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test(grouped_gemm): add unit tests for grouped_gemm bquant with preshuffleB true (#3119)
* add tensorwise quant in grouped gemm * fix example issue * update test cases * format codes * clang format * use GTEST_FAIL * add bquant to grouped_gemm * add tensorwise quant in grouped gemm * fix example issue * update test cases * format codes * clang format * use GTEST_FAIL * fix a bug in test_grouped_gemm_util * skip test when use wmma on grouped_quant kernel * change cmake * fix a bug in test_grouped_gemm_util * skip test when use wmma on grouped_quant kernel * change cmake * tests(quant_grouped_gemm): add unit tests to cover bquant in grouped_gemm * Update test/ck_tile/grouped_gemm_quant/test_grouped_gemm_util_quant.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update example/ck_tile/17_grouped_gemm/quant_grouped_gemm.hpp Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * feat: add bf8 support * chore: remove unnecessary decltype usage * chore: add default quant_mode to function signature as fallback * fix: pass correct runtime pipeline params in grouped_gemm bquant kernel Calculate has_hot_loop, num_loop, and tail_number on device side for each GEMM problem instead of using default values. This fixes incorrect results when different problems in the group have different K dimensions. * chore: set default quant mode in function signature * test: add additional test cases to cover edge case of no hotloop * change code based on comments * WIP: bquant preshuffle b compiles but gives numerical error * feat(grouped_gemm_quant): bquant with preshuffleB support added to grouped_gemm example & kernel * refactor: refactor code after merge commit * chore: remove print statements * test(grouped_gemm): split test cases by quant mode to reduce compilation time and add bquant-preshuffleB mode test cases --------- Co-authored-by: kyle-256 <Kyle.Zhao@amd.com> Co-authored-by: ThomasNing <thomas.ning@amd.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Aviral Goel
@@ -483,6 +483,7 @@ struct QuantGemmKernel
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const QuantGemmKernelArgs& kargs,
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const SplitKBatchOffset& splitk_batch_offset)
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{
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static_assert(!TilePartitioner::BlockGemmShape::PermuteA, "Not implemented!");
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const auto& a_tensor_view = [&]() {
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if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
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@@ -790,6 +791,7 @@ struct QuantGemmKernel
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}();
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if constexpr(PreshuffleB)
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{
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return make_tuple(a_pad_view, aq_pad_view, b_flat_view, bq_pad_view, c_pad_view);
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}
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else
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@@ -802,6 +804,7 @@ struct QuantGemmKernel
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CK_TILE_DEVICE static auto
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MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n)
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{
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const auto& a_pad_view = views.at(I0);
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const auto& aq_pad_view = views.at(I1);
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const auto& b_pad_view = views.at(I2);
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@@ -867,6 +870,7 @@ struct QuantGemmKernel
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const auto& b_block_window = [&]() {
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if constexpr(PreshuffleB)
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{
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return make_tile_window(
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b_pad_view,
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make_tuple(number<GemmPipeline::flatNPerWarp>{},
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@@ -317,13 +317,88 @@ struct QuantGroupedGemmKernel
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const BQDataType* bq_ptr = static_cast<const BQDataType*>(kargs.bq_ptr);
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CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
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static_assert(GemmPipeline::DoubleSmemBuffer == false,
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"DoubleSmemBuffer needs to be false");
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// allocate LDS
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__shared__ char smem_ptr_0[GetSmemSize()];
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RunGemmWithPipelineSelection(
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a_ptr, b_ptr, aq_ptr, bq_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
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// Only for BQuantGrouped DoubleSmemBuffer is supported
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if constexpr(GemmPipeline::DoubleSmemBuffer == true &&
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kQuantType == QuantType::BQuantGrouped)
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{
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__shared__ char smem_ptr_1[GetSmemSize()];
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RunGemmWithPipelineSelection2LDS(a_ptr,
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b_ptr,
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aq_ptr,
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bq_ptr,
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c_ptr,
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smem_ptr_0,
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smem_ptr_1,
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kargs,
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splitk_batch_offset,
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i_m,
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i_n);
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}
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else
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{
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RunGemmWithPipelineSelection(a_ptr,
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b_ptr,
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aq_ptr,
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bq_ptr,
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c_ptr,
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smem_ptr_0,
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kargs,
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splitk_batch_offset,
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i_m,
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i_n);
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}
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}
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template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
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CK_TILE_DEVICE static void
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RunGemmWithPipelineSelection2LDS(const ADataType* a_ptr,
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const BDataType* b_ptr,
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const AQDataType* aq_ptr,
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const BQDataType* bq_ptr,
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CDataType* c_ptr,
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void* smem_ptr_0,
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void* smem_ptr_1,
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const QuantGroupedGemmKernelArgs& kargs,
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const typename Base::SplitKBatchOffset& splitk_batch_offset,
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const index_t block_idx_m,
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const index_t block_idx_n)
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{
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static_assert(kQuantType == QuantType::BQuantGrouped, "kQuantType must be BQuantGrouped");
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// Create Gemm tensor views, pad views and tile windows
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const auto& gemm_tensor_views_tuple =
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Base::template MakeGemmTensorViews<EpiloguePipeline::MemoryOperation>(
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a_ptr, b_ptr, aq_ptr, bq_ptr, c_ptr, kargs, splitk_batch_offset);
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const auto& gemm_pad_views = Base::MakeGemmPadViews(gemm_tensor_views_tuple);
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auto gemm_tile_windows =
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Base::MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
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const index_t num_loop = __builtin_amdgcn_readfirstlane(
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TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k));
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const TailNumber tail_num = GemmPipeline::GetBlockLoopTailNum(num_loop);
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// Run GEMM cooperatively by whole workgroup.
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const auto& a_block_window = gemm_tile_windows.at(Base::I0);
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const auto& b_block_window = gemm_tile_windows.at(Base::I2);
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const auto& bq_block_window = gemm_tile_windows.at(Base::I3);
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const auto& c_block_tile = GemmPipeline{}.template operator()(a_block_window,
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b_block_window,
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bq_block_window,
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num_loop,
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tail_num,
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smem_ptr_0,
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smem_ptr_1);
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// Run Epilogue Pipeline
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auto& c_block_window = gemm_tile_windows.at(Base::I4);
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EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr_0);
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}
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/**
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