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Revert "[CK-Tile] Add the API to load SGPR (#2878)" (#2904)

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This reverts commit 2cbbf5dcb3.
This commit is contained in:
asleepzzz
2025-09-24 05:33:51 +08:00
committed by GitHub
parent 959df2a155
commit f161b5b738
40 changed files with 167 additions and 273 deletions
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18
include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_backward_data_kernel.hpp
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@@ -840,7 +840,7 @@ struct GroupedConvolutionBackwardDataKernel
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const index_t num_loop = amd_wave_read_first_lane(TilePartitioner::GetLoopNum(
const index_t num_loop = __builtin_amdgcn_readfirstlane(TilePartitioner::GetLoopNum(
gemm_pad_views.at(I0).get_tensor_descriptor().get_length(I1)));
// Run GEMM cooperatively by whole workgroup.
@@ -891,7 +891,7 @@ struct GroupedConvolutionBackwardDataKernel
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const index_t num_loop = amd_wave_read_first_lane(
const index_t num_loop = __builtin_amdgcn_readfirstlane(
TilePartitioner::GetLoopNum(gemm_tile_windows.at(I0).get_length(I1)));
// Run GEMM cooperatively by whole workgroup.
@@ -936,7 +936,7 @@ struct GroupedConvolutionBackwardDataKernel
CK_TILE_DEVICE void operator()(GroupedConvBwdDataKernelArgsSpecialized kargs) const
{
const auto blockIdX = amd_wave_read_first_lane(blockIdx.x);
const auto blockIdX = __builtin_amdgcn_readfirstlane(blockIdx.x);
const index_t group_id = FindGroupId(kargs, blockIdX);
const auto [iM, iN] = OffsettedTile1DPartitioner<TilePartitioner>::GetOffsetedTileIndex(
@@ -944,13 +944,13 @@ struct GroupedConvolutionBackwardDataKernel
kargs.c_grid_descs_m_n[group_id].get_length(I0),
kargs.c_grid_descs_m_n[group_id].get_length(I1));
const index_t i_m = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
const index_t i_n = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
const auto blockIdY = amd_wave_read_first_lane(blockIdx.y);
const auto group_offset_a = amd_wave_read_first_lane(kargs.group_stride_a * blockIdY);
const auto group_offset_b = amd_wave_read_first_lane(kargs.group_stride_b * blockIdY);
const auto group_offset_c = amd_wave_read_first_lane(kargs.group_stride_c * blockIdY);
const auto blockIdY = __builtin_amdgcn_readfirstlane(blockIdx.y);
const auto group_offset_a = __builtin_amdgcn_readfirstlane(kargs.group_stride_a * blockIdY);
const auto group_offset_b = __builtin_amdgcn_readfirstlane(kargs.group_stride_b * blockIdY);
const auto group_offset_c = __builtin_amdgcn_readfirstlane(kargs.group_stride_c * blockIdY);
// options
// conv_bwd_data = Out * Weight = In

36
include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_backward_weight_kernel.hpp
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@@ -423,20 +423,22 @@ struct GroupedConvolutionBackwardWeightKernel
__device__ SplitKBatchOffset(const GroupedConvBwdWeightKernelArgsSpecialized& kargs,
const std::size_t k_id = blockIdx.z)
{
constexpr auto K1 = TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{});
const index_t K_t = amd_wave_read_first_lane(kargs.k_batch * K1);
const index_t KRead = amd_wave_read_first_lane((kargs.GemmK + K_t - 1) / K_t * K1);
constexpr auto K1 = TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{});
const index_t K_t = __builtin_amdgcn_readfirstlane(kargs.k_batch * K1);
const index_t KRead =
__builtin_amdgcn_readfirstlane((kargs.GemmK + K_t - 1) / K_t * K1);
a_k_split_offset = amd_wave_read_first_lane(k_id * KRead);
b_k_split_offset = amd_wave_read_first_lane(k_id * KRead);
a_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead);
b_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead);
if(k_id < static_cast<uint32_t>(kargs.k_batch - 1))
{
splitted_k = amd_wave_read_first_lane(KRead);
splitted_k = __builtin_amdgcn_readfirstlane(KRead);
}
else
{
splitted_k = amd_wave_read_first_lane(kargs.GemmK - KRead * (kargs.k_batch - 1));
splitted_k =
__builtin_amdgcn_readfirstlane(kargs.GemmK - KRead * (kargs.k_batch - 1));
}
}
@@ -803,22 +805,22 @@ struct GroupedConvolutionBackwardWeightKernel
CK_TILE_DEVICE void operator()(GroupedConvBwdWeightKernelArgsSpecialized kargs) const
{
const auto blockIdX = amd_wave_read_first_lane(blockIdx.x);
const auto blockIdX = __builtin_amdgcn_readfirstlane(blockIdx.x);
const auto [iM, iN] =
TilePartitioner{kargs.GemmM, kargs.GemmN}.GetOutputTileIndex(blockIdX);
const index_t i_m = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
const index_t i_n = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
const auto blockIdZ = amd_wave_read_first_lane(blockIdx.z);
const index_t num_loop = amd_wave_read_first_lane(
const auto blockIdZ = __builtin_amdgcn_readfirstlane(blockIdx.z);
const index_t num_loop = __builtin_amdgcn_readfirstlane(
ck_tile::integer_divide_ceil(kargs.GemmK, kargs.k_batch * TilePartitioner::KPerBlock));
const index_t i_k =
amd_wave_read_first_lane(blockIdZ * num_loop * TilePartitioner::KPerBlock);
__builtin_amdgcn_readfirstlane(blockIdZ * num_loop * TilePartitioner::KPerBlock);
const auto blockIdY = amd_wave_read_first_lane(blockIdx.y);
const auto group_offset_a = amd_wave_read_first_lane(kargs.group_stride_a * blockIdY);
const auto group_offset_b = amd_wave_read_first_lane(kargs.group_stride_b * blockIdY);
const auto group_offset_c = amd_wave_read_first_lane(kargs.group_stride_c * blockIdY);
const auto blockIdY = __builtin_amdgcn_readfirstlane(blockIdx.y);
const auto group_offset_a = __builtin_amdgcn_readfirstlane(kargs.group_stride_a * blockIdY);
const auto group_offset_b = __builtin_amdgcn_readfirstlane(kargs.group_stride_b * blockIdY);
const auto group_offset_c = __builtin_amdgcn_readfirstlane(kargs.group_stride_c * blockIdY);
// options
// conv_bwd_weight = Out * In = Weight

24
include/ck_tile/ops/grouped_convolution/kernel/grouped_convolution_forward_kernel.hpp
View File

@@ -752,7 +752,8 @@ struct GroupedConvolutionForwardKernel
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const index_t num_loop = amd_wave_read_first_lane(TilePartitioner::GetLoopNum(kargs.GemmK));
const index_t num_loop =
__builtin_amdgcn_readfirstlane(TilePartitioner::GetLoopNum(kargs.GemmK));
// Run GEMM cooperatively by whole workgroup.
const auto& a_block_window = gemm_tile_windows.at(I0);
@@ -801,7 +802,8 @@ struct GroupedConvolutionForwardKernel
const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
auto gemm_tile_windows = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
const index_t num_loop = amd_wave_read_first_lane(TilePartitioner::GetLoopNum(kargs.GemmK));
const index_t num_loop =
__builtin_amdgcn_readfirstlane(TilePartitioner::GetLoopNum(kargs.GemmK));
// Run GEMM cooperatively by whole workgroup.
const auto& a_block_window = gemm_tile_windows.at(I0);
@@ -820,22 +822,22 @@ struct GroupedConvolutionForwardKernel
CK_TILE_DEVICE void operator()(GroupedConvFwdKernelArgsSpecialized kargs) const
{
const auto blockIdX = amd_wave_read_first_lane(blockIdx.x);
const auto blockIdX = __builtin_amdgcn_readfirstlane(blockIdx.x);
const auto [iM, iN] =
TilePartitioner{kargs.GemmM, kargs.GemmN}.GetOutputTileIndex(blockIdX);
const index_t i_m = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
const index_t i_n = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
const index_t i_m = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
const index_t i_n = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
const auto blockIdY = amd_wave_read_first_lane(blockIdx.y);
const auto group_offset_a = amd_wave_read_first_lane(kargs.group_stride_a * blockIdY);
const auto group_offset_b = amd_wave_read_first_lane(kargs.group_stride_b * blockIdY);
const auto group_offset_c = amd_wave_read_first_lane(kargs.group_stride_c * blockIdY);
const auto blockIdY = __builtin_amdgcn_readfirstlane(blockIdx.y);
const auto group_offset_a = __builtin_amdgcn_readfirstlane(kargs.group_stride_a * blockIdY);
const auto group_offset_b = __builtin_amdgcn_readfirstlane(kargs.group_stride_b * blockIdY);
const auto group_offset_c = __builtin_amdgcn_readfirstlane(kargs.group_stride_c * blockIdY);
// Split-N handling: Get which split this workgroup handles
const auto blockIdZ = amd_wave_read_first_lane(blockIdx.z);
const auto blockIdZ = __builtin_amdgcn_readfirstlane(blockIdx.z);
// Calculate batch offset for this split
const index_t batch_offset = amd_wave_read_first_lane(blockIdZ * kargs.n_per_split);
const index_t batch_offset = __builtin_amdgcn_readfirstlane(blockIdZ * kargs.n_per_split);
// Calculate memory offsets for this split
const long_index_t input_batch_offset = static_cast<long_index_t>(batch_offset) *