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https://github.com/ROCm/composable_kernel.git
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Reorganize files, Part 1 (#119)
* delete obselete files * move files * build * update cmake * update cmake * fix build * reorg examples * update cmake for example and test
This commit is contained in:
Chao Liu
759
include/ck/tensor_operation/gpu/warp/xdlops_gemm.hpp
Normal file
759
include/ck/tensor_operation/gpu/warp/xdlops_gemm.hpp
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@@ -0,0 +1,759 @@
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#ifndef CK_XDLOPS_GEMM_HPP
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#define CK_XDLOPS_GEMM_HPP
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#include "common_header.hpp"
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#include "math.hpp"
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#include "amd_xdlops.hpp"
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namespace ck {
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enum struct MfmaInstr
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{
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mfma_f32_32x32x1xf32 = 0,
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mfma_f32_16x16x1xf32,
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mfma_f32_4x4x1xf32,
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mfma_f32_32x32x2xf32,
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mfma_f32_16x16x4xf32,
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mfma_f32_32x32x4f16,
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mfma_f32_16x16x4f16,
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mfma_f32_4x4x4f16,
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mfma_f32_32x32x8f16,
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mfma_f32_16x16x16f16,
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mfma_f32_32x32x8bf16_1k,
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mfma_f32_16x16x16bf16_1k,
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mfma_f32_32x32x4bf16,
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mfma_f32_16x16x8bf16,
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mfma_i32_32x32x8i8,
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mfma_i32_16x16x16i8,
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};
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template <MfmaInstr instr>
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struct mfma_type;
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_32x32x1xf32>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 4;
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static constexpr index_t num_regs_per_blk = 16;
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static constexpr index_t num_threads_per_blk = 32;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 2;
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static constexpr index_t num_output_blks = 2;
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static constexpr index_t m_per_blk = 32;
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static constexpr index_t n_per_blk = 32;
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static constexpr index_t k_per_blk = 1;
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static constexpr bool is_k_reduction = false;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_32x32x1f32<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_32x32x2xf32>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 4;
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static constexpr index_t num_regs_per_blk = 16;
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static constexpr index_t num_threads_per_blk = 32;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 2;
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static constexpr index_t num_output_blks = 1;
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static constexpr index_t m_per_blk = 32;
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static constexpr index_t n_per_blk = 32;
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static constexpr index_t k_per_blk = 1;
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static constexpr bool is_k_reduction = true;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_32x32x2f32<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_16x16x4xf32>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 1;
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static constexpr index_t num_regs_per_blk = 4;
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static constexpr index_t num_threads_per_blk = 16;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 4;
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static constexpr index_t num_output_blks = 1;
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static constexpr index_t m_per_blk = 16;
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static constexpr index_t n_per_blk = 16;
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static constexpr index_t k_per_blk = 1;
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static constexpr bool is_k_reduction = true;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_16x16x4f32<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_16x16x1xf32>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 1;
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static constexpr index_t num_regs_per_blk = 4;
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static constexpr index_t num_threads_per_blk = 16;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 4;
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static constexpr index_t num_output_blks = 4;
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static constexpr index_t m_per_blk = 16;
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static constexpr index_t n_per_blk = 16;
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static constexpr index_t k_per_blk = 1;
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static constexpr bool is_k_reduction = false;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_16x16x1f32<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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// treat 4x4x1 as a single-blk 4x64 mfma
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_4x4x1xf32>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 1;
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static constexpr index_t num_regs_per_blk = 4;
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static constexpr index_t num_threads_per_blk = 64;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 1;
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static constexpr index_t num_output_blks = 1;
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static constexpr index_t m_per_blk = 4;
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static constexpr index_t n_per_blk = 64;
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static constexpr index_t k_per_blk = 1;
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static constexpr bool is_k_reduction = false;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_4x4x1f32<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_32x32x4f16>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 4;
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static constexpr index_t num_regs_per_blk = 16;
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static constexpr index_t num_threads_per_blk = 32;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 2;
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static constexpr index_t num_output_blks = 2;
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static constexpr index_t m_per_blk = 32;
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static constexpr index_t n_per_blk = 32;
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static constexpr index_t k_per_blk = 4;
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static constexpr bool is_k_reduction = false;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_32x32x4f16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_32x32x8f16>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 4;
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static constexpr index_t num_regs_per_blk = 16;
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static constexpr index_t num_threads_per_blk = 32;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 2;
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static constexpr index_t num_output_blks = 1;
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static constexpr index_t m_per_blk = 32;
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static constexpr index_t n_per_blk = 32;
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static constexpr index_t k_per_blk = 4;
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static constexpr bool is_k_reduction = true;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_32x32x8f16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_16x16x16f16>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 1;
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static constexpr index_t num_regs_per_blk = 4;
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static constexpr index_t num_threads_per_blk = 16;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 4;
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static constexpr index_t num_output_blks = 1;
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static constexpr index_t m_per_blk = 16;
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static constexpr index_t n_per_blk = 16;
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static constexpr index_t k_per_blk = 4;
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static constexpr bool is_k_reduction = true;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_16x16x16f16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_16x16x4f16>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 1;
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static constexpr index_t num_regs_per_blk = 4;
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static constexpr index_t num_threads_per_blk = 16;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 4;
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static constexpr index_t num_output_blks = 4;
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static constexpr index_t m_per_blk = 16;
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static constexpr index_t n_per_blk = 16;
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static constexpr index_t k_per_blk = 4;
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static constexpr bool is_k_reduction = false;
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
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{
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intrin_mfma_f32_16x16x4f16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_4x4x4f16>
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 1;
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static constexpr index_t num_regs_per_blk = 4;
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static constexpr index_t num_threads_per_blk = 64;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 1;
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static constexpr index_t num_output_blks = 1;
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static constexpr index_t m_per_blk = 4;
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static constexpr index_t n_per_blk = 64;
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static constexpr index_t k_per_blk = 4;
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static constexpr bool is_k_reduction = false;
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|
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
|
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{
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intrin_mfma_f32_4x4x4f16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
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}
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};
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template <>
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struct mfma_type<MfmaInstr::mfma_f32_32x32x8bf16_1k>
|
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{
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static constexpr index_t group_size = 4;
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static constexpr index_t num_groups_per_blk = 4;
|
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static constexpr index_t num_regs_per_blk = 16;
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static constexpr index_t num_threads_per_blk = 32;
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static constexpr index_t wave_size = 64;
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static constexpr index_t num_input_blks = 2;
|
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static constexpr index_t num_output_blks = 1;
|
||||
static constexpr index_t m_per_blk = 32;
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static constexpr index_t n_per_blk = 32;
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static constexpr index_t k_per_blk = 4;
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static constexpr bool is_k_reduction = true;
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|
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template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
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||||
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
|
||||
{
|
||||
intrin_mfma_f32_32x32x8bf16_1k<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
|
||||
}
|
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};
|
||||
|
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template <>
|
||||
struct mfma_type<MfmaInstr::mfma_f32_16x16x16bf16_1k>
|
||||
{
|
||||
static constexpr index_t group_size = 4;
|
||||
static constexpr index_t num_groups_per_blk = 1;
|
||||
static constexpr index_t num_regs_per_blk = 4;
|
||||
static constexpr index_t num_threads_per_blk = 16;
|
||||
static constexpr index_t wave_size = 64;
|
||||
static constexpr index_t num_input_blks = 4;
|
||||
static constexpr index_t num_output_blks = 1;
|
||||
static constexpr index_t m_per_blk = 16;
|
||||
static constexpr index_t n_per_blk = 16;
|
||||
static constexpr index_t k_per_blk = 4;
|
||||
static constexpr bool is_k_reduction = true;
|
||||
|
||||
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
|
||||
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
|
||||
{
|
||||
intrin_mfma_f32_16x16x16bf16_1k<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct mfma_type<MfmaInstr::mfma_f32_32x32x4bf16>
|
||||
{
|
||||
static constexpr index_t group_size = 4;
|
||||
static constexpr index_t num_groups_per_blk = 4;
|
||||
static constexpr index_t num_regs_per_blk = 16;
|
||||
static constexpr index_t num_threads_per_blk = 32;
|
||||
static constexpr index_t wave_size = 64;
|
||||
static constexpr index_t num_input_blks = 2;
|
||||
static constexpr index_t num_output_blks = 1;
|
||||
static constexpr index_t m_per_blk = 32;
|
||||
static constexpr index_t n_per_blk = 32;
|
||||
static constexpr index_t k_per_blk = 2;
|
||||
static constexpr bool is_k_reduction = true;
|
||||
|
||||
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
|
||||
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
|
||||
{
|
||||
intrin_mfma_f32_32x32x4bf16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct mfma_type<MfmaInstr::mfma_f32_16x16x8bf16>
|
||||
{
|
||||
static constexpr index_t group_size = 4;
|
||||
static constexpr index_t num_groups_per_blk = 1;
|
||||
static constexpr index_t num_regs_per_blk = 4;
|
||||
static constexpr index_t num_threads_per_blk = 16;
|
||||
static constexpr index_t wave_size = 64;
|
||||
static constexpr index_t num_input_blks = 4;
|
||||
static constexpr index_t num_output_blks = 1;
|
||||
static constexpr index_t m_per_blk = 16;
|
||||
static constexpr index_t n_per_blk = 16;
|
||||
static constexpr index_t k_per_blk = 2;
|
||||
static constexpr bool is_k_reduction = true;
|
||||
|
||||
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
|
||||
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
|
||||
{
|
||||
intrin_mfma_f32_16x16x8bf16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct mfma_type<MfmaInstr::mfma_i32_32x32x8i8>
|
||||
{
|
||||
static constexpr index_t group_size = 4;
|
||||
static constexpr index_t num_groups_per_blk = 4;
|
||||
static constexpr index_t num_regs_per_blk = 16;
|
||||
static constexpr index_t num_threads_per_blk = 32;
|
||||
static constexpr index_t wave_size = 64;
|
||||
static constexpr index_t num_input_blks = 2;
|
||||
static constexpr index_t num_output_blks = 1;
|
||||
static constexpr index_t m_per_blk = 32;
|
||||
static constexpr index_t n_per_blk = 32;
|
||||
static constexpr index_t k_per_blk = 4;
|
||||
static constexpr bool is_k_reduction = true;
|
||||
|
||||
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
|
||||
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
|
||||
{
|
||||
intrin_mfma_i32_32x32x8i8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct mfma_type<MfmaInstr::mfma_i32_16x16x16i8>
|
||||
{
|
||||
static constexpr index_t group_size = 4;
|
||||
static constexpr index_t num_groups_per_blk = 1;
|
||||
static constexpr index_t num_regs_per_blk = 4;
|
||||
static constexpr index_t num_threads_per_blk = 16;
|
||||
static constexpr index_t wave_size = 64;
|
||||
static constexpr index_t num_input_blks = 4;
|
||||
static constexpr index_t num_output_blks = 1;
|
||||
static constexpr index_t m_per_blk = 16;
|
||||
static constexpr index_t n_per_blk = 16;
|
||||
static constexpr index_t k_per_blk = 4;
|
||||
static constexpr bool is_k_reduction = true;
|
||||
|
||||
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
|
||||
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
|
||||
{
|
||||
intrin_mfma_i32_16x16x16i8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
|
||||
}
|
||||
};
|
||||
|
||||
template <typename base_type, index_t MPerXdlops, index_t NPerXdlops>
|
||||
struct MfmaSelector
|
||||
{
|
||||
template <typename base_type_, index_t MPerXdlops_, index_t NPerXdlops_>
|
||||
static constexpr auto GetMfma();
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<float, 64, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_32x32x1xf32;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<float, 32, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_32x32x1xf32;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<float, 16, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_16x16x1xf32;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<float, 8, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_4x4x1xf32;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<float, 4, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_4x4x1xf32;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<float, 32, 32>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_32x32x2xf32;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<float, 16, 16>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_16x16x4xf32;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<half_t, 64, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_32x32x4f16;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<half_t, 32, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_32x32x4f16;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<half_t, 32, 32>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_32x32x8f16;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<half_t, 16, 16>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_16x16x16f16;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<half_t, 16, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_16x16x4f16;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<half_t, 8, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_4x4x4f16;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<half_t, 4, 64>()
|
||||
{
|
||||
return MfmaInstr::mfma_f32_4x4x4f16;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<bhalf_t, 32, 32>()
|
||||
{
|
||||
#if defined(CK_AMD_GPU_GFX90A)
|
||||
return MfmaInstr::mfma_f32_32x32x8bf16_1k;
|
||||
#else
|
||||
return MfmaInstr::mfma_f32_32x32x4bf16;
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<bhalf_t, 16, 16>()
|
||||
{
|
||||
#if defined(CK_AMD_GPU_GFX90A)
|
||||
return MfmaInstr::mfma_f32_16x16x16bf16_1k;
|
||||
#else
|
||||
return MfmaInstr::mfma_f32_16x16x8bf16;
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<int8_t, 32, 32>()
|
||||
{
|
||||
return MfmaInstr::mfma_i32_32x32x8i8;
|
||||
}
|
||||
|
||||
template <>
|
||||
static constexpr auto GetMfma<int8_t, 16, 16>()
|
||||
{
|
||||
return MfmaInstr::mfma_i32_16x16x16i8;
|
||||
}
|
||||
|
||||
static constexpr auto selected_mfma = mfma_type<GetMfma<base_type, MPerXdlops, NPerXdlops>()>{};
|
||||
|
||||
__host__ __device__ constexpr MfmaSelector()
|
||||
{
|
||||
static_assert(selected_mfma.group_size * selected_mfma.num_groups_per_blk ==
|
||||
selected_mfma.num_regs_per_blk,
|
||||
"wrong! num_regs_per_blk");
|
||||
|
||||
static_assert(selected_mfma.num_threads_per_blk == selected_mfma.n_per_blk,
|
||||
"n_per_blk != num_threads_per_blk");
|
||||
|
||||
static_assert(selected_mfma.num_regs_per_blk * selected_mfma.num_input_blks ==
|
||||
selected_mfma.m_per_blk,
|
||||
"m_per_blk != num_input_blks * num_regs_per_blk");
|
||||
|
||||
static_assert(selected_mfma.num_output_blks == selected_mfma.num_input_blks ||
|
||||
selected_mfma.num_output_blks == 1,
|
||||
"incorrect num_output_blks");
|
||||
|
||||
static_assert(selected_mfma.num_regs_per_blk * selected_mfma.wave_size ==
|
||||
selected_mfma.m_per_blk * selected_mfma.n_per_blk,
|
||||
"num_regs_per_blk incorrect");
|
||||
|
||||
static_assert(selected_mfma.is_k_reduction ||
|
||||
(selected_mfma.num_input_blks == selected_mfma.num_output_blks),
|
||||
"is_k_reduction wrong!");
|
||||
}
|
||||
|
||||
static constexpr bool IsABroadcast()
|
||||
{
|
||||
static_assert(NPerXdlops >= MPerXdlops, "only support ABroadcast");
|
||||
return true;
|
||||
}
|
||||
|
||||
static constexpr index_t GetKPerXdlops()
|
||||
{
|
||||
return (selected_mfma.is_k_reduction ? selected_mfma.num_input_blks : 1) *
|
||||
selected_mfma.k_per_blk;
|
||||
}
|
||||
|
||||
static constexpr index_t GetK1PerXdlops() { return selected_mfma.k_per_blk; }
|
||||
};
|
||||
|
||||
template <typename base_type, index_t MPerXdlops, index_t NPerXdlops, index_t KPack>
|
||||
struct XdlopsGemm
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
static constexpr auto I4 = Number<4>{};
|
||||
static constexpr auto I5 = Number<5>{};
|
||||
|
||||
using CIndex = MultiIndex<2>;
|
||||
|
||||
__device__ static constexpr index_t GetNumBlks() { return mfma_instr.num_output_blks; }
|
||||
|
||||
__device__ static constexpr index_t GetNumXdlops()
|
||||
{
|
||||
return MPerXdlops * NPerXdlops /
|
||||
(mfma_instr.m_per_blk * mfma_instr.n_per_blk * mfma_instr.num_output_blks);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr XdlopsGemm()
|
||||
{
|
||||
static_assert(NPerXdlops == 4 || NPerXdlops == 8 || NPerXdlops == 16 || NPerXdlops == 32 ||
|
||||
NPerXdlops == 64,
|
||||
"Only support GemmNPerXdlops == 4, 8, 16, 32 or 64 for xdlops");
|
||||
|
||||
static_assert(MPerXdlops == 4 || MPerXdlops == 8 || MPerXdlops == 16 || MPerXdlops == 32 ||
|
||||
MPerXdlops == 64,
|
||||
"Only support GemmMPerXdlops == 4, 8, 16, 32 or 64 for xdlops");
|
||||
|
||||
static_assert(KPack % mfma_instr.k_per_blk == 0, "KPack cannot be divided by k_per_blk");
|
||||
}
|
||||
|
||||
template <typename CDesc_M0_N0_M1_N1_M2_N2>
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(const CDesc_M0_N0_M1_N1_M2_N2& c_desc_m0_n0_m1_n1_m2_n2)
|
||||
{
|
||||
const auto M0 = c_desc_m0_n0_m1_n1_m2_n2.GetLength(I0);
|
||||
const auto N0 = c_desc_m0_n0_m1_n1_m2_n2.GetLength(I1);
|
||||
const auto M1 = c_desc_m0_n0_m1_n1_m2_n2.GetLength(I2);
|
||||
const auto N1 = c_desc_m0_n0_m1_n1_m2_n2.GetLength(I3);
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_desc_m0_n0_m1_n1_m2_n2,
|
||||
make_tuple(make_pass_through_transform(M0),
|
||||
make_pass_through_transform(N0),
|
||||
make_pass_through_transform(M1),
|
||||
make_pass_through_transform(N1),
|
||||
make_unmerge_transform(make_tuple(mfma_instr.num_groups_per_blk,
|
||||
mfma_instr.num_input_blks,
|
||||
mfma_instr.group_size)),
|
||||
make_pass_through_transform(mfma_instr.num_threads_per_blk)),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5>{}),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4, 5, 6>{},
|
||||
Sequence<7>{}));
|
||||
}
|
||||
|
||||
template <typename CDesc_G_M0_N0_M1_N1_M2_N2>
|
||||
__host__ __device__ static constexpr auto MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
|
||||
const CDesc_G_M0_N0_M1_N1_M2_N2& c_desc_g_m0_n0_m1_n1_m2_n2)
|
||||
{
|
||||
const auto G = c_desc_g_m0_n0_m1_n1_m2_n2.GetLength(I0);
|
||||
const auto M0 = c_desc_g_m0_n0_m1_n1_m2_n2.GetLength(I1);
|
||||
const auto N0 = c_desc_g_m0_n0_m1_n1_m2_n2.GetLength(I2);
|
||||
const auto M1 = c_desc_g_m0_n0_m1_n1_m2_n2.GetLength(I3);
|
||||
const auto N1 = c_desc_g_m0_n0_m1_n1_m2_n2.GetLength(I4);
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_desc_g_m0_n0_m1_n1_m2_n2,
|
||||
make_tuple(make_pass_through_transform(G),
|
||||
make_pass_through_transform(M0),
|
||||
make_pass_through_transform(N0),
|
||||
make_pass_through_transform(M1),
|
||||
make_pass_through_transform(N1),
|
||||
make_unmerge_transform(make_tuple(mfma_instr.num_groups_per_blk,
|
||||
mfma_instr.num_input_blks,
|
||||
mfma_instr.group_size)),
|
||||
make_pass_through_transform(mfma_instr.num_threads_per_blk)),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5>{},
|
||||
Sequence<6>{}),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5, 6, 7>{},
|
||||
Sequence<8>{}));
|
||||
}
|
||||
|
||||
__device__ static constexpr index_t GetRegSizePerXdlops()
|
||||
{
|
||||
return MPerXdlops * NPerXdlops / mfma_instr.wave_size;
|
||||
}
|
||||
|
||||
__device__ static constexpr index_t GetWaveSize() { return mfma_instr.wave_size; }
|
||||
|
||||
template <class FloatA, class FloatB, class FloatC>
|
||||
__device__ void Run(const FloatA& p_a_wave, const FloatB& p_b_wave, FloatC& p_c_thread) const
|
||||
{
|
||||
static_assert(is_same<base_type, float>::value || is_same<base_type, half_t>::value ||
|
||||
is_same<base_type, bhalf_t>::value || is_same<base_type, int8_t>::value,
|
||||
"base base_type must be float, half, bfloat16, and int8_t!");
|
||||
|
||||
static_for<0, KPack / mfma_instr.k_per_blk, 1>{}([&](auto k) {
|
||||
mfma_instr.template run<MPerXdlops, NPerXdlops>(p_a_wave[k], p_b_wave[k], p_c_thread);
|
||||
});
|
||||
}
|
||||
|
||||
__device__ static auto GetLaneId() { return get_thread_local_1d_id() % mfma_instr.wave_size; }
|
||||
|
||||
__device__ static auto GetBlkIdx()
|
||||
{
|
||||
const auto laneId = GetLaneId();
|
||||
|
||||
constexpr auto threadidx_to_blk_idx_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(
|
||||
make_tuple(1, mfma_instr.num_input_blks, mfma_instr.num_threads_per_blk))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto blk_idx =
|
||||
threadidx_to_blk_idx_adaptor.CalculateBottomIndex(make_multi_index(laneId));
|
||||
|
||||
const auto blk_id = blk_idx[I1];
|
||||
const auto blk_td = blk_idx[I2];
|
||||
|
||||
return make_tuple(blk_id, blk_td);
|
||||
}
|
||||
|
||||
__host__ __device__ static auto CalculateAThreadOriginDataIndex()
|
||||
{
|
||||
const auto laneId = GetLaneId();
|
||||
const auto blk_idx = GetBlkIdx();
|
||||
|
||||
const auto blk_id = blk_idx[I0];
|
||||
const auto blk_td = blk_idx[I1];
|
||||
|
||||
if constexpr(mfma_instr.is_k_reduction)
|
||||
{
|
||||
return make_tuple(blk_id, blk_td);
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_tuple(0, laneId);
|
||||
}
|
||||
}
|
||||
|
||||
__host__ __device__ static auto CalculateBThreadOriginDataIndex()
|
||||
{
|
||||
const auto laneId = GetLaneId();
|
||||
const auto blk_idx = GetBlkIdx();
|
||||
|
||||
const auto blk_id = blk_idx[I0];
|
||||
const auto blk_td = blk_idx[I1];
|
||||
|
||||
if constexpr(mfma_instr.is_k_reduction)
|
||||
{
|
||||
return make_tuple(blk_id, blk_td);
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_tuple(0, laneId);
|
||||
}
|
||||
}
|
||||
|
||||
__device__ static CIndex GetBeginOfThreadBlk(index_t xdlops_i, index_t blk_i)
|
||||
{
|
||||
const auto blk_idx = GetBlkIdx();
|
||||
|
||||
const auto blk_id = blk_idx[I0];
|
||||
const auto blk_td = blk_idx[I1];
|
||||
|
||||
index_t n_offset = blk_i * mfma_instr.n_per_blk + blk_td;
|
||||
index_t m_offset = xdlops_i * mfma_instr.m_per_blk + blk_id * mfma_instr.group_size;
|
||||
|
||||
return CIndex{m_offset, n_offset};
|
||||
}
|
||||
|
||||
static constexpr auto mfma = MfmaSelector<base_type, MPerXdlops, NPerXdlops>{};
|
||||
|
||||
static constexpr auto mfma_instr = mfma.selected_mfma;
|
||||
|
||||
static constexpr auto KPerXdlops = mfma.GetKPerXdlops();
|
||||
static constexpr auto K1PerXdlops = mfma.GetK1PerXdlops();
|
||||
static constexpr auto K0PerXdlops = KPerXdlops / K1PerXdlops;
|
||||
|
||||
__host__ __device__ static constexpr auto GetCM0M1M2NThreadBlkLengths()
|
||||
{
|
||||
return make_tuple(
|
||||
Number<mfma_instr.num_groups_per_blk>{}, I1, Number<mfma_instr.group_size>{}, I1);
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
Reference in New Issue
Block a user