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Unverified Commit 627d8ef3 authored by ltqin's avatar ltqin Committed by GitHub
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Backward weight v4r4r2 with xdlops (#18) 



* start

* modify transformat

* modify device convolutiion

* modify host

* added host conv bwd and wrw

* remove bwd, seperate wrw

* clean

* hacall k to zero

* out log

* fixed

* fixed

* change to (out in wei)

* input hack

* hack to out

* format

* fix by comments

* change wei hacks(wei transform has not merge)

* fix program once issue

* fix review comment

* fix vector load issue

* tweak
Co-authored-by: default avatarltqin <letaoqin@amd.com>
Co-authored-by: default avatarJing Zhang <jizhan@amd.com>
Co-authored-by: default avatarChao Liu <chao.liu2@amd.com>
parent 10bb8110
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Showing with 783 additions and 139 deletions
composable_kernel/include/problem_transform/transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp 0 → 100644
View file @ 627d8ef3
#ifndef CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R2_NCHW_KCYX_NKHW_HPP
#define CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R2_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// GemmM = K
// GemmK = N * Ho * Wo
// GemmN = C * Y * X
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
const TensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
const TensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
const TensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto GemmK1 = Number<GemmK1Value>{};
const auto N = in_n_c_hi_wi_grid_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_grid_desc.GetLength(I1);
const auto K = out_n_k_ho_wo_grid_desc.GetLength(I1);
const auto Hi = in_n_c_hi_wi_grid_desc.GetLength(I2);
const auto Wi = in_n_c_hi_wi_grid_desc.GetLength(I3);
const auto Ho = out_n_k_ho_wo_grid_desc.GetLength(I2);
const auto Wo = out_n_k_ho_wo_grid_desc.GetLength(I3);
const auto Y = wei_k_c_y_x_grid_desc.GetLength(I2);
const auto X = wei_k_c_y_x_grid_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
const auto GemmM = K;
const auto GemmN = C * Y * X;
const auto GemmK = N * Ho * Wo;
const auto GemmK0 = GemmK / GemmK1;
// weight tensor
const auto wei_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(C * Y * X)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// input tensor
const auto in_n_c_hip_wip_grid_desc = transform_tensor_descriptor(
in_n_c_hi_wi_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_c_y_ho_x_wo_grid_desc = transform_tensor_descriptor(
in_n_c_hip_wip_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_gemmk_gemmn_grid_desc =
transform_tensor_descriptor(in_n_c_y_ho_x_wo_grid_desc,
make_tuple(make_merge_transform(make_tuple(C, Y, X)),
make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
const auto in_gemmk0_gemmn_gemmk1_grid_desc =
transform_tensor_descriptor(in_gemmk_gemmn_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
// output tensor
const auto out_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo)),
make_tuple(make_pass_through_transform(K), make_merge_transform(make_tuple(N, Ho * Wo))),
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
const auto out_gemmk0_gemmm_gemmk1_grid_desc =
transform_tensor_descriptor(out_gemmk_gemmm_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmM)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
wei_gemmm_gemmn_grid_desc);
}
} // namespace ck
#endif
host/driver_offline/CMakeLists.txt
View file @ 627d8ef3
...@@ -13,9 +13,12 @@ include_directories(BEFORE ...@@ -13,9 +13,12 @@ include_directories(BEFORE
set(CONV_FWD_DRIVER_OFFLINE_SOURCE src/conv_fwd_driver_offline.cpp) set(CONV_FWD_DRIVER_OFFLINE_SOURCE src/conv_fwd_driver_offline.cpp)
set(CONV_BWD_DRIVER_OFFLINE_SOURCE src/conv_bwd_driver_offline.cpp) set(CONV_BWD_DRIVER_OFFLINE_SOURCE src/conv_bwd_driver_offline.cpp)
set(CONV_WRW_DRIVER_OFFLINE_SOURCE src/conv_wrw_driver_offline.cpp)
add_executable(conv_fwd_driver_offline ${CONV_FWD_DRIVER_OFFLINE_SOURCE}) add_executable(conv_fwd_driver_offline ${CONV_FWD_DRIVER_OFFLINE_SOURCE})
add_executable(conv_bwd_driver_offline ${CONV_BWD_DRIVER_OFFLINE_SOURCE}) add_executable(conv_bwd_driver_offline ${CONV_BWD_DRIVER_OFFLINE_SOURCE})
add_executable(conv_wrw_driver_offline ${CONV_WRW_DRIVER_OFFLINE_SOURCE})
target_link_libraries(conv_fwd_driver_offline PRIVATE host_tensor) target_link_libraries(conv_fwd_driver_offline PRIVATE host_tensor)
target_link_libraries(conv_bwd_driver_offline PRIVATE host_tensor) target_link_libraries(conv_bwd_driver_offline PRIVATE host_tensor)
target_link_libraries(conv_wrw_driver_offline PRIVATE host_tensor)
host/driver_offline/include/device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp
View file @ 627d8ef3
...@@ -208,20 +208,20 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk( ...@@ -208,20 +208,20 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
// HACK: hacks that control index calculation when iterating over A, B, C matrix // HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: gemmk0 make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: gemmm Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: gemmk1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 0-: Gemmk0 make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: Gemmm Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: Gemmk1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto out_gemmk0_gemmn_gemmk1_grid_step_hacks = make_tuple( constexpr auto out_gemmk0_gemmn_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: gemmk0 make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{}, // 1+: gemmn Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{}, // 1+: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: gemmk1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 0-: gemmk0 make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{}, // 1-: gemmn Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{}, // 1-: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: gemmk1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: GemmK1
// clang-format off // clang-format off
constexpr auto in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks = make_tuple( constexpr auto in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks = make_tuple(
......
host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk.hpp → host/driver_offline/include/device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp
View file @ 627d8ef3
#include <unistd.h> #include <unistd.h>
#include "device.hpp" #include "device.hpp"
#include "host_tensor.hpp" #include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk.hpp" #include "transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp"
#include "driver_gemm_xdlops_v2r3.hpp" #include "driver_gemm_xdlops_v2r3.hpp"
template <typename TInWei, template <typename TInWei,
...@@ -14,17 +14,17 @@ template <typename TInWei, ...@@ -14,17 +14,17 @@ template <typename TInWei,
typename ConvDilations, typename ConvDilations,
typename InLeftPads, typename InLeftPads,
typename InRightPads> typename InRightPads>
void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk( void device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
const InLengths& in_n_hi_wi_c_lengths, const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_y_x_c_lengths, const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_ho_wo_k_lengths, const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides, const ConvStrides& conv_strides,
const ConvDilations& conv_dilations, const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads, const InLeftPads& in_left_pads,
const InRightPads& in_right_pads, const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_hi_wi_c, const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_y_x_c, Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_ho_wo_k, const Tensor<TOut>& out_n_k_ho_wo,
ck::index_t nrepeat) ck::index_t nrepeat)
{ {
using namespace ck; using namespace ck;
...@@ -34,55 +34,21 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk( ...@@ -34,55 +34,21 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
constexpr auto I0 = Number<0>{}; constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{}; constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{}; constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace()); DeviceMem in_n_c_hi_wi_device_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace()); DeviceMem wei_k_c_y_x_device_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_ho_wo_k_device_buf(sizeof(TOut) * out_n_ho_wo_k.mDesc.GetElementSpace()); DeviceMem out_n_k_ho_wo_device_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_hi_wi_c_device_buf.ToDevice(in_n_hi_wi_c.mData.data()); in_n_c_hi_wi_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data()); wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data()); out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths); const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths); const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths); const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 1 #if 1
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32 // [M, N, K0, K1] = [128, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerWave = 32;
constexpr index_t GemmNPerWave = 32;
constexpr index_t GemmK1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 4>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 4>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 4;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [128, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256; constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128; constexpr index_t GemmMPerBlock = 128;
...@@ -91,54 +57,26 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk( ...@@ -91,54 +57,26 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
constexpr index_t GemmMPerWave = 32; constexpr index_t GemmMPerWave = 32;
constexpr index_t GemmNPerWave = 32; constexpr index_t GemmNPerWave = 32;
constexpr index_t GemmK1 = 4; constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 2; constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2; constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 2, 4>; using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 2, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>; using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
// using vector load 4, so config's wo*ho must be a multiple of 4
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4; constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4; constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 4>; using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 4;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [256, 256, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256;
constexpr index_t GemmNPerBlock = 256;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerWave = 32;
constexpr index_t GemmNPerWave = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 4;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 4, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>; using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8; constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8; constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 4; constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 1 #elif 1
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16 // [M, N, K0, K1] = [128, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256; constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256; constexpr index_t GemmMPerBlock = 256;
...@@ -154,70 +92,73 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk( ...@@ -154,70 +92,73 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>; using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>; using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
// using vector load 4, so config's wo*ho must be a multiple of 4
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8; constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8; constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>; using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>; using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8; constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8; constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 4; constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#endif #endif
const auto descs = const auto descs = transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(wei_k_y_x_c_desc, wei_k_c_y_x_desc,
in_n_hi_wi_c_desc, in_n_c_hi_wi_desc,
out_n_ho_wo_k_desc, out_n_k_ho_wo_desc,
conv_strides, conv_strides,
conv_dilations, conv_dilations,
in_left_pads, in_left_pads,
in_right_pads, in_right_pads,
Number<GemmK1>{}); Number<GemmK1>{});
const auto wei_gemmk0_gemmm_gemmk1_grid_desc = descs[I0]; const auto out_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];
const auto in_gemmk0_gemmn_gemmk1_grid_desc = descs[I1]; const auto in_gemmk0_gemmn_gemmk1_grid_desc = descs[I1];
const auto out_gemmm_gemmn_grid_desc = descs[I2]; const auto wei_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix // HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple( constexpr auto out_gemmk0_gemmm_gemmk1_grid_step_hacks =
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}), make_tuple(make_tuple(Sequence<0, 0, 1, 0, 0>{}, // 0+: GemmK0
make_tuple( Sequence<0, 0, 0, 0, 0>{}, // 1+: GemmM
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})); Sequence<0, 0, 1, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 2, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0>{}, // 1-: GemmM
Sequence<0, 0, 2, 0, 0>{})); // 2-: GemmK1
constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks = constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, // 1+: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}), Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}, make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}, // 1-: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})); Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})); // 2-: GemmK1
constexpr auto out_m0_m1_m2_n_grid_step_hacks = constexpr auto wei_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{}, make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 1, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 1, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 1, 0, 0>{}), Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0>{}, make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 2, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 2, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 2, 0, 0>{})); Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks = constexpr auto out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{}; Sequence<0, 0, 1, 0, 0>{};
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks = constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{}; Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0>{};
for(index_t i = 0; i < 5; ++i) for(index_t i = 0; i < 5; ++i)
{ {
...@@ -227,14 +168,15 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk( ...@@ -227,14 +168,15 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
TAcc, TAcc,
TOut, TOut,
InMemoryDataOperationEnum_t::Set, InMemoryDataOperationEnum_t::Set,
decltype(wei_gemmk0_gemmm_gemmk1_grid_desc), decltype(out_gemmk0_gemmm_gemmk1_grid_desc),
decltype(in_gemmk0_gemmn_gemmk1_grid_desc), decltype(in_gemmk0_gemmn_gemmk1_grid_desc),
decltype(out_gemmm_gemmn_grid_desc), decltype(wei_gemmm_gemmn_grid_desc),
GemmMPerBlock, GemmMPerBlock,
GemmNPerBlock, GemmNPerBlock,
GemmKPerBlock, GemmKPerBlock,
GemmMPerWave, GemmMPerWave,
GemmNPerWave, GemmNPerWave,
GemmK1,
MRepeat, MRepeat,
NRepeat, NRepeat,
GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1, GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1,
...@@ -250,53 +192,37 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk( ...@@ -250,53 +192,37 @@ void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
Sequence<1, 0, 2>, Sequence<1, 0, 2>,
Sequence<1, 0, 2>, Sequence<1, 0, 2>,
2, 2,
GemmBBlockTransferSrcScalarPerVector_GemmK1, GemmBBlockTransferSrcScalarPerVector_GemmN,
GemmBBlockTransferDstScalarPerVector_GemmK1, GemmBBlockTransferDstScalarPerVector_GemmK1,
false, // don't move back src coordinate after threadwise copy false, // don't move back src coordinate after threadwise copy
Sequence<2, 3, 0, 1, 7, 5, 4, 6>, Sequence<3, 0, 1, 2, 7, 5, 4, 6>,
6, 7,
GemmCThreadTransferDstScalarPerVector, GemmCThreadTransferDstScalarPerVector,
decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks), decltype(out_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks), decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(out_m0_m1_m2_n_grid_step_hacks), decltype(wei_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks), decltype(out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks), decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat false>(static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
>(static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()), static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()), static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()), out_gemmk0_gemmm_gemmk1_grid_desc,
wei_gemmk0_gemmm_gemmk1_grid_desc, in_gemmk0_gemmn_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc, wei_gemmm_gemmn_grid_desc,
out_gemmm_gemmn_grid_desc, out_gemmk0_gemmm_gemmk1_grid_step_hacks,
wei_gemmk0_gemmm_gemmk1_grid_step_hacks, in_gemmk0_gemmn_gemmk1_grid_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_step_hacks, wei_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
out_m0_m1_m2_n_grid_step_hacks, out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks, in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks, nrepeat);
nrepeat);
float perf = static_cast<float>(calculate_convolution_flops(
{ in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc)) /
const auto N = out_n_ho_wo_k_lengths[I0]; (std::size_t(1000) * 1000 * 1000) / ave_time;
const auto K = out_n_ho_wo_k_lengths[I3];
const auto C = wei_k_y_x_c_lengths[I3]; std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
const auto Hi = in_n_hi_wi_c_lengths[I1];
const auto Wi = in_n_hi_wi_c_lengths[I2];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
} }
// copy result back to host // copy result back to host
out_n_ho_wo_k_device_buf.FromDevice(out_n_ho_wo_k.mData.data()); wei_k_c_y_x_device_buf.FromDevice(wei_k_c_y_x.mData.data());
} }
host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp
View file @ 627d8ef3
...@@ -47,7 +47,7 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw( ...@@ -47,7 +47,7 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths); const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths); const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 1 #if 0
// [M, N, K0, K1] = [128, 128, 4, 8] for fp16 // [M, N, K0, K1] = [128, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256; constexpr index_t BlockSize = 256;
...@@ -74,6 +74,34 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw( ...@@ -74,6 +74,34 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1; constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8; constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerWave = 32;
constexpr index_t GemmNPerWave = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1; constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#endif #endif
...@@ -92,36 +120,39 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw( ...@@ -92,36 +120,39 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
const auto out_gemmm_gemmn_grid_desc = descs[I2]; const auto out_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix // HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple( constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks =
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}), make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0+: GemmK0
make_tuple( Sequence<0, 0, 0, 0, 0>{}, // 1+: GemmM
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})); Sequence<0, 0, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0>{}, // 1-: GemmM
Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks = constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 1+: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}), Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}, make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 1-: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})); Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks = constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}), Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{})); Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks = constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{}; Sequence<0, 0, 0, 0, 0>{};
......
host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp
View file @ 627d8ef3
...@@ -250,22 +250,22 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk( ...@@ -250,22 +250,22 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1 Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks = constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: MRepeat make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: NRepeat Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: MWaves Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: NWaves Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M0 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M2 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: MRepeat make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: NRepeat Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: MWaves Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: NWaves Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M0 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M2 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N1 Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks = constexpr auto in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{}; Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
......
host/driver_offline/src/conv_bwd_driver_offline.cpp
View file @ 627d8ef3
...@@ -41,7 +41,7 @@ int main(int argc, char* argv[]) ...@@ -41,7 +41,7 @@ int main(int argc, char* argv[])
// dynamic mode // dynamic mode
if(argc != 22) if(argc != 22)
{ {
printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n"); printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n"); printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
exit(1); exit(1);
} }
...@@ -79,7 +79,7 @@ int main(int argc, char* argv[]) ...@@ -79,7 +79,7 @@ int main(int argc, char* argv[])
// static mode // static mode
if(argc < 7) if(argc < 7)
{ {
printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n"); printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
exit(1); exit(1);
} }
...@@ -90,28 +90,28 @@ int main(int argc, char* argv[]) ...@@ -90,28 +90,28 @@ int main(int argc, char* argv[])
const bool do_log = std::stoi(argv[5]); const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]); const int nrepeat = std::stoi(argv[6]);
constexpr index_t N = 128; constexpr auto N = Number<128>{};
constexpr index_t C = 192; constexpr auto C = Number<192>{};
constexpr index_t Hi = 71; constexpr auto Hi = Number<71>{};
constexpr index_t Wi = 71; constexpr auto Wi = Number<71>{};
constexpr index_t K = 256; constexpr auto K = Number<256>{};
constexpr index_t Y = 3; constexpr auto Y = Number<3>{};
constexpr index_t X = 3; constexpr auto X = Number<3>{};
const index_t conv_stride_h = 2; constexpr auto conv_stride_h = I2;
const index_t conv_stride_w = 2; constexpr auto conv_stride_w = I2;
const index_t conv_dilation_h = 1; constexpr auto conv_dilation_h = I1;
const index_t conv_dilation_w = 1; constexpr auto conv_dilation_w = I1;
const index_t in_left_pad_h = 1; constexpr auto in_left_pad_h = I1;
const index_t in_left_pad_w = 1; constexpr auto in_left_pad_w = I1;
const index_t in_right_pad_h = 1; constexpr auto in_right_pad_h = I1;
const index_t in_right_pad_w = 1; constexpr auto in_right_pad_w = I1;
const index_t YEff = (Y - 1) * conv_dilation_h + 1; constexpr auto YEff = (Y - I1) * conv_dilation_h + I1;
const index_t XEff = (X - 1) * conv_dilation_w + 1; constexpr auto XEff = (X - I1) * conv_dilation_w + I1;
const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1; constexpr auto Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + I1;
const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1; constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
#endif #endif
#if 0 #if 0
...@@ -119,9 +119,9 @@ int main(int argc, char* argv[]) ...@@ -119,9 +119,9 @@ int main(int argc, char* argv[])
using acc_data_t = float; using acc_data_t = float;
using out_data_t = float; using out_data_t = float;
#elif 1 #elif 1
using in_data_t = half_t; using in_data_t = half_t;
using acc_data_t = float; using acc_data_t = float;
using out_data_t = half_t; using out_data_t = half_t;
#endif #endif
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4); std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
......
host/driver_offline/src/conv_fwd_driver_offline.cpp
View file @ 627d8ef3
...@@ -19,7 +19,7 @@ ...@@ -19,7 +19,7 @@
#include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_MODE 1 #define USE_DYNAMIC_MODE 1
#define USE_CONV_FWD_V4R4_NCHW 0 #define USE_CONV_FWD_V4R4_NCHW 0
#define USE_CONV_FWD_V4R4R2_NHWC 0 #define USE_CONV_FWD_V4R4R2_NHWC 0
#define USE_CONV_FWD_V6R1_NCHW 0 #define USE_CONV_FWD_V6R1_NCHW 0
...@@ -49,11 +49,11 @@ int main(int argc, char* argv[]) ...@@ -49,11 +49,11 @@ int main(int argc, char* argv[])
constexpr auto I5 = Number<5>{}; constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{}; constexpr auto I6 = Number<6>{};
#if USE_MODE #if USE_DYNAMIC_MODE
// dynamic mode // dynamic mode
if(argc != 22) if(argc != 22)
{ {
printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n"); printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n"); printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
exit(1); exit(1);
} }
...@@ -91,7 +91,7 @@ int main(int argc, char* argv[]) ...@@ -91,7 +91,7 @@ int main(int argc, char* argv[])
// static mode // static mode
if(argc < 7) if(argc < 7)
{ {
printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n"); printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
exit(1); exit(1);
} }
...@@ -102,28 +102,28 @@ int main(int argc, char* argv[]) ...@@ -102,28 +102,28 @@ int main(int argc, char* argv[])
const bool do_log = std::stoi(argv[5]); const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]); const int nrepeat = std::stoi(argv[6]);
constexpr index_t N = 128; constexpr auto N = Number<128>{};
constexpr index_t C = 192; constexpr auto C = Number<192>{};
constexpr index_t Hi = 71; constexpr auto Hi = Number<71>{};
constexpr index_t Wi = 71; constexpr auto Wi = Number<71>{};
constexpr index_t K = 256; constexpr auto K = Number<256>{};
constexpr index_t Y = 3; constexpr auto Y = Number<3>{};
constexpr index_t X = 3; constexpr auto X = Number<3>{};
const index_t conv_stride_h = 2; constexpr auto conv_stride_h = I2;
const index_t conv_stride_w = 2; constexpr auto conv_stride_w = I2;
const index_t conv_dilation_h = 1; constexpr auto conv_dilation_h = I1;
const index_t conv_dilation_w = 1; constexpr auto conv_dilation_w = I1;
const index_t in_left_pad_h = 1; constexpr auto in_left_pad_h = I1;
const index_t in_left_pad_w = 1; constexpr auto in_left_pad_w = I1;
const index_t in_right_pad_h = 1; constexpr auto in_right_pad_h = I1;
const index_t in_right_pad_w = 1; constexpr auto in_right_pad_w = I1;
const index_t YEff = (Y - 1) * conv_dilation_h + 1; constexpr auto YEff = (Y - I1) * conv_dilation_h + I1;
const index_t XEff = (X - 1) * conv_dilation_w + 1; constexpr auto XEff = (X - I1) * conv_dilation_w + I1;
const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1; constexpr auto Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + I1;
const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1; constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
#endif #endif
#if 0 #if 0
...@@ -131,9 +131,9 @@ int main(int argc, char* argv[]) ...@@ -131,9 +131,9 @@ int main(int argc, char* argv[])
using acc_data_t = float; using acc_data_t = float;
using out_data_t = float; using out_data_t = float;
#elif 1 #elif 1
using in_data_t = half_t; using in_data_t = half_t;
using acc_data_t = float; using acc_data_t = float;
using out_data_t = half_t; using out_data_t = half_t;
#elif 1 #elif 1
using in_data_t = int8_t; using in_data_t = int8_t;
using acc_data_t = int32_t; using acc_data_t = int32_t;
...@@ -228,7 +228,6 @@ int main(int argc, char* argv[]) ...@@ -228,7 +228,6 @@ int main(int argc, char* argv[])
} }
auto f_make_for_device_nchw = [&]() { auto f_make_for_device_nchw = [&]() {
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi); const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X); const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo); const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
...@@ -236,19 +235,6 @@ int main(int argc, char* argv[]) ...@@ -236,19 +235,6 @@ int main(int argc, char* argv[])
const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w); const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w); const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w); const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w);
#else
const auto in_lengths_dev =
make_tuple(Number<N>{}, Number<C>{}, Number<Hi>{}, Number<Wi>{});
const auto wei_lengths_dev = make_tuple(Number<K>{}, Number<C>{}, Number<Y>{}, Number<X>{});
const auto out_lengths_dev =
make_tuple(Number<N>{}, Number<K>{}, Number<Ho>{}, Number<Wo>{});
const auto conv_strides_dev = make_tuple(Number<conv_stride_h>{}, Number<conv_stride_w>{});
const auto conv_dilations_dev =
make_tuple(Number<conv_dilation_h>{}, Number<conv_dilation_w>{});
const auto in_left_pads_dev = make_tuple(Number<in_left_pad_h>{}, Number<in_left_pad_w>{});
const auto in_right_pads_dev =
make_tuple(Number<in_right_pad_h>{}, Number<in_right_pad_w>{});
#endif
return make_tuple(in_lengths_dev, return make_tuple(in_lengths_dev,
wei_lengths_dev, wei_lengths_dev,
...@@ -260,7 +246,6 @@ int main(int argc, char* argv[]) ...@@ -260,7 +246,6 @@ int main(int argc, char* argv[])
}; };
auto f_make_for_device_nhwc = [&]() { auto f_make_for_device_nhwc = [&]() {
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C); const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C); const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K); const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
...@@ -268,19 +253,6 @@ int main(int argc, char* argv[]) ...@@ -268,19 +253,6 @@ int main(int argc, char* argv[])
const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w); const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w); const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w); const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w);
#else
const auto in_lengths_dev =
make_tuple(Number<N>{}, Number<Hi>{}, Number<Wi>{}, Number<C>{});
const auto wei_lengths_dev = make_tuple(Number<K>{}, Number<Y>{}, Number<X>{}, Number<C>{});
const auto out_lengths_dev =
make_tuple(Number<N>{}, Number<Ho>{}, Number<Wo>{}, Number<K>{});
const auto conv_strides_dev = make_tuple(Number<conv_stride_h>{}, Number<conv_stride_w>{});
const auto conv_dilations_dev =
make_tuple(Number<conv_dilation_h>{}, Number<conv_dilation_w>{});
const auto in_left_pads_dev = make_tuple(Number<in_left_pad_h>{}, Number<in_left_pad_w>{});
const auto in_right_pads_dev =
make_tuple(Number<in_right_pad_h>{}, Number<in_right_pad_w>{});
#endif
return make_tuple(in_lengths_dev, return make_tuple(in_lengths_dev,
wei_lengths_dev, wei_lengths_dev,
......
host/driver_offline/src/conv_wrw_driver_offline.cpp 0 → 100644
View file @ 627d8ef3
#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 "conv_common.hpp"
#include "host_conv_bwd_weight.hpp"
#include "device_tensor.hpp"
#include "device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#define USE_DYNAMIC_MODE 1
#define USE_CONV_WRW_V4R4R2_XDL_NCHW 1
enum ConvBackwardWeightAlgo
{
V4R4R2XDLNCHW,
};
int main(int argc, char* argv[])
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
#if USE_DYNAMIC_MODE
// dynamic mode
if(argc != 22)
{
printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvBackwardWeightAlgo algo = static_cast<ConvBackwardWeightAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
const index_t N = std::stoi(argv[7]);
const index_t K = std::stoi(argv[8]);
const index_t C = std::stoi(argv[9]);
const index_t Y = std::stoi(argv[10]);
const index_t X = std::stoi(argv[11]);
const index_t Hi = std::stoi(argv[12]);
const index_t Wi = std::stoi(argv[13]);
const index_t conv_stride_h = std::stoi(argv[14]);
const index_t conv_stride_w = std::stoi(argv[15]);
const index_t conv_dilation_h = std::stoi(argv[16]);
const index_t conv_dilation_w = std::stoi(argv[17]);
const index_t in_left_pad_h = std::stoi(argv[18]);
const index_t in_left_pad_w = std::stoi(argv[19]);
const index_t in_right_pad_h = std::stoi(argv[20]);
const index_t in_right_pad_w = std::stoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
#else
// static mode
if(argc < 7)
{
printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvBackwardWeightAlgo algo = static_cast<ConvBackwardWeightAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
constexpr auto N = Number<128>{};
constexpr auto C = Number<128>{};
constexpr auto Hi = Number<14>{};
constexpr auto Wi = Number<14>{};
constexpr auto K = Number<256>{};
constexpr auto Y = Number<3>{};
constexpr auto X = Number<3>{};
constexpr auto conv_stride_h = I1;
constexpr auto conv_stride_w = I1;
constexpr auto conv_dilation_h = I1;
constexpr auto conv_dilation_w = I1;
constexpr auto in_left_pad_h = I1;
constexpr auto in_left_pad_w = I1;
constexpr auto in_right_pad_h = I1;
constexpr auto in_right_pad_w = I1;
constexpr auto YEff = (Y - I1) * conv_dilation_h + I1;
constexpr auto XEff = (X - I1) * conv_dilation_w + I1;
constexpr auto Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + I1;
constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
#endif
#if 1
using in_data_t = float;
using acc_data_t = float;
using out_data_t = float;
#elif 1
using in_data_t = half_t;
using acc_data_t = float;
using out_data_t = half_t;
#elif 1
using in_data_t = int8_t;
using acc_data_t = int32_t;
using out_data_t = int8_t;
#endif
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
if(layout == ConvTensorLayout::NCHW)
{
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
in_lengths_host[3] = static_cast<std::size_t>(Wi);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(C);
wei_lengths_host[2] = static_cast<std::size_t>(Y);
wei_lengths_host[3] = static_cast<std::size_t>(X);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
}
else if(layout == ConvTensorLayout::NHWC)
{
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
in_lengths_host[3] = static_cast<std::size_t>(C);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(Y);
wei_lengths_host[2] = static_cast<std::size_t>(X);
wei_lengths_host[3] = static_cast<std::size_t>(C);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
}
else
{
std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in(in_lengths_host);
Tensor<in_data_t> wei_device(wei_lengths_host);
Tensor<out_data_t> wei_host(wei_lengths_host);
Tensor<out_data_t> out(out_lengths_host);
std::cout << "layout: " << layout << std::endl;
ostream_HostTensorDescriptor(in.mDesc, std::cout << "in: ");
ostream_HostTensorDescriptor(wei_host.mDesc, std::cout << "wei: ");
ostream_HostTensorDescriptor(out.mDesc, std::cout << "out: ");
print_array("InLeftPads", make_tuple(in_left_pad_h, in_left_pad_w));
print_array("InRightPads", make_tuple(in_right_pad_h, in_right_pad_w));
print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
std::size_t num_thread = std::thread::hardware_concurrency();
switch(init_method)
{
case 0:
// no initialization
break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
out.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
out.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 3:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
out.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 4:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
out.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 5:
in.GenerateTensorValue(GeneratorTensor_3<float>{-0.1, 0.1}, num_thread);
out.GenerateTensorValue(GeneratorTensor_3<float>{-0.1, 0.1}, num_thread);
break;
default:
in.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
auto gen_out = [](auto... is) {
return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
};
out.GenerateTensorValue(gen_out, num_thread);
}
auto f_make_for_device_nchw = [&]() {
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
const auto conv_strides_dev = make_tuple(conv_stride_h, conv_stride_w);
const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w);
return make_tuple(in_lengths_dev,
wei_lengths_dev,
out_lengths_dev,
conv_strides_dev,
conv_dilations_dev,
in_left_pads_dev,
in_right_pads_dev);
};
#if USE_CONV_WRW_V4R4R2_XDL_NCHW
if(algo == ConvBackwardWeightAlgo::V4R4R2XDLNCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei_device,
out,
nrepeat);
}
#endif
if(do_verification)
{
host_direct_convolution_backward_weights(out,
in,
wei_host,
make_tuple(conv_stride_h, conv_stride_w),
make_tuple(conv_dilation_h, conv_dilation_w),
make_tuple(in_left_pad_h, in_left_pad_w),
make_tuple(in_right_pad_h, in_right_pad_w),
layout);
check_error(wei_host, wei_device);
if(do_log)
{
LogRangeAsType<float>(std::cout << "out: ", out.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "wei_device: ", wei_device.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "wei_host : ", wei_host.mData, ",") << std::endl;
}
}
}
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