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Unverified Commit f18cfec4 authored by Haocong WANG's avatar Haocong WANG Committed by GitHub
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Merge branch 'develop' into update_cka8w8_uc

parents 4599ee00 0e5e29c4
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Showing with 967 additions and 106 deletions
include/ck_tile/host/reference/reference_moe_sorting.hpp
View file @ f18cfec4
......@@ -14,12 +14,15 @@ namespace ck_tile {
template <typename WeightType, typename IndexType = index_t>
CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
const HostTensor<WeightType>& weights,
const HostTensor<IndexType>& local_expert_mask,
HostTensor<IndexType>& p_sorted_token_ids,
HostTensor<WeightType>& sorted_weight,
HostTensor<IndexType>& sorted_expert_ids,
index_t& unit_cnt,
const index_t experts,
const index_t unit_size)
const index_t unit_size,
bool local_expert_masking,
bool skip_experts_with_zero_token = true)
{
const index_t num_token = topk_ids.mDesc.get_lengths()[0];
const index_t topk = topk_ids.mDesc.get_lengths()[1];
......@@ -33,8 +36,11 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
#endif
std::vector<std::vector<WeightType>> expert_token_weights(
experts, std::vector<WeightType>(unit_size, 0));
// count number of unit-size slices in this expert
std::vector<IndexType> expert_slices(experts, 1);
// count the tokens used in this expert
std::vector<IndexType> expert_slice_idxs(experts, 0);
// TODO: above 2 buffer seems duplicated
for(index_t t = 0; t < num_token; t++)
{
......@@ -72,8 +78,23 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
IndexType* out_tokens = p_sorted_token_ids.data();
WeightType* out_weights = sorted_weight.data();
IndexType* out_expert_id = sorted_expert_ids.data();
int curr_expert_id = 0;
for(index_t e = 0; e < experts; e++)
{
if(local_expert_masking)
{
if(local_expert_mask(e) == 0)
continue;
}
if(skip_experts_with_zero_token)
{
if(expert_slice_idxs[e] == 0)
{
curr_expert_id++;
continue;
}
}
memcpy(out_tokens, expert_tokens[e].data(), sizeof(index_t) * expert_slices[e] * unit_size);
out_tokens += expert_slices[e] * unit_size;
memcpy(out_weights,
......@@ -83,10 +104,11 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
for(index_t s = 0; s < expert_slices[e]; s++)
{
out_expert_id[s] = e;
out_expert_id[s] = curr_expert_id;
unit_cnt++;
}
out_expert_id += expert_slices[e];
curr_expert_id++;
}
unit_cnt *= unit_size;
return;
......
include/ck_tile/ops/add_rmsnorm2d_rdquant.hpp
View file @ f18cfec4
......@@ -10,3 +10,4 @@
#include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_three_pass.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/batched_transpose.hpp
View file @ f18cfec4
......@@ -9,3 +9,4 @@
#include "ck_tile/ops/batched_transpose/pipeline/batched_transpose_problem.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/common.hpp
View file @ f18cfec4
......@@ -5,3 +5,4 @@
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/common/utils.hpp 0 → 100644
View file @ f18cfec4
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <string>
#include "ck_tile/core.hpp"
namespace ck_tile {
// clang-format off
template <typename T> struct typeToStr;
template <> struct typeToStr<float> { static constexpr const char * name = "fp32"; };
template <> struct typeToStr<fp16_t> { static constexpr const char * name = "fp16"; };
template <> struct typeToStr<bf16_t> { static constexpr const char * name = "bf16"; };
template <> struct typeToStr<fp8_t> { static constexpr const char * name = "fp8"; };
template <> struct typeToStr<bf8_t> { static constexpr const char * name = "bf8"; };
template <> struct typeToStr<int8_t> { static constexpr const char * name = "int8"; };
// clang-format on
template <typename ADataType_, typename BDataType_>
std::string gemm_prec_str()
{
std::string base_str = std::string(typeToStr<ADataType_>::name);
if(!std::is_same_v<ADataType_, BDataType_>)
{
base_str += "_" + std::string(typeToStr<BDataType_>::name);
}
return base_str;
}
} // namespace ck_tile
include/ck_tile/ops/elementwise.hpp
View file @ f18cfec4
......@@ -6,3 +6,4 @@
#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/epilogue.hpp
View file @ f18cfec4
......@@ -8,3 +8,4 @@
#include "ck_tile/ops/epilogue/dynamic_quant_epilogue.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/flatmm.hpp
View file @ f18cfec4
......@@ -9,3 +9,4 @@
#include "ck_tile/ops/flatmm/block/flatmm_uk_config.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/fmha.hpp
View file @ f18cfec4
......@@ -44,3 +44,4 @@
#include "ck_tile/ops/fmha/pipeline/tile_fmha_traits.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/fused_moe.hpp
View file @ f18cfec4
......@@ -7,6 +7,7 @@
#include "ck_tile/ops/fused_moe/kernel/fused_moegemm_shape.hpp"
#include "ck_tile/ops/fused_moe/kernel/fused_moegemm_tile_partitioner.hpp"
#include "ck_tile/ops/fused_moe/kernel/moe_sorting_kernel.hpp"
#include "ck_tile/ops/fused_moe/kernel/moe_sorting_problem.hpp"
#include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_ex.hpp"
#include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_policy.hpp"
#include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_uk.hpp"
......@@ -14,6 +15,6 @@
#include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_traits.hpp"
#include "ck_tile/ops/fused_moe/pipeline/moe_sorting_pipeline.hpp"
#include "ck_tile/ops/fused_moe/pipeline/moe_sorting_policy.hpp"
#include "ck_tile/ops/fused_moe/pipeline/moe_sorting_problem.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/fused_moe/kernel/fused_moegemm_kernel.hpp
View file @ f18cfec4
......@@ -22,7 +22,7 @@
// (only for reference) exp-0 exp-1 exp-2 exp-3 exp-4 exp-5
// weight_id_per_expert is: [[a], [g, j, m], [d, k], [b, e, h, l, n], [], [c, f, i, o]]
//
// max_num_tokens_padded : topk * input_tokens + num_experts * (M_a - 1)
// max_num_tokens_padded : topk * input_tokens + num_experts * M_a - topk (updated)
// * this could be larger than actual, since actual tokens are on GPU
//
// sorted_token_ids_ptr : [0, 6, 6, 6, 2, 3, 4, 6, 1, 3, 6, 6, 0, 1, 2, 3, 4, 6, 6, 6, 6, 6, 6, 6, 0, 1, 2, 5]
......
include/ck_tile/ops/fused_moe/pipeline/moe_sorting_problem.hpp → include/ck_tile/ops/fused_moe/kernel/moe_sorting_problem.hpp
View file @ f18cfec4
......@@ -25,4 +25,28 @@ struct MoeSortingProblem
InternalLoadUnroll_; // TODO: need better design(like tile size)
static constexpr index_t ExpertTile = ExpertTile_; // TODO: only used in store out
};
template <typename IndexType_,
typename WeightType_,
index_t SubTokenTile_, // 1,2,4,8, or 0 in the future
bool SubTokenOneShot_, // if we only loop over once or not
bool LocalExpertMasking_, // used in EP case
bool SkipExpertsWithZeroTokens_ = true,
index_t ExpertTile_ = 0>
struct MoeSortingProblemEx
{
// TODO: this kernel only support warp per row
using WeightType = remove_cvref_t<WeightType_>;
using IndexType = remove_cvref_t<IndexType_>;
static constexpr index_t WarpSize = get_warp_size();
static constexpr index_t WarpsPerBlock = 1;
static constexpr index_t SubTokenTile = SubTokenTile_;
static constexpr bool SubTokenOneShot = SubTokenOneShot_;
static constexpr bool LocalExpertMasking = LocalExpertMasking_;
static constexpr bool SkipExpertsWithZeroTokens = SkipExpertsWithZeroTokens_;
static_assert(SubTokenTile == 1 || SubTokenTile == 2 || SubTokenTile == 4 || SubTokenTile == 8);
static constexpr index_t ExpertTile = ExpertTile_; // TODO: only used in store out
};
} // namespace ck_tile
include/ck_tile/ops/gemm.hpp
View file @ f18cfec4
......@@ -29,6 +29,8 @@
#include "ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v3.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4_default_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_mem.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1.hpp"
......@@ -46,3 +48,4 @@
#include "ck_tile/ops/gemm/warp/warp_gemm_impl.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1.hpp
View file @ f18cfec4
......@@ -14,24 +14,54 @@ namespace ck_tile {
template <typename Problem_, typename Policy_ = BlockGemmARegBRegCRegV1DefaultPolicy>
struct BlockGemmARegBRegCRegV1
{
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using CDataType = remove_cvref_t<typename Problem::CDataType>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr auto config = Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WG = remove_cvref_t<decltype(config.template at<0>())>;
static constexpr index_t MWarp = config.template at<1>();
static constexpr index_t NWarp = config.template at<2>();
static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WG::kM);
static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WG::kN);
static constexpr index_t KIterPerWarp = KPerBlock / WG::kK;
private:
template <typename PipelineProblem_, typename GemmPolicy_>
struct GemmTraits_
{
using Problem = remove_cvref_t<PipelineProblem_>;
using Policy = remove_cvref_t<GemmPolicy_>;
using ADataType = remove_cvref_t<typename Problem::ADataType>;
using BDataType = remove_cvref_t<typename Problem::BDataType>;
using CDataType = remove_cvref_t<typename Problem::CDataType>;
using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
static constexpr index_t kBlockSize = Problem::kBlockSize;
static constexpr index_t MPerBlock = BlockGemmShape::kM;
static constexpr index_t NPerBlock = BlockGemmShape::kN;
static constexpr index_t KPerBlock = BlockGemmShape::kK;
static constexpr auto config = Policy::template GetWarpGemmMWarpNWarp<Problem>();
using WarpGemm = remove_cvref_t<decltype(config.template at<0>())>;
static constexpr index_t MWarp = config.template at<1>();
static constexpr index_t NWarp = config.template at<2>();
static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WarpGemm::kM);
static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WarpGemm::kN);
static constexpr index_t KIterPerWarp = KPerBlock / WarpGemm::kK;
static constexpr index_t KPack = WarpGemm::kKPerThread;
};
public:
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using Traits = GemmTraits_<Problem, Policy>;
using WarpGemm = typename Traits::WarpGemm;
using BlockGemmShape = typename Traits::BlockGemmShape;
using ADataType = remove_cvref_t<typename Traits::ADataType>;
using BDataType = remove_cvref_t<typename Traits::BDataType>;
using CDataType = remove_cvref_t<typename Traits::CDataType>;
static constexpr index_t KIterPerWarp = Traits::KIterPerWarp;
static constexpr index_t MIterPerWarp = Traits::MIterPerWarp;
static constexpr index_t NIterPerWarp = Traits::NIterPerWarp;
static constexpr index_t MWarp = Traits::MWarp;
static constexpr index_t NWarp = Traits::NWarp;
CK_TILE_DEVICE static constexpr auto MakeABlockDistributionEncode()
{
......@@ -43,7 +73,7 @@ struct BlockGemmARegBRegCRegV1
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto a_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
a_block_outer_dstr_encoding, typename WG::AWarpDstrEncoding{});
a_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
return a_block_dstr_encode;
}
......@@ -58,7 +88,7 @@ struct BlockGemmARegBRegCRegV1
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto b_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
b_block_outer_dstr_encoding, typename WG::BWarpDstrEncoding{});
b_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
return b_block_dstr_encode;
}
......@@ -73,7 +103,7 @@ struct BlockGemmARegBRegCRegV1
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
c_block_outer_dstr_encoding, typename WG::CWarpDstrEncoding{});
c_block_outer_dstr_encoding, typename WarpGemm::CWarpDstrEncoding{});
return c_block_dstr_encode;
}
......@@ -112,13 +142,13 @@ struct BlockGemmARegBRegCRegV1
.get_static_tile_distribution_encoding())>>,
"C distribution is wrong!");
using AWarpDstr = typename WG::AWarpDstr;
using BWarpDstr = typename WG::BWarpDstr;
using CWarpDstr = typename WG::CWarpDstr;
using AWarpDstr = typename WarpGemm::AWarpDstr;
using BWarpDstr = typename WarpGemm::BWarpDstr;
using CWarpDstr = typename WarpGemm::CWarpDstr;
using AWarpTensor = typename WG::AWarpTensor;
using BWarpTensor = typename WG::BWarpTensor;
using CWarpTensor = typename WG::CWarpTensor;
using AWarpTensor = typename WarpGemm::AWarpTensor;
using BWarpTensor = typename WarpGemm::BWarpTensor;
using CWarpTensor = typename WarpGemm::CWarpTensor;
constexpr auto a_warp_y_lengths =
to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
......@@ -157,7 +187,7 @@ struct BlockGemmARegBRegCRegV1
merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
// warp GEMM
WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
// write C warp tensor into C block tensor
c_block_tensor.set_y_sliced_thread_data(
......@@ -180,7 +210,7 @@ struct BlockGemmARegBRegCRegV1
sequence<0, 0>>{};
constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
c_block_outer_dstr_encoding, typename WG::CWarpDstrEncoding{});
c_block_outer_dstr_encoding, typename WarpGemm::CWarpDstrEncoding{});
constexpr auto c_block_dstr = make_static_tile_distribution(c_block_dstr_encode);
auto c_block_tensor = make_static_distributed_tensor<CDataType>(c_block_dstr);
return c_block_tensor;
......
include/ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp
View file @ f18cfec4
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -57,6 +59,18 @@ struct BatchedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
using BLayout = typename Base::BLayout;
using CLayout = typename Base::CLayout;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
using P_ = GemmPipeline;
return concat('_', "gemm_batched", gemm_prec_str<ADataType, BDataType>,
concat('x', P_::kMPerBlock, P_::kNPerBlock, P_::kKPerBlock),
concat('x', P_::GetVectorSizeA(), P_::GetVectorSizeB(), P_::GetVectorSizeC()),
concat('x', P_::kPadM, P_::kPadN, P_::kPadK));
// clang-format on
}
struct BatchedGemmKernelArgs : GemmKernelArgs
{
index_t batch_stride_A;
......
include/ck_tile/ops/gemm/kernel/gemm_kernel.hpp
View file @ f18cfec4
......@@ -8,6 +8,7 @@
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -75,6 +76,13 @@ struct GemmKernel
static constexpr auto I1 = number<1>();
static constexpr auto I2 = number<2>();
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "gemm", gemm_prec_str<ADataType, BDataType>, GemmPipeline::GetName());
// clang-format on
}
CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
{
return dim3(TilePartitioner::GridSize(M, N), 1, KBatch);
......@@ -455,7 +463,9 @@ struct GemmKernel
* @param a_ptr input A pointer
* @param b_ptr input B pointer
* @param c_ptr output C pointer
* @param smem_ptr_0 The start memory pointer of the shared memory block.
* @param kargs GEMM kernel arguments
* @param splitk_batch_offset splitk_batch_offset Utility structure used to calculate k batch.
* @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
* @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
*
......@@ -465,7 +475,7 @@ struct GemmKernel
CK_TILE_DEVICE static void RunGemm(const ADataType* a_ptr,
const BDataType* b_ptr,
CDataType* c_ptr,
void* smem_ptr,
void* smem_ptr_0,
const GemmKernelArgs& kargs,
const SplitKBatchOffset& splitk_batch_offset,
const index_t block_idx_m,
......@@ -483,15 +493,67 @@ struct GemmKernel
// Run GEMM cooperatively by whole workgroup.
const auto& a_block_window = gemm_tile_windows.at(I0);
const auto& b_block_window = gemm_tile_windows.at(I1);
const auto& c_block_tile =
GemmPipeline{}.template operator()(a_block_window, b_block_window, num_loop, smem_ptr);
const auto& c_block_tile = GemmPipeline{}.template operator()(
a_block_window, b_block_window, num_loop, smem_ptr_0);
// Run Epilogue Pipeline
auto& c_block_window = gemm_tile_windows.at(I2);
EpiloguePipeline{}
.template operator()<decltype(c_block_window), decltype(c_block_tile), DstInMemOp>(
c_block_window, c_block_tile, smem_ptr);
c_block_window, c_block_tile, smem_ptr_0);
}
/**
* @brief Runs single GEMM problem cooperatively by whole workgroup.
*
* @note RunGEMM2LDS in with two shared memory buffers using the ping pong buffer mechanism.
*
* @param a_ptr input A pointer
* @param b_ptr input B pointer
* @param c_ptr output C pointer
* @param smem_ptr_0 The starting pointer of 1st shared memory block.
* @param smem_ptr_1 The starting pointer of 2nd shared memory block.
* @param kargs GEMM kernel arguments
* @param splitk_batch_offset Utility structure used to calculate k batch.
* @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
* @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
*
* @tparam DstInMemOp Destination memory operation (default: set).
*/
template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
CK_TILE_DEVICE static void RunGemm2LDS(const ADataType* a_ptr,
const BDataType* b_ptr,
CDataType* c_ptr,
void* __restrict__ smem_ptr_0,
void* __restrict__ smem_ptr_1,
const GemmKernelArgs& kargs,
const SplitKBatchOffset& splitk_batch_offset,
const index_t block_idx_m,
const index_t block_idx_n)
{
// Create Gemm tensor views, pad views and tile windows
const auto& gemm_tensor_views_tuple =
MakeGemmTensorViews<DstInMemOp>(a_ptr, b_ptr, c_ptr, kargs, splitk_batch_offset);
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 = TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k);
// Run GEMM cooperatively by whole workgroup.
const auto& a_block_window = gemm_tile_windows.at(I0);
const auto& b_block_window = gemm_tile_windows.at(I1);
const auto& c_block_tile = GemmPipeline{}.template operator()(
a_block_window, b_block_window, num_loop, smem_ptr_0, smem_ptr_1);
// Run Epilogue Pipeline
auto& c_block_window = gemm_tile_windows.at(I2);
EpiloguePipeline{}
.template operator()<decltype(c_block_window), decltype(c_block_tile), DstInMemOp>(
c_block_window, c_block_tile, smem_ptr_0);
}
CK_TILE_DEVICE void operator()(GemmKernelArgs kargs) const
......@@ -509,11 +571,27 @@ struct GemmKernel
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
// allocate LDS
__shared__ char smem_ptr[GetSmemSize()];
__shared__ char smem_ptr_0[GetSmemSize()];
__shared__ char smem_ptr_1[GetSmemSize()];
if(kargs.k_batch == 1)
{
RunGemm(a_ptr, b_ptr, c_ptr, smem_ptr, kargs, splitk_batch_offset, i_m, i_n);
if constexpr(GemmPipeline::DoubleSmemBuffer == true)
{
RunGemm2LDS(a_ptr,
b_ptr,
c_ptr,
smem_ptr_0,
smem_ptr_1,
kargs,
splitk_batch_offset,
i_m,
i_n);
}
else
{
RunGemm(a_ptr, b_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
}
}
else
{
......@@ -522,8 +600,23 @@ struct GemmKernel
if constexpr(!(EpiloguePipeline::template GetVectorSizeC<CDataType>() % 2 != 0 &&
is_any_of<CDataType, fp16_t, bf16_t>::value))
{
RunGemm<memory_operation_enum::atomic_add>(
a_ptr, b_ptr, c_ptr, smem_ptr, kargs, splitk_batch_offset, i_m, i_n);
if constexpr(GemmPipeline::DoubleSmemBuffer == true)
{
RunGemm2LDS<memory_operation_enum::atomic_add>(a_ptr,
b_ptr,
c_ptr,
smem_ptr_0,
smem_ptr_1,
kargs,
splitk_batch_offset,
i_m,
i_n);
}
else
{
RunGemm<memory_operation_enum::atomic_add>(
a_ptr, b_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
}
}
}
}
......
include/ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp
View file @ f18cfec4
......@@ -64,6 +64,18 @@ struct GroupedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
}
};
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
using P_ = GemmPipeline;
return concat('_', "gemm_grouped", gemm_prec_str<ADataType, BDataType>,
concat('x', P_::kMPerBlock, P_::kNPerBlock, P_::kKPerBlock),
concat('x', P_::GetVectorSizeA(), P_::GetVectorSizeB(), P_::GetVectorSizeC()),
concat('x', P_::kPadM, P_::kPadN, P_::kPadK));
// clang-format on
}
__host__ static auto GetWorkSpaceSize(const std::vector<GroupedGemmHostArgs>& gemm_descs)
-> std::size_t
{
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp
View file @ f18cfec4
......@@ -41,20 +41,26 @@ struct GemmPipelineAgBgCrImplBase
store_tile(lds_tile_window, block_tile_tmp);
}
template <typename DstBlockTile, typename SrcTileWindow>
CK_TILE_DEVICE void LocalPrefetch(DstBlockTile& dst_block_tile,
const SrcTileWindow& lds_tile_window) const
{
load_tile(dst_block_tile, lds_tile_window);
}
CK_TILE_DEVICE auto GetABLdsTensorViews(void* p_smem) const
{
// A tile in LDS
ADataType* p_a_lds = static_cast<ADataType*>(p_smem);
ADataType* __restrict__ p_a_lds = static_cast<ADataType*>(p_smem);
constexpr auto a_lds_block_desc = Policy::template MakeALdsBlockDescriptor<Problem>();
auto a_lds_block = make_tensor_view<address_space_enum::lds>(p_a_lds, a_lds_block_desc);
// TODO: LDS alignment should come from Policy!
constexpr index_t a_lds_block_space_size_aligned =
integer_divide_ceil(sizeof(ADataType) * a_lds_block_desc.get_element_space_size(), 16) *
16;
constexpr index_t a_lds_block_space_size_aligned = integer_least_multiple(
sizeof(ADataType) * a_lds_block_desc.get_element_space_size(), 16);
// B tile in LDS
BDataType* p_b_lds = static_cast<BDataType*>(
BDataType* __restrict__ p_b_lds = static_cast<BDataType*>(
static_cast<void*>(static_cast<char*>(p_smem) + a_lds_block_space_size_aligned));
constexpr auto b_lds_block_desc = Policy::template MakeBLdsBlockDescriptor<Problem>();
auto b_lds_block = make_tensor_view<address_space_enum::lds>(p_b_lds, b_lds_block_desc);
......
include/ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v3.hpp
View file @ f18cfec4
......@@ -10,6 +10,7 @@
#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/host/concat.hpp"
namespace ck_tile {
......@@ -75,12 +76,23 @@ struct GemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
static constexpr bool kPadN = Problem::kPadN;
static constexpr bool kPadK = Problem::kPadK;
static constexpr bool DoubleSmemBuffer = Problem::DoubleSmemBuffer;
static constexpr bool HasHotLoop = Problem::HasHotLoop;
static constexpr auto TailNum = Problem::TailNum;
static constexpr auto Scheduler = Problem::Scheduler;
using Base::PrefetchStages;
[[nodiscard]] CK_TILE_HOST static const std::string GetName()
{
// clang-format off
return concat('_', "pipeline_AgBgCrCompV3", BlockSize,
concat('x', GetVectorSizeA(), GetVectorSizeB(), GetVectorSizeC()),
concat('x', kPadM, kPadN, kPadK));
// clang-format on
}
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return Policy::template GetSmemSize<Problem>();
......
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