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[rocm-libraries] ROCm/rocm-libraries#4302 (commit e62bd8a)
[CK_TILE] add tf32 support MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Proposed changes TF32 is added in CK on gfx942 and gfx950. This PR is to initiate tf32 in CK_TILE on gfx942 and gfx950. ## Checklist Please put an into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask. - [ ] I have added tests relevant to the introduced functionality, and the unit tests are passing locally - [ ] I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more than 30 seconds to run. - [ ] I have added inline documentation which enables the maintainers with understanding the motivation - [ ] I have removed the stale documentation which is no longer relevant after this pull request - [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request - [x] I have run on all changed files - [ ] Any dependent changes have been merged ## Discussion
This commit is contained in:
yinglu
committed by
assistant-librarian[bot]
assistant-librarian[bot]
parent
652d3456ca
commit
d460ab35b6
@@ -41,6 +41,17 @@ int run_gemm_example(ck_tile::ArgParser& arg_parser)
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return run_gemm_example_prec_type<GemmConfig, Invoker, ck_tile::bf16_t>(
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a_layout, b_layout, arg_parser);
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}
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#ifdef CK_GFX950_SUPPORT
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else if(data_type == "tf32")
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{
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// Pass tf32_t as A/B types - epilogue auto-detects and maps to float for data operations
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return run_gemm_example_prec_type<GemmConfig,
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Invoker,
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ck_tile::tf32_t,
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ck_tile::tf32_t,
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float>(a_layout, b_layout, arg_parser);
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}
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#endif
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else if(data_type == "fp8")
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{
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return run_gemm_example_prec_type<GemmConfig,
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@@ -6,8 +6,8 @@
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struct BasicInvoker
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{
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template <typename GemmConfig,
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typename ADataType,
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typename BDataType,
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typename ADataType_,
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typename BDataType_,
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typename DsDataType,
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typename AccDataType,
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typename CDataType,
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@@ -19,14 +19,30 @@ struct BasicInvoker
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typename CDEElementWise>
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static float gemm(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
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{
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// ADataTypeCompute: compute type (tf32_t for TF32 mode, used for warp gemm selection)
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// ADataTypeBuf: buffer/storage type (fp32 when tf32)
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using ADataTypeCompute = ADataType_;
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using BDataTypeCompute = BDataType_;
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using ADataTypeBuf = ck_tile::if_select_t<ADataType_, ck_tile::tf32_t, float, ADataType_>;
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using BDataTypeBuf = ck_tile::if_select_t<BDataType_, ck_tile::tf32_t, float, BDataType_>;
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if constexpr(std::is_same_v<ADataTypeCompute, ck_tile::tf32_t>)
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{
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static_assert(std::is_same_v<ADataTypeCompute, BDataTypeCompute>,
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"ADataTypeCompute and BDataTypeCompute must be the same");
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}
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if constexpr(Persistent)
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{
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std::cout << "WARNING: Ignoring persistent kernel option for basic gemm." << std::endl;
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}
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constexpr bool is_fp32_input = std::is_same_v<ADataTypeBuf, float>;
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constexpr bool is_tf32_compute = std::is_same_v<ADataTypeCompute, ck_tile::tf32_t>;
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// This part comes from the Codegen
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constexpr ck_tile::index_t M_Tile = 256;
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constexpr ck_tile::index_t N_Tile = 256;
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constexpr ck_tile::index_t M_Tile = is_fp32_input ? 128 : 256;
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constexpr ck_tile::index_t N_Tile = is_fp32_input ? 128 : 256;
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constexpr ck_tile::index_t K_Tile = 64;
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#if CK_TILE_USE_WMMA
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@@ -38,12 +54,14 @@ struct BasicInvoker
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constexpr ck_tile::index_t N_Warp_Tile = 16;
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constexpr ck_tile::index_t K_Warp_Tile = 16;
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#else
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constexpr ck_tile::index_t M_Warp = 2;
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constexpr ck_tile::index_t N_Warp = 2;
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// gfx950: fp32 uses 16x16x16 tile (native MFMA)
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// tf32 uses 32x32x16 tile (3x bf16 32x32x16 MFMA emulation)
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constexpr ck_tile::index_t M_Warp = (is_fp32_input && !is_tf32_compute) ? 4 : 2;
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constexpr ck_tile::index_t N_Warp = (is_fp32_input && !is_tf32_compute) ? 4 : 2;
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constexpr ck_tile::index_t K_Warp = 1;
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constexpr ck_tile::index_t M_Warp_Tile = 32;
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constexpr ck_tile::index_t N_Warp_Tile = 32;
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constexpr ck_tile::index_t M_Warp_Tile = (is_fp32_input && !is_tf32_compute) ? 16 : 32;
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constexpr ck_tile::index_t N_Warp_Tile = (is_fp32_input && !is_tf32_compute) ? 16 : 32;
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constexpr ck_tile::index_t K_Warp_Tile = 16;
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#endif
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@@ -61,17 +79,21 @@ struct BasicInvoker
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BLayout,
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CLayout>;
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using CodegenPipelineProblem = ck_tile::GemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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CodegenGemmShape,
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CodegenGemmTraits>;
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using CodegenPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataTypeBuf,
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BDataTypeBuf,
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AccDataType,
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CodegenGemmShape,
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CodegenGemmTraits,
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ck_tile::element_wise::PassThrough,
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ck_tile::element_wise::PassThrough,
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ADataTypeCompute>;
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using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ADataType,
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BDataType,
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ck_tile::CShuffleEpilogueProblem<ADataTypeCompute,
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BDataTypeCompute,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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@@ -112,7 +134,7 @@ struct BasicInvoker
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}
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// Declare rotating_mem_ptr here so it stays in scope until it is needed
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std::unique_ptr<ck_tile::RotatingMemWrapper<ADataType, BDataType>> rotating_mem_ptr;
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std::unique_ptr<ck_tile::RotatingMemWrapper<ADataTypeBuf, BDataTypeBuf>> rotating_mem_ptr;
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std::function<void()> preprocess;
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auto clear_gemm_output = [&]() {
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@@ -125,16 +147,21 @@ struct BasicInvoker
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{
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std::cout << "Flushing cache..." << std::endl;
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ck_tile::HostTensor<ADataType> a_m(ck_tile::host_tensor_descriptor(
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ck_tile::HostTensor<ADataTypeBuf> a_m(ck_tile::host_tensor_descriptor(
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args.M, args.K, args.stride_A, is_row_major(ALayout{})));
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ck_tile::HostTensor<BDataType> b_n(ck_tile::host_tensor_descriptor(
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ck_tile::HostTensor<BDataTypeBuf> b_n(ck_tile::host_tensor_descriptor(
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args.K, args.N, args.stride_B, is_row_major(BLayout{})));
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auto size_a_buffer = a_m.get_element_space_size_in_bytes();
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auto size_b_buffer = b_n.get_element_space_size_in_bytes();
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rotating_mem_ptr = std::make_unique<ck_tile::RotatingMemWrapper<ADataType, BDataType>>(
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kargs.as_ptr[0], kargs.bs_ptr[0], s.rotating_count_, size_a_buffer, size_b_buffer);
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rotating_mem_ptr =
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std::make_unique<ck_tile::RotatingMemWrapper<ADataTypeBuf, BDataTypeBuf>>(
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kargs.as_ptr[0],
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kargs.bs_ptr[0],
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s.rotating_count_,
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size_a_buffer,
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size_b_buffer);
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rotating_mem_ptr->Print();
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preprocess = [&]() {
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@@ -35,6 +35,10 @@ struct GemmConfigBase
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static constexpr bool TiledMMAPermuteN = false;
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};
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// Type trait for tf32 storage type (tf32 uses float for memory layout calculations)
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template <typename T>
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using prec_storage_type = ck_tile::if_select_t<T, ck_tile::tf32_t, float, T>;
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template <typename PrecType>
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struct GemmConfigMemoryInterwave : public GemmConfigBase
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{
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@@ -81,7 +85,7 @@ struct GemmConfigComputeV3 : public GemmConfigBase
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// Compute V3 only support Intrawave scheduler
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static constexpr ck_tile::index_t M_Tile = 16;
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static constexpr ck_tile::index_t N_Tile = 64;
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static constexpr ck_tile::index_t K_Tile = 256 / sizeof(PrecType);
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static constexpr ck_tile::index_t K_Tile = 256 / sizeof(prec_storage_type<PrecType>);
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static constexpr ck_tile::index_t M_Warp = 1;
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static constexpr ck_tile::index_t N_Warp = 4;
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@@ -121,7 +125,7 @@ struct GemmConfigComputeV3_2 : public GemmConfigBase
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{
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static constexpr ck_tile::index_t M_Tile = 128;
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static constexpr ck_tile::index_t N_Tile = 128;
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static constexpr ck_tile::index_t K_Tile = 128 / sizeof(PrecType);
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static constexpr ck_tile::index_t K_Tile = 128 / sizeof(prec_storage_type<PrecType>);
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static constexpr ck_tile::index_t M_Warp = 2;
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static constexpr ck_tile::index_t N_Warp = 2;
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@@ -293,7 +297,7 @@ struct GemmConfigPreshufflePrefill : public GemmConfigBase
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{
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static constexpr ck_tile::index_t M_Tile = 128;
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static constexpr ck_tile::index_t N_Tile = 128;
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static constexpr ck_tile::index_t K_Tile = 128 / sizeof(PrecType);
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static constexpr ck_tile::index_t K_Tile = 128 / sizeof(prec_storage_type<PrecType>);
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static constexpr ck_tile::index_t M_Warp = 1;
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static constexpr ck_tile::index_t N_Warp = 4;
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@@ -302,7 +306,7 @@ struct GemmConfigPreshufflePrefill : public GemmConfigBase
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static constexpr ck_tile::index_t M_Warp_Tile = 16;
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static constexpr ck_tile::index_t N_Warp_Tile = 16;
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static constexpr ck_tile::index_t K_Warp_Tile =
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ck_tile::get_k_warp_tile<PrecType, M_Warp_Tile, true>();
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ck_tile::get_k_warp_tile<prec_storage_type<PrecType>, M_Warp_Tile, true>();
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static constexpr int kBlockPerCu = 2;
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static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
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@@ -324,6 +328,15 @@ struct GemmConfigPreshufflePrefill_Wmma : public GemmConfigPreshufflePrefill<Pre
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template <typename ADataType, typename BDataType = ADataType, typename CDataType = ADataType>
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struct GemmTypeConfig;
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template <>
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struct GemmTypeConfig<ck_tile::tf32_t, ck_tile::tf32_t, float>
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{
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using ADataType = float;
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using BDataType = float;
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using AccDataType = float;
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using CDataType = float;
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};
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template <>
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struct GemmTypeConfig<ck_tile::half_t>
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{
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@@ -486,7 +499,7 @@ inline auto create_args()
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "2", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8/pk_int4_t")
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.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8/pk_int4_t/tf32 (tf32 only on gfx950)")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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@@ -30,6 +30,7 @@ auto calculate_rtol_atol(const ck_tile::index_t K,
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ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
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const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
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max_accumulated_value, kbatch);
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// Use higher threshold
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return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
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}
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@@ -205,11 +206,13 @@ std::tuple<ck_tile::index_t, ck_tile::index_t, ck_tile::index_t> inline parse_ge
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return std::make_tuple(M, N, K);
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}
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// ADataType_ and BDataType_ are original types (e.g., tf32_t for TF32 mode)
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// They are passed through invoke_gemm to invoker for tf32 auto-detection
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template <typename GemmConfig,
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typename Invoker,
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typename ADataType,
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typename BDataType = ADataType,
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typename CDataType = ADataType,
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typename ADataType_,
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typename BDataType_ = ADataType_,
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typename CDataType_ = ADataType_,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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@@ -218,7 +221,18 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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const BLayout b_layout = BLayout{},
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[[maybe_unused]] const CLayout c_layout = CLayout{})
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{
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using AccDataType = typename GemmTypeConfig<ADataType, BDataType, CDataType>::AccDataType;
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// ADataTypeCompute: compute type (tf32_t for TF32 mode, used for warp gemm selection)
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// ADataTypeBuf: buffer/storage type (fp32 when tf32, from TypeConfig)
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using ADataTypeCompute = ADataType_;
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using BDataTypeCompute = BDataType_;
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// Use GemmTypeConfig to get actual data types for tensor operations
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// This handles tf32 -> float mapping for host tensors and device buffers
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using TypeConfig = GemmTypeConfig<ADataType_, BDataType_, CDataType_>;
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using ADataTypeBuf = typename TypeConfig::ADataType;
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using BDataTypeBuf = typename TypeConfig::BDataType;
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using CDataType = typename TypeConfig::CDataType;
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using AccDataType = typename TypeConfig::AccDataType;
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ck_tile::index_t M = arg_parser.get_int("m");
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ck_tile::index_t N = arg_parser.get_int("n");
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@@ -242,27 +256,27 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
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stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
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ck_tile::HostTensor<ADataType> a_m_k(
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ck_tile::HostTensor<ADataTypeBuf> a_m_k(
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ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
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ck_tile::HostTensor<BDataType> b_k_n(
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ck_tile::HostTensor<BDataTypeBuf> b_k_n(
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ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
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ck_tile::HostTensor<CDataType> c_m_n_dev_result(
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ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
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if(init_method == 0)
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{
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ck_tile::FillUniformDistribution<ADataType>{-2.f, 2.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataType>{-2.f, 2.f}(b_k_n);
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ck_tile::FillUniformDistribution<ADataTypeBuf>{-2.f, 2.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataTypeBuf>{-2.f, 2.f}(b_k_n);
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}
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else if(init_method == 1)
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{
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ck_tile::FillMonotonicSeq<ADataType>{}(a_m_k);
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ck_tile::FillMonotonicSeq<BDataType>{}(b_k_n);
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ck_tile::FillMonotonicSeq<ADataTypeBuf>{}(a_m_k);
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ck_tile::FillMonotonicSeq<BDataTypeBuf>{}(b_k_n);
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}
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else if(init_method == 2)
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{
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ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_k_n);
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ck_tile::FillUniformDistribution<ADataTypeBuf>{1.f, 1.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataTypeBuf>{1.f, 1.f}(b_k_n);
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}
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else
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{
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@@ -274,7 +288,7 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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{
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if constexpr(GemmConfig::UseStructuredSparsity)
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{
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ck_tile::AdjustToStructuredSparsity<ADataType>{}(a_m_k);
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ck_tile::AdjustToStructuredSparsity<ADataTypeBuf>{}(a_m_k);
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}
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}
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@@ -286,7 +300,7 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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if constexpr(preshuffle)
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{
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ck_tile::HostTensor<BDataType> b_shuffle_host = [&]() {
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ck_tile::HostTensor<BDataTypeBuf> b_shuffle_host = [&]() {
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if constexpr(GemmConfig::TiledMMAPermuteN)
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{
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std::cout << "Run with PermuteN" << std::endl;
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@@ -299,7 +313,7 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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}
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}();
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// shuffled buffer B for device implementation
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if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
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if constexpr(std::is_same_v<BDataTypeBuf, ck_tile::pk_int4_t>)
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{
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ck_tile::permute_vectors_i4x4_b(b_shuffle_host);
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}
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@@ -307,16 +321,16 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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}
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else
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{
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if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
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if constexpr(std::is_same_v<BDataTypeBuf, ck_tile::pk_int4_t>)
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{
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// Permute vector pk_i4x4 data for device implementation
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ck_tile::HostTensor<BDataType> b_k_n_dev = b_k_n;
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ck_tile::HostTensor<BDataTypeBuf> b_k_n_dev = b_k_n;
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if constexpr(GemmConfig::PermuteB)
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{
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permute_tensor_b<GemmConfig,
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decltype(b_k_n_dev),
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ADataType,
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BDataType,
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ADataTypeBuf,
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BDataTypeBuf,
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AccDataType,
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CDataType,
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ALayout,
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@@ -343,8 +357,8 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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float ave_time = invoke_gemm<GemmConfig,
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Invoker,
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ADataType,
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BDataType,
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ADataTypeCompute,
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BDataTypeCompute,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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@@ -371,8 +385,8 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_byte =
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sizeof(ADataType) * M * K / ck_tile::numeric_traits<ADataType>::PackedSize +
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sizeof(BDataType) * N * K / ck_tile::numeric_traits<BDataType>::PackedSize +
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sizeof(ADataTypeBuf) * M * K / ck_tile::numeric_traits<ADataTypeBuf>::PackedSize +
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sizeof(BDataTypeBuf) * N * K / ck_tile::numeric_traits<BDataTypeBuf>::PackedSize +
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sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_byte / 1.E6 / ave_time;
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@@ -381,8 +395,8 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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<< " StrideA=" << stride_A << " StrideB=" << stride_B << " StrideC=" << stride_C
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<< " A_Layout=" << ALayout::name << " B_Layout =" << BLayout::name
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<< " C_Layout=" << CLayout::name
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<< " A_Type=" << ck_tile::DataTypeTraits<ADataType>::name
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<< " B_Type=" << ck_tile::DataTypeTraits<BDataType>::name
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<< " A_Type=" << ck_tile::DataTypeTraits<ADataTypeBuf>::name
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<< " B_Type=" << ck_tile::DataTypeTraits<BDataTypeBuf>::name
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<< " C_Type=" << ck_tile::DataTypeTraits<CDataType>::name
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<< " StructuredSparsity=" << (GemmConfig::UseStructuredSparsity ? "on" : "off")
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<< " Persistent=" << (persistent ? "on" : "off") << " : " << ave_time << " ms, "
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@@ -397,17 +411,18 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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if(arg_parser.get_int("v") == 1)
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{
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ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
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ck_tile::reference_gemm<ADataTypeCompute, BDataTypeCompute, AccDataType, CDataType>(
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a_m_k, b_k_n, c_m_n_ref);
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const float max_accumulated_value =
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*std::max_element(c_m_n_ref.mData.begin(), c_m_n_ref.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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const auto rtol_atol =
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calculate_rtol_atol<ADataTypeCompute, BDataTypeCompute, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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pass = do_verify(c_m_n_dev_result, c_m_n_ref, rtol_atol, "CPU");
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}
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else if(arg_parser.get_int("v") == 2)
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{
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if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
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if constexpr(std::is_same_v<BDataTypeBuf, ck_tile::pk_int4_t>)
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{
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// Restore input for B for gpu reference
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b_k_n_dev_buf.ToDevice(b_k_n.data());
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@@ -421,12 +436,12 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_ref.get_element_space_size_in_bytes());
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c_m_n_gpu_buf_ref.SetZero();
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ADataType* d_A = static_cast<ADataType*>(a_m_k_dev_buf.GetDeviceBuffer());
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BDataType* d_B = static_cast<BDataType*>(b_k_n_dev_buf.GetDeviceBuffer());
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CDataType* d_C = static_cast<CDataType*>(c_m_n_gpu_buf_ref.GetDeviceBuffer());
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ADataTypeBuf* d_A = static_cast<ADataTypeBuf*>(a_m_k_dev_buf.GetDeviceBuffer());
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BDataTypeBuf* d_B = static_cast<BDataTypeBuf*>(b_k_n_dev_buf.GetDeviceBuffer());
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CDataType* d_C = static_cast<CDataType*>(c_m_n_gpu_buf_ref.GetDeviceBuffer());
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ck_tile::reference_gemm_gpu<ADataType,
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BDataType,
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ck_tile::reference_gemm_gpu<ADataTypeCompute,
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BDataTypeCompute,
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AccDataType,
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CDataType,
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ALayout,
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@@ -437,8 +452,9 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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const float max_accumulated_value =
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*std::max_element(c_m_n_ref.mData.begin(), c_m_n_ref.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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const auto rtol_atol =
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calculate_rtol_atol<ADataTypeCompute, BDataTypeCompute, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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pass = do_verify(c_m_n_dev_result, c_m_n_ref, rtol_atol, "GPU");
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}
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@@ -447,8 +463,8 @@ int run_gemm_example_with_layouts(ck_tile::ArgParser& arg_parser,
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dump_gemm_json_results<ALayout,
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BLayout,
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CLayout,
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ADataType,
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BDataType,
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ADataTypeBuf,
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BDataTypeBuf,
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CDataType,
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GemmConfig,
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ck_tile::DataTypeTraits>(arg_parser.get_str("jsonfile"),
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Reference in New Issue
Block a user