mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-07-17 09:08:35 +00:00
add ckProfiler. performance debugging for blockscale_wp prefill
This commit is contained in:
aska-0096
@@ -54,8 +54,8 @@ constexpr auto BlockGemmBlockScaleBPreshufflePipeline_Selector()
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NPerBlock,
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KPerBlock,
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MScaleBlock,
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NScaleBlock,
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KScaleBlock,
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NScaleBlock,
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KScaleBlock,
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MPerXDL,
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NPerXDL,
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MRepeat,
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@@ -108,8 +108,8 @@ KScaleBlock,
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NPerBlock,
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KPerBlock,
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MScaleBlock,
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NScaleBlock,
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KScaleBlock,
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NScaleBlock,
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KScaleBlock,
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MPerXDL,
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NPerXDL,
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MRepeat,
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@@ -55,8 +55,8 @@ template <index_t BlockSize,
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index_t NPerBlock,
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index_t KPerBlock,
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index_t MScaleBlock,
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index_t NScaleBlock,
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index_t KScaleBlock,
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index_t NScaleBlock,
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index_t KScaleBlock,
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index_t MPerXDL,
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index_t NPerXDL,
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index_t MRepeat,
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@@ -198,7 +198,8 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v1<BlockGemmPipelineS
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constexpr auto num_buffer_load_inst_b = HotLoopInstList::B_Buffer_Load_Inst_Num * MWaves;
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constexpr auto num_pk_fma_per_kscaleblock = MPerXDL == 16 ? 2 : 8;
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constexpr auto num_mfma_per_kscaleblock = MPerXDL == 16 ? KScaleBlock / 32 : KScaleBlock / 16;
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constexpr auto num_mfma_per_kscaleblock =
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MPerXDL == 16 ? KScaleBlock / 32 : KScaleBlock / 16;
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// B global
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static_for<0, num_buffer_load_inst_b, 1>{}([&](auto i) {
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@@ -481,7 +482,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v1<BlockGemmPipelineS
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c_scale_thread_buf[Number<cscale_offset>{}];
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c_scale_thread_vec.template AsType<AccDataType>()(Number<1>{}) =
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c_scale_thread_buf[Number<cscale_offset>{}];
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static_for<0, KRepeat / num_scale_k_block, 1>{}([&](auto k0) {
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vector_type<ComputeDataType, KPack> a_thread_vec;
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vector_type<ComputeDataType, KPack> b_thread_vec;
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@@ -730,7 +731,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v1<BlockGemmPipelineS
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c_scale_thread_buf[Number<cscale_offset>{}];
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c_scale_thread_vec.template AsType<AccDataType>()(Number<1>{}) =
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c_scale_thread_buf[Number<cscale_offset>{}];
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static_for<0, KRepeat / num_scale_k_block, 1>{}([&](auto k0) {
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vector_type<ComputeDataType, KPack> a_thread_vec;
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vector_type<ComputeDataType, KPack> b_thread_vec;
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@@ -271,7 +271,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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else if constexpr(stage.value == 1)
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{
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@@ -390,7 +390,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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}
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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else if constexpr(stage.value == 2)
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{
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@@ -506,7 +506,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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}
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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else
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{
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@@ -527,7 +527,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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}
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@@ -569,7 +569,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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else if constexpr(stage.value == 1)
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{
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@@ -616,7 +616,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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}
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}
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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else
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{
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@@ -628,7 +628,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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}
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@@ -650,7 +650,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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}
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template <bool HasMainLoop,
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@@ -715,7 +715,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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{
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ignore = b_block_desc;
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ignore = b_block_buf;
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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static_assert(CScaleThreadDesc{}.GetLength(Number<0>{}) == 1,
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"Pipeline v3 only support scaleblocksliceK=1");
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static_assert(CScaleThreadDesc{}.GetLength(Number<2>{}) == 1,
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@@ -836,7 +836,7 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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a_thread_buf);
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});
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// __builtin_amdgcn_sched_barrier(0);
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__builtin_amdgcn_sched_barrier(0);
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// main body
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if constexpr(HasMainLoop)
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@@ -1001,11 +1001,10 @@ struct BlockwiseGemmXdlops_pipeline_blockscale_bpreshuffle_v3<BlockGemmPipelineS
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b_scale_thread_copy_step);
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__builtin_amdgcn_sched_group_barrier(0x020, MRepeat + 1, 0); // VMEM read
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__builtin_amdgcn_sched_barrier(0);
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};
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LoopFunc(I0, I1);
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// Just adding this will cause correctness issue.
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// __builtin_amdgcn_sched_barrier(0);
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LoopFunc(I1, I0);
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i += 2;
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@@ -233,11 +233,11 @@ struct DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle
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}
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};
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constexpr index_t minimum_occupancy =
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(BlkGemmPipeSched == BlockGemmPipelineScheduler::Intrawave &&
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MPerBlock * NPerBlock / BlockSize > 64)
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? 1
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: 2;
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constexpr index_t minimum_occupancy = 2;
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// (BlkGemmPipeSched == BlockGemmPipelineScheduler::Intrawave &&
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// MPerBlock * NPerBlock / BlockSize > 64)
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// ? 1
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// : 2;
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if(has_main_k_block_loop)
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{
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@@ -1125,7 +1125,7 @@ struct GridwiseGemmMultiD_blockscale_xdl_cshuffle_v3_b_preshuffle
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ignore = b_element_op;
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const auto a_grid_desc_ak0_m_ak1 = MakeAGridDescriptor_AK0_M_AK1(
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problem.M, problem.MPadded, problem.K, problem.KPadded, problem.StrideA, problem.AK0);
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const auto b_grid_desc_bpreshuffled =
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MakeBGridDescriptor_Preshuffled(problem.BN0Shuffled, problem.BK0Shuffled);
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const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N<CLayout>(
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@@ -0,0 +1,172 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <vector>
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#include <memory>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3_blockscale_bpreshuffle.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace instance {
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#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_FP8))
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void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_default_instances(
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std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
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Col,
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Tuple<>,
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Row,
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F8,
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F32,
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F8,
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F32,
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Tuple<>,
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BF16,
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1,
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128,
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128,
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PassThrough,
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PassThrough,
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PassThrough>>>&
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instances);
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void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_kpadding_instances(
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std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
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Col,
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Tuple<>,
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Row,
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F8,
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F32,
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F8,
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F32,
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Tuple<>,
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BF16,
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1,
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128,
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128,
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PassThrough,
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PassThrough,
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PassThrough>>>&
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instances);
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void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_v1_default_instances(
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std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
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Col,
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Tuple<>,
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Row,
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F8,
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F32,
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F8,
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F32,
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Tuple<>,
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BF16,
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1,
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128,
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128,
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PassThrough,
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PassThrough,
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PassThrough>>>&
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instances);
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void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_v1_kpadding_instances(
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std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
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Col,
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Tuple<>,
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Row,
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F8,
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F32,
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F8,
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F32,
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Tuple<>,
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BF16,
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1,
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128,
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128,
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PassThrough,
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PassThrough,
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PassThrough>>>&
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instances);
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#endif
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template <typename A0DataType,
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typename A1DataType,
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typename B0DataType,
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typename B1DataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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struct DeviceOperationInstanceFactory<
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ck::tensor_operation::device::DeviceGemmMultipleD_BlockScale_BPreshuffle<
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ALayout,
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BLayout,
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Tuple<>,
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CLayout,
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A0DataType,
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A1DataType,
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B0DataType,
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B1DataType,
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Tuple<>,
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CDataType,
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1,
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128,
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128,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough>>
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{
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using DeviceOp =
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DeviceGemmMultipleD_BlockScale_BPreshuffle<ALayout,
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BLayout,
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Tuple<>,
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CLayout,
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A0DataType,
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A1DataType,
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B0DataType,
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B1DataType,
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Tuple<>,
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CDataType,
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1,
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128,
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128,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough>;
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static auto GetInstances()
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{
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std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
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#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_FP8))
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if constexpr(is_same_v<A0DataType, f8_t> && is_same_v<B0DataType, f8_t> &&
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is_same_v<CDataType, bhalf_t>)
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{
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if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
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is_same_v<CLayout, Row>)
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{
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add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_default_instances(
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op_ptrs);
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add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_kpadding_instances(
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op_ptrs);
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add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_v1_default_instances(
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op_ptrs);
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add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_v1_kpadding_instances(
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op_ptrs);
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}
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}
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#endif
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return op_ptrs;
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}
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};
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} // namespace instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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@@ -0,0 +1,16 @@
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# ONLY XDL_KERNELS
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set(GEMM_BLOCKSCALE_WP_INSTANCES)
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list(APPEND GEMM_BLOCKSCALE_WP_INSTANCES
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device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_comp_default_instance.cpp
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device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_comp_kpadding_instance.cpp
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device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_mem_v1_default_instance.cpp
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device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_mem_v1_kpadding_instance.cpp
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)
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set_source_files_properties(device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_comp_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
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set_source_files_properties(device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_comp_kpadding_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
|
||||
set_source_files_properties(device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_mem_v1_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
|
||||
set_source_files_properties(device_gemm_blockscale_wp_xdl_f8_f8_bf16/device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128_mem_v1_kpadding_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
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add_instance_library(device_gemm_blockscale_wp_instance ${GEMM_BLOCKSCALE_WP_INSTANCES})
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@@ -0,0 +1,86 @@
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// SPDX-License-Identifier: MIT
|
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
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|
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3_blockscale_bpreshuffle.hpp"
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|
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#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
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|
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace instance {
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using F8 = f8_t;
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using BF16 = bhalf_t;
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using F32 = float;
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using Row = tensor_layout::gemm::RowMajor;
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using Col = tensor_layout::gemm::ColumnMajor;
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|
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template <index_t... Is>
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using S = Sequence<Is...>;
|
||||
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||||
using PassThrough = element_wise::PassThrough;
|
||||
using PassThrough = element_wise::PassThrough;
|
||||
|
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static constexpr auto GemmDefault = GemmSpecialization::Default;
|
||||
static constexpr auto GemmKPadding = GemmSpecialization::KPadding;
|
||||
static constexpr auto GemmMNPadding = GemmSpecialization::MNPadding;
|
||||
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
|
||||
|
||||
static constexpr auto Intrawave = BlockGemmPipelineScheduler::Intrawave;
|
||||
|
||||
template <GemmSpecialization GemmSpec>
|
||||
using device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_instances = std::tuple<
|
||||
// clang-format off
|
||||
//################################################| ALayout| BLayout| DsLayout| ELayout| AData| BData| DsData| EData| AccData| Cshuffle| A| B| C| GEMM| Block| Scale| Scale| Scale| MPer| NPer| KPer| AK1| BK1|MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Block-wiseGemm| Block-wiseGemm|
|
||||
//################################################| | | | | Type| Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector| Pipeline| Pipeline|
|
||||
//################################################| | | | | | | | | | | Operation| Operation| Operation| | | M| N| K| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
|
||||
//################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
|
||||
// Compute friendly
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8, F32, F8, F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 128, 128, 128, 16, 16, 32, 32, 4, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8, F32, F8, F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 128, 64, 128, 16, 16, 16, 16, 4, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8, F32, F8, F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 64, 128, 128, 16, 16, 16, 16, 4, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8, F32, F8, F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 64, 64, 128, 16, 16, 16, 16, 4, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
template <BlockGemmPipelineScheduler BlkGemmPipeSched, GemmSpecialization GemmSpec>
|
||||
using device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_instances = std::tuple<
|
||||
// clang-format off
|
||||
//################################| ALayout| BLayout| DsLayout| ELayout|AData | BData| DsData| EData| AccData| Cshuffle| A| B| C| GEMM| Block| Scale| Scale| Scale| MPer| NPer| KPer| AK1| BK1|MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Block-wiseGemm| Block-wiseGemm|
|
||||
//################################| | | | | Type | Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector| Pipeline| Pipeline|
|
||||
//################################| | | | | | | | | | | Operation| Operation| Operation| | | M| N| K| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
|
||||
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
|
||||
// Memory friendly
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 16, 256, 128, 8, 16, 16, 16, 1, 4, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 16, 128, 128, 8, 16, 16, 16, 1, 2, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 16, 64, 128, 8, 16, 16, 16, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 16, 128, 256, 16, 16, 16, 16, 1, 2, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 16, 1, 16>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 16, 64, 256, 16, 16, 16, 16, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 32, 256, 128, 16, 16, 32, 32, 1, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 32, 128, 128, 16, 16, 32, 32, 1, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 32, 64, 128, 16, 16, 16, 16, 2, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 32, 128, 256, 16, 16, 32, 32, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 32, 64, 256, 16, 16, 16, 16, 2, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 64, 256, 128, 16, 16, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 64, 128, 128, 16, 16, 32, 32, 2, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 64, 64, 128, 16, 16, 32, 32, 1, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 64, 128, 256, 16, 16, 32, 32, 2, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
|
||||
DeviceGemmMultiD_BlockScale_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<>, Row, F8,F32, F8,F32, Tuple<>, BF16, F32, F32, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 1, 128, 128, 64, 64, 256, 16, 16, 32, 32, 1, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>
|
||||
// clang-format on
|
||||
>;
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,38 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_default_instances(
|
||||
std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
|
||||
Col,
|
||||
Tuple<>,
|
||||
Row,
|
||||
F8,
|
||||
F32,
|
||||
F8,
|
||||
F32,
|
||||
Tuple<>,
|
||||
BF16,
|
||||
1,
|
||||
128,
|
||||
128,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>&
|
||||
instances)
|
||||
{
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_instances<GemmDefault>{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,38 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_kpadding_instances(
|
||||
std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
|
||||
Col,
|
||||
Tuple<>,
|
||||
Row,
|
||||
F8,
|
||||
F32,
|
||||
F8,
|
||||
F32,
|
||||
Tuple<>,
|
||||
BF16,
|
||||
1,
|
||||
128,
|
||||
128,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>&
|
||||
instances)
|
||||
{
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_comp_instances<GemmKPadding>{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,39 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_v1_default_instances(
|
||||
std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
|
||||
Col,
|
||||
Tuple<>,
|
||||
Row,
|
||||
F8,
|
||||
F32,
|
||||
F8,
|
||||
F32,
|
||||
Tuple<>,
|
||||
BF16,
|
||||
1,
|
||||
128,
|
||||
128,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>&
|
||||
instances)
|
||||
{
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_instances<Intrawave,
|
||||
GemmDefault>{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,39 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_128_128_128.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
void add_device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_v1_kpadding_instances(
|
||||
std::vector<std::unique_ptr<DeviceGemmMultipleD_BlockScale_BPreshuffle<Row,
|
||||
Col,
|
||||
Tuple<>,
|
||||
Row,
|
||||
F8,
|
||||
F32,
|
||||
F8,
|
||||
F32,
|
||||
Tuple<>,
|
||||
BF16,
|
||||
1,
|
||||
128,
|
||||
128,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>&
|
||||
instances)
|
||||
{
|
||||
add_device_operation_instances(
|
||||
instances,
|
||||
device_gemm_blockscale_wp_xdl_f8_f8_bf16_mk_nk_mn_1_128_128_mem_instances<Intrawave,
|
||||
GemmKPadding>{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
408
profiler/include/profiler/profile_gemm_blockscale_wp_impl.hpp
Normal file
408
profiler/include/profiler/profile_gemm_blockscale_wp_impl.hpp
Normal file
@@ -0,0 +1,408 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <typeinfo>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3_blockscale_bpreshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/gpu/gemm_blockscale_wp.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
#include "ck/library/utility/literals.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace profiler {
|
||||
|
||||
template <typename InOutDataType>
|
||||
void preShuffleBuffer(const InOutDataType* src, InOutDataType* dst, int N, int K, int NXdl)
|
||||
{
|
||||
int KPack = 16;
|
||||
int NLane = NXdl;
|
||||
int KLane = 64 / NLane;
|
||||
|
||||
int K0 = K / (KLane * KPack);
|
||||
// K -> K0 KLane KPack
|
||||
// N -> N0 NLane
|
||||
// N, K -> N0 K0 KLane NLane KPack
|
||||
int tempk;
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
for(int k = 0; k < K; ++k)
|
||||
{
|
||||
int n0 = n / NLane;
|
||||
int n1 = n % NLane;
|
||||
|
||||
int k0 = k / (KLane * KPack);
|
||||
tempk = k % (KLane * KPack);
|
||||
int k1 = tempk / KPack;
|
||||
int k2 = tempk % KPack;
|
||||
|
||||
int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
|
||||
k1 * KPack * NLane + n1 * KPack + k2;
|
||||
|
||||
dst[outputIndex] = src[n * K + k];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename A0DataType,
|
||||
typename A1DataType,
|
||||
typename B0DataType,
|
||||
typename B1DataType,
|
||||
typename ComputeDataType,
|
||||
typename AccDataType,
|
||||
typename EDataType,
|
||||
index_t ScaleBlockM,
|
||||
index_t ScaleBlockN,
|
||||
index_t ScaleBlockK,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename ELayout>
|
||||
bool profile_gemm_blockscale_weighpreshuffle_impl(int do_verification,
|
||||
int init_method,
|
||||
bool do_log,
|
||||
bool time_kernel,
|
||||
int M,
|
||||
int N,
|
||||
int K,
|
||||
int StrideA,
|
||||
int StrideB,
|
||||
int StrideE,
|
||||
int n_warmup,
|
||||
int n_iter,
|
||||
uint64_t rotating = 0)
|
||||
{
|
||||
bool pass = true;
|
||||
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
using namespace ck::literals;
|
||||
|
||||
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor({row, col}, {stride, 1_uz});
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor({row, col}, {1_uz, stride});
|
||||
}
|
||||
};
|
||||
|
||||
ck::index_t Scale_Stride_AM = ck::is_same_v<ALayout, tensor_layout::gemm::RowMajor>
|
||||
? ((K + ScaleBlockK - 1) / ScaleBlockK)
|
||||
: ((M + ScaleBlockM - 1) / ScaleBlockM);
|
||||
ck::index_t Scale_Stride_BN = ck::is_same_v<BLayout, ck::tensor_layout::gemm::ColumnMajor>
|
||||
? ((K + ScaleBlockK - 1) / ScaleBlockK)
|
||||
: ((N + ScaleBlockN - 1) / ScaleBlockN);
|
||||
|
||||
Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
||||
Tensor<A1DataType> a1_m_k(f_host_tensor_descriptor((M + ScaleBlockM - 1) / ScaleBlockM,
|
||||
(K + ScaleBlockK - 1) / ScaleBlockK,
|
||||
Scale_Stride_AM,
|
||||
ALayout{}));
|
||||
Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
||||
Tensor<B0DataType> b_preshuffled_mfma16(
|
||||
f_host_tensor_descriptor(K, N, StrideB, BLayout{})); // use layout only for size
|
||||
Tensor<B0DataType> b_preshuffled_mfma32(
|
||||
f_host_tensor_descriptor(K, N, StrideB, BLayout{})); // use layout only for size
|
||||
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor((K + ScaleBlockK - 1) / ScaleBlockK,
|
||||
(N + ScaleBlockN - 1) / ScaleBlockN,
|
||||
Scale_Stride_BN,
|
||||
BLayout{}));
|
||||
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
|
||||
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
|
||||
|
||||
int total_gemm_needed =
|
||||
a0_m_k.GetElementSpaceSizeInBytes() + b0_k_n.GetElementSpaceSizeInBytes() +
|
||||
a1_m_k.GetElementSpaceSizeInBytes() + b1_k_n.GetElementSpaceSizeInBytes();
|
||||
int rotating_count = std::max(
|
||||
1,
|
||||
std::min(n_iter,
|
||||
static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
|
||||
|
||||
std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
|
||||
std::cout << "a1_m_k: " << a1_m_k.mDesc << std::endl;
|
||||
std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
|
||||
std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
|
||||
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
|
||||
std::cout << "rotating count: " << rotating_count << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
|
||||
b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
|
||||
a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
|
||||
b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
|
||||
break;
|
||||
default:
|
||||
a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{-0.5, 0.5});
|
||||
b0_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
|
||||
a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
|
||||
b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
|
||||
}
|
||||
|
||||
preShuffleBuffer(b0_k_n.mData.data(), b_preshuffled_mfma16.mData.data(), N, K, 16);
|
||||
preShuffleBuffer(b0_k_n.mData.data(), b_preshuffled_mfma32.mData.data(), N, K, 32);
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CElementOp = PassThrough;
|
||||
|
||||
const auto a_element_op = AElementOp{};
|
||||
const auto b_element_op = BElementOp{};
|
||||
const auto c_element_op = CElementOp{};
|
||||
|
||||
DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf_mfma16(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf_mfma32(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem a1_device_buf(sizeof(A1DataType) * a1_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b1_device_buf(sizeof(B1DataType) * b1_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
a0_device_buf.ToDevice(a0_m_k.mData.data());
|
||||
b_device_buf_mfma16.ToDevice(b_preshuffled_mfma16.mData.data());
|
||||
b_device_buf_mfma32.ToDevice(b_preshuffled_mfma32.mData.data());
|
||||
a1_device_buf.ToDevice(a1_m_k.mData.data());
|
||||
b1_device_buf.ToDevice(b1_k_n.mData.data());
|
||||
|
||||
using DeviceOp =
|
||||
ck::tensor_operation::device::DeviceGemmMultipleD_BlockScale_BPreshuffle<ALayout,
|
||||
BLayout,
|
||||
ck::Tuple<>,
|
||||
ELayout,
|
||||
A0DataType,
|
||||
A1DataType,
|
||||
B0DataType,
|
||||
B1DataType,
|
||||
ck::Tuple<>,
|
||||
EDataType,
|
||||
ScaleBlockM,
|
||||
ScaleBlockN,
|
||||
ScaleBlockK,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
|
||||
// get device op instances
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
||||
|
||||
// Run reference GEMM
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<AccDataType> c_m_n({M, N});
|
||||
Tensor<float> a_m_k({M, K});
|
||||
Tensor<float> b_k_n({K, N});
|
||||
|
||||
for(int m = 0; m < M; m++)
|
||||
{
|
||||
for(int k = 0; k < K; k++)
|
||||
{
|
||||
a_m_k(m, k) = ck::type_convert<float>(a0_m_k(m, k)) *
|
||||
a1_m_k(m / ScaleBlockM, k / ScaleBlockK);
|
||||
}
|
||||
}
|
||||
|
||||
for(int n = 0; n < N; n++)
|
||||
{
|
||||
for(int k = 0; k < K; k++)
|
||||
{
|
||||
b_k_n(k, n) = ck::type_convert<float>(b0_k_n(k, n)) *
|
||||
b1_k_n(k / ScaleBlockK, n / ScaleBlockN);
|
||||
}
|
||||
}
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<float,
|
||||
float,
|
||||
AccDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough,
|
||||
float>;
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
e_m_n_host_result(m, n) = ck::type_convert<EDataType>(c_m_n(m, n));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::string best_op_name;
|
||||
float best_ave_time = 0;
|
||||
float best_tflops = 0;
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
// profile device GEMM instances
|
||||
for(auto& op_ptr : op_ptrs)
|
||||
{
|
||||
int NPerXdl = op_ptr->GetPreShuffleParameters();
|
||||
|
||||
auto argument_ptr = op_ptr->MakeArgumentPointer(
|
||||
static_cast<A0DataType*>(a0_device_buf.GetDeviceBuffer()),
|
||||
static_cast<B0DataType*>(NPerXdl == 16 ? b_device_buf_mfma16.GetDeviceBuffer()
|
||||
: b_device_buf_mfma32.GetDeviceBuffer()),
|
||||
std::array<const void*, 0>{},
|
||||
static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
std::array<ck::index_t, 0>{},
|
||||
StrideE,
|
||||
a1_device_buf.GetDeviceBuffer(),
|
||||
b1_device_buf.GetDeviceBuffer(),
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op);
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
|
||||
// re-init C to zero before profiling next kernel
|
||||
c_device_buf.SetZero();
|
||||
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false, 0, n_warmup, n_iter});
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
c_device_buf.FromDevice(e_m_n_device_result.mData.data());
|
||||
|
||||
#if defined CK_ENABLE_FP8
|
||||
// set softer tolerances for fp8
|
||||
if constexpr(is_same_v<A0DataType, f8_t> || is_same_v<B0DataType, f8_t> ||
|
||||
is_same_v<EDataType, f8_t>)
|
||||
{
|
||||
std::string msg = "Error: Incorrect results!";
|
||||
double rtol = 5e-2;
|
||||
double atol = 5e-2;
|
||||
pass = pass & ck::utils::check_err(
|
||||
e_m_n_device_result, e_m_n_host_result, msg, rtol, atol);
|
||||
}
|
||||
else
|
||||
{
|
||||
#endif
|
||||
pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
|
||||
#if defined CK_ENABLE_FP8
|
||||
}
|
||||
#endif
|
||||
|
||||
if(do_log)
|
||||
{
|
||||
LogRangeAsType<float>(std::cout << "a : ", a0_m_k.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "b: ", b0_k_n.mData, ",") << std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",")
|
||||
<< std::endl;
|
||||
LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",")
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
float ave_time = invoker_ptr->Run(
|
||||
argument_ptr.get(),
|
||||
StreamConfig{
|
||||
nullptr, time_kernel, 0, n_warmup, n_iter, rotating_count > 1, rotating_count});
|
||||
|
||||
std::size_t flop = std::size_t(2) * M * N * K;
|
||||
|
||||
std::size_t num_btype =
|
||||
sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * M * N;
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_op_name = op_name;
|
||||
best_tflops = tflops;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(is_same<EDataType, float>::value)
|
||||
{
|
||||
std::cout << "Best Perf for datatype = f32";
|
||||
}
|
||||
else if constexpr(is_same<EDataType, half_t>::value)
|
||||
{
|
||||
std::cout << "Best Perf for datatype = f16";
|
||||
}
|
||||
else if constexpr(is_same<EDataType, bhalf_t>::value)
|
||||
{
|
||||
std::cout << "Best Perf for datatype = bf16";
|
||||
}
|
||||
else if constexpr(is_same<EDataType, int8_t>::value)
|
||||
{
|
||||
std::cout << "Best Perf for datatype = int8";
|
||||
}
|
||||
|
||||
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
std::cout << " ALayout = RowMajor";
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
|
||||
{
|
||||
std::cout << " ALayout = ColumnMajor";
|
||||
}
|
||||
|
||||
if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
std::cout << " BLayout = RowMajor";
|
||||
}
|
||||
else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
|
||||
{
|
||||
std::cout << " BLayout = ColumnMajor";
|
||||
}
|
||||
|
||||
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
|
||||
<< " StrideB = " << StrideB << " StrideE = " << StrideE << " : " << best_ave_time
|
||||
<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_op_name << std::endl;
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
@@ -51,7 +51,8 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9")
|
||||
# if(SUPPORTED_GPU_TARGETS MATCHES "gfx94")
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_multiply_multiply.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_multiply_multiply_weight_preshuffle.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_ab_scale.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_gemm_ab_scale.cpp)
|
||||
list(APPEND PROFILER_SOURCES profile_gemm_blockscale_wp.cpp)
|
||||
# endif()
|
||||
# list(APPEND PROFILER_SOURCES profile_batched_gemm.cpp)
|
||||
# list(APPEND PROFILER_SOURCES profile_batched_gemm_reduce.cpp)
|
||||
@@ -141,7 +142,8 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9")
|
||||
# if(SUPPORTED_GPU_TARGETS MATCHES "gfx94")
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_multiply_multiply_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_multiply_multiply_weight_preshuffle_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_ab_scale_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_ab_scale_instance)
|
||||
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_blockscale_wp_instance)
|
||||
# endif()
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance)
|
||||
# target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_instance)
|
||||
|
||||
184
profiler/src/profile_gemm_blockscale_wp.cpp
Normal file
184
profiler/src/profile_gemm_blockscale_wp.cpp
Normal file
@@ -0,0 +1,184 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
#include <numeric>
|
||||
#include <initializer_list>
|
||||
#include <cstdlib>
|
||||
|
||||
#include "profiler/profile_gemm_blockscale_wp_impl.hpp"
|
||||
#include "profiler_operation_registry.hpp"
|
||||
|
||||
enum struct GemmMatrixLayout
|
||||
{
|
||||
MK_KN_MN, // 0
|
||||
MK_NK_MN, // 1
|
||||
KM_KN_MN, // 2
|
||||
KM_NK_MN, // 3
|
||||
};
|
||||
|
||||
enum struct GemmDataType
|
||||
{
|
||||
F32_F32_F32, // 0
|
||||
F16_F16_F16, // 1
|
||||
BF16_BF16_BF16, // 2
|
||||
INT8_INT8_INT8, // 3
|
||||
F8_F16_F16, // 4
|
||||
F16_F8_F16, // 5
|
||||
F16_F16_F16_F8, // 6
|
||||
F8_F8_BF16, // 7
|
||||
};
|
||||
|
||||
enum struct ScaleBlockTile
|
||||
{
|
||||
Tile_128_128_128, // 0
|
||||
Tile_1_128_128, // 1
|
||||
};
|
||||
|
||||
#define OP_NAME "gemm_blockscale_weighpreshuffle"
|
||||
#define OP_DESC "GEMM_BlockScale_WeightPreshuffle"
|
||||
|
||||
int profile_gemm_blockscale_weighpreshuffle(int argc, char* argv[])
|
||||
{
|
||||
if(argc != 15 && argc != 18)
|
||||
{
|
||||
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
|
||||
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
|
||||
"f16->f8; 7: f8->bf16, "
|
||||
"comp f8)\n");
|
||||
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
|
||||
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
|
||||
printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
|
||||
printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
|
||||
printf("arg4: scale block tile (0: ScaleBlockM/N/K = [128, 128, 128]; 1: ScaleBlockM/N/K = "
|
||||
"[1, 128, 128];\n");
|
||||
printf("arg5: verification (0: no; 1: yes)\n");
|
||||
printf("arg6: initialization (0: no init; 1: integer value; 2: decimal value)\n");
|
||||
printf("arg7: print tensor value (0: no; 1: yes)\n");
|
||||
printf("arg8: time kernel (0=no, 1=yes)\n");
|
||||
printf("arg9 to 14: M, N, K, StrideA, StrideB, StrideE\n");
|
||||
printf("optional:\n");
|
||||
printf("arg15: number of warm-up cycles (default 1)\n");
|
||||
printf("arg16: number of iterations (default 10)\n");
|
||||
printf("arg17: memory for rotating buffer (default 0, size in MB)\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
|
||||
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
|
||||
const auto scale_block_tile = static_cast<ScaleBlockTile>(std::stoi(argv[4]));
|
||||
const bool do_verification = std::stoi(argv[5]);
|
||||
const int init_method = std::stoi(argv[6]);
|
||||
const bool do_log = std::stoi(argv[7]);
|
||||
const bool time_kernel = std::stoi(argv[8]);
|
||||
|
||||
const int M = std::stoi(argv[9]);
|
||||
const int N = std::stoi(argv[10]);
|
||||
const int K = std::stoi(argv[11]);
|
||||
|
||||
const int StrideA = std::stoi(argv[12]);
|
||||
const int StrideB = std::stoi(argv[13]);
|
||||
const int StrideE = std::stoi(argv[14]);
|
||||
|
||||
int n_warmup = 1;
|
||||
int n_iter = 10;
|
||||
uint64_t rotating = 0;
|
||||
if(argc == 18)
|
||||
{
|
||||
n_warmup = std::stoi(argv[15]);
|
||||
n_iter = std::stoi(argv[16]);
|
||||
rotating = std::stoull(argv[17]) * 1024 * 1024;
|
||||
}
|
||||
|
||||
using F32 = float;
|
||||
using BF16 = ck::bhalf_t;
|
||||
using F8 = ck::f8_t;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
auto profile = [&](auto a0_type,
|
||||
auto a1_type,
|
||||
auto b0_type,
|
||||
auto b1_type,
|
||||
auto comp_type,
|
||||
auto acc_type,
|
||||
auto c_type,
|
||||
auto scale_block_m,
|
||||
auto scale_block_n,
|
||||
auto scale_block_k,
|
||||
auto a_layout,
|
||||
auto b_layout,
|
||||
auto e_layout) {
|
||||
using A0DataType = decltype(a0_type);
|
||||
using A1DataType = decltype(a1_type);
|
||||
using B0DataType = decltype(b0_type);
|
||||
using B1DataType = decltype(b1_type);
|
||||
using ComputeDataType = decltype(comp_type);
|
||||
using AccDataType = decltype(acc_type);
|
||||
using EDataType = decltype(c_type);
|
||||
|
||||
using ALayout = decltype(a_layout);
|
||||
using BLayout = decltype(b_layout);
|
||||
using ELayout = decltype(e_layout);
|
||||
|
||||
const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
|
||||
const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
|
||||
const int DefaultStrideE = ck::is_same_v<ELayout, Row> ? N : M;
|
||||
|
||||
bool pass = ck::profiler::profile_gemm_blockscale_weighpreshuffle_impl<A0DataType,
|
||||
A1DataType,
|
||||
B0DataType,
|
||||
B1DataType,
|
||||
ComputeDataType,
|
||||
AccDataType,
|
||||
EDataType,
|
||||
scale_block_m,
|
||||
scale_block_n,
|
||||
scale_block_k,
|
||||
ALayout,
|
||||
BLayout,
|
||||
ELayout>(
|
||||
do_verification,
|
||||
init_method,
|
||||
do_log,
|
||||
time_kernel,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
(StrideA < 0) ? DefaultStrideA : StrideA,
|
||||
(StrideB < 0) ? DefaultStrideB : StrideB,
|
||||
(StrideE < 0) ? DefaultStrideE : StrideE,
|
||||
n_warmup,
|
||||
n_iter,
|
||||
rotating);
|
||||
|
||||
return pass ? 0 : 1;
|
||||
};
|
||||
|
||||
if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN &&
|
||||
scale_block_tile == ScaleBlockTile::Tile_1_128_128)
|
||||
{
|
||||
return profile(F8{},
|
||||
F32{},
|
||||
F8{},
|
||||
F32{},
|
||||
F8{},
|
||||
F32{},
|
||||
BF16{},
|
||||
ck::Number<1>{},
|
||||
ck::Number<128>{},
|
||||
ck::Number<128>{},
|
||||
Row{},
|
||||
Col{},
|
||||
Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "this data_type & layout is not implemented" << std::endl;
|
||||
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_blockscale_weighpreshuffle);
|
||||
Reference in New Issue
Block a user