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
+                                        std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
+        }
+        else if constexpr(ck::is_same<decltype(layout),
+                                      ck::tensor_layout::convolution::NHWC>::value ||
+                          ck::is_same<decltype(layout),
+                                      ck::tensor_layout::convolution::KYXC>::value ||
+                          ck::is_same<decltype(layout),
+                                      ck::tensor_layout::convolution::NHWK>::value)
+        {
+            return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
+                                        std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
+        }
+    };
+
+    Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
+    Tensor<WeiDataType> wei_k_c_y_x(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
+    Tensor<OutDataType> out_n_k_ho_wo_host_result(
+        f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
+    Tensor<OutDataType> out_n_k_ho_wo_device_result(
+        f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
+
+    std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
+    std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
+    std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-1, 1});
+        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-1, 1});
+        break;
+    default:
+        in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{0, 1});
+        wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-1, 1});
+    }
+
+    DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpace());
+    DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
+    DeviceMem out_device_buf(sizeof(OutDataType) *
+                             out_n_k_ho_wo_device_result.mDesc.GetElementSpace());
+
+    in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
+    wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
+
+    // do GEMM
+    auto conv     = DeviceConvFwdInstance{};
+    auto invoker  = conv.MakeInvoker();
+    auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
+                                      static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
+                                      static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
+                                      N,
+                                      K,
+                                      C,
+                                      std::vector<ck::index_t>{Hi, Wi},
+                                      std::vector<ck::index_t>{Y, X},
+                                      std::vector<ck::index_t>{Ho, Wo},
+                                      conv_filter_strides,
+                                      conv_filter_dilations,
+                                      input_left_pads,
+                                      input_right_pads,
+                                      InElementOp{},
+                                      WeiElementOp{},
+                                      OutElementOp{});
+
+    if(!conv.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_conv with the specified compilation parameters does "
+            "not support this Conv problem");
+    }
+
+    float ave_time = invoker.Run(argument, nrepeat);
+
+    std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
+
+    std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
+                            sizeof(WeiDataType) * (K * C * Y * X) +
+                            sizeof(OutDataType) * (N * K * Ho * Wo);
+
+    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;
+
+    if(do_verification)
+    {
+        auto ref_conv    = ReferenceConvFwdInstance{};
+        auto ref_invoker = ref_conv.MakeInvoker();
+
+        auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi,
+                                                  wei_k_c_y_x,
+                                                  out_n_k_ho_wo_host_result,
+                                                  conv_filter_strides,
+                                                  conv_filter_dilations,
+                                                  input_left_pads,
+                                                  input_right_pads,
+                                                  InElementOp{},
+                                                  WeiElementOp{},
+                                                  OutElementOp{});
+
+        ref_invoker.Run(ref_argument);
+
+        out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
+
+        check_error(out_n_k_ho_wo_host_result, out_n_k_ho_wo_device_result);
+    }
+}