mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 10:09:41 +00:00
Support b_scale: (#2350)
- extend pipeline v1 and v3
- add instances
- add tests
- add example
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
[ROCm/composable_kernel commit: b01a27ff22]
This commit is contained in:
Enrico Degregori
@@ -128,3 +128,5 @@ add_example_executable(example_gemm_wmma_fp16_pk_i4_v3 gemm_wmma_fp16_pk_i4_v3.c
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add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16_pk_i4_v3)
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add_example_executable(example_gemm_wmma_fp16_fp8_v3 gemm_wmma_fp16_fp8_v3.cpp)
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add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16_fp8_v3)
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add_example_executable(example_gemm_wmma_fp16_pk_i4_v3_b_scale gemm_wmma_fp16_pk_i4_v3_b_scale.cpp)
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add_example_dependencies(example_gemm_wmma example_gemm_wmma_fp16_pk_i4_v3_b_scale)
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367
example/01_gemm/gemm_wmma_fp16_pk_i4_v3_b_scale.cpp
Normal file
367
example/01_gemm/gemm_wmma_fp16_pk_i4_v3_b_scale.cpp
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@@ -0,0 +1,367 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#include "common.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_wmma_cshuffle_v3_b_scale.hpp"
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using ADataType = ck::half_t;
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using BDataType = ck::pk_i4_t;
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using BScaleDataType = ck::half_t;
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using AccDataType = float;
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using CShuffleDataType = ck::half_t;
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using CDataType = ck::half_t;
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using ALayout = Row;
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using BLayout = Col;
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using CLayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CElementOp = PassThrough;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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static constexpr bool PermuteA = false;
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static constexpr bool PermuteB = true;
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static constexpr ck::index_t Scale_Block_N = 1;
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static constexpr ck::index_t Scale_Block_K = 128;
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static constexpr ck::index_t KPerBlock = 64;
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// clang-format off
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using DeviceGemmV2Instance =
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ck::tensor_operation::device::DeviceGemm_BScale_Wmma_CShuffleV3<
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ALayout, BLayout, CLayout,
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ADataType, BDataType, BScaleDataType, CDataType, AccDataType, CShuffleDataType,
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AElementOp, BElementOp, CElementOp, GemmDefault,
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256, Scale_Block_N, Scale_Block_K,
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128, 128,
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KPerBlock, 8, 8,
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16, 16,
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4, 2,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
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2, 8, 8, 0,
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S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
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2, 8, 8, 0,
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1, 1, S<1, 32, 1, 8>, 8,
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ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3,
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CDataType, CDataType, PermuteA, PermuteB>;
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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AccDataType,
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CDataType,
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AccDataType,
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PassThrough,
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PassThrough,
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PassThrough>;
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template <typename ProblemType>
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bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
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{
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using namespace ck::literals;
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auto M = problem_size.M;
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auto N = problem_size.N;
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auto K = problem_size.K;
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auto StrideA = problem_size.StrideA;
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auto StrideB = problem_size.StrideB;
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auto StrideC = problem_size.StrideC;
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auto KBatch = problem_size.KBatch;
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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{
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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auto f_get_default_stride =
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[](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
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if(stride == -1)
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{
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// give a chance if stride is -1, return a default packed stride
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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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{
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return static_cast<std::size_t>(col);
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}
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else
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{
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return static_cast<std::size_t>(row);
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}
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}
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else
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return static_cast<std::size_t>(stride);
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};
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ck::index_t Scale_Stride_BN = (K + Scale_Block_K - 1) / Scale_Block_K;
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StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
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StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
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StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<BDataType> b_k_n_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<BScaleDataType> b1_k_n(f_host_tensor_descriptor((K + Scale_Block_K - 1) / Scale_Block_K,
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(N + Scale_Block_N - 1) / Scale_Block_N,
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Scale_Stride_BN,
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BLayout{}));
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switch(config.init_method)
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{
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case 0:
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a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
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b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
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b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
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break;
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case 1:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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b1_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
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break;
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case 2:
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a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
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break;
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case 3:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
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b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
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break;
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case 4:
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a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
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b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
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b1_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
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break;
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case 5:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
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break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.5, 0.5});
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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b1_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
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}
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
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DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize() / 2);
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DeviceMem b1_scale_device_buf(sizeof(BScaleDataType) * b1_k_n.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
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// weight permute
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if constexpr(PermuteB)
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{
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int K1 = KPerBlock;
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int K0 = K / KPerBlock;
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// int K0, N, K1
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for(int j = 0; j < K0; j++)
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{
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for(int i = 0; i < N; i++)
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{
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for(int jj = 0; jj < K1; jj++)
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{
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b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
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}
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}
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}
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}
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else
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{
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for(int i = 0; i < N; i++)
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{
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for(int j = 0; j < K; j++)
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{
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b_k_n_permute(i * K + j) = b_k_n(i * K + j);
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}
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}
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}
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// vector pk_i4x4 permute
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for(int i = 0; i < N; i++)
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{
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for(int j = 0; j < K; j += 8)
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{
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int input[8];
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for(int k = 0; k < 4; k++)
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{
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int i4x2 = b_k_n_permute(j + k * 2, i).data;
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input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
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input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
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}
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// permute 01234567->20643175
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{
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int hi = input[2];
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int lo = input[0];
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int i4x2 = (hi << 4) | lo;
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b_k_n_permute(j + 0, i) = i4x2;
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}
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{
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int hi = input[6];
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int lo = input[4];
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int i4x2 = (hi << 4) | lo;
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b_k_n_permute(j + 2, i) = i4x2;
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}
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{
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int hi = input[3];
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int lo = input[1];
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int i4x2 = (hi << 4) | lo;
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b_k_n_permute(j + 4, i) = i4x2;
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}
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{
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int hi = input[7];
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int lo = input[5];
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int i4x2 = (hi << 4) | lo;
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b_k_n_permute(j + 6, i) = i4x2;
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}
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}
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}
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a_m_k_device_buf.ToDevice(a_m_k.mData.data());
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b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
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b1_scale_device_buf.ToDevice(b1_k_n.mData.data());
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DeviceMem workspace;
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto c_element_op = CElementOp{};
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// do GEMM
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auto gemm = DeviceGemmV2Instance{};
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auto invoker = gemm.MakeInvoker();
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float ave_time = 0;
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auto argument =
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gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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Scale_Stride_BN,
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static_cast<BScaleDataType*>(b1_scale_device_buf.GetDeviceBuffer()),
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KBatch,
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a_element_op,
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b_element_op,
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c_element_op);
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if(!gemm.IsSupportedArgument(argument))
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{
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std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
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return true;
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}
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std::string device_name = ck::get_device_name();
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if(!(device_name.find("gfx11") != std::string::npos ||
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device_name.find("gfx12") != std::string::npos))
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{
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std::cout << "This kernel support gfx1100 and gfx1200 only" << std::endl;
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return true;
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}
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bool pass = true;
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if(config.do_verification)
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{
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Tensor<float> b_k_n_dequant({K, N});
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float v_b = 0;
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for(int n = 0; n < N; n++)
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{
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for(int k = 0; k < K; k++)
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{
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ck::pk_i4_t i4x2 = b_k_n(k, n).data;
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int8_t i4 = 0;
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if(k % 2 == 1)
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i4 = (i4x2.data >> 0) & 0xf;
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else
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i4 = (i4x2.data >> 4) & 0xf;
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i4 = i4 - 8;
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v_b = ck::type_convert<float>(i4);
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b_k_n_dequant(k, n) =
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ck::type_convert<float>(v_b) *
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ck::type_convert<float>(b1_k_n(k / Scale_Block_K, n / Scale_Block_N));
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}
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}
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(
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a_m_k, b_k_n_dequant, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
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ref_invoker.Run(ref_argument);
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ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
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c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
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pass &= ck::utils::check_err(c_m_n_device_result,
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c_m_n_host_result,
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"Error: Incorrect results!",
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get_rtol<CDataType>(),
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get_atol<CDataType>());
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}
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if(config.time_kernel)
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{
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ave_time =
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invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
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std::size_t flop = 2_uz * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K +
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sizeof(BDataType) * K * N /
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(ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
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sizeof(CDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
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<< " GB/s, " << gemm.GetTypeString() << std::endl;
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}
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return pass;
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}
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bool run_gemm_splitk_example(int argc, char* argv[])
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{
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ProblemSizeSplitK problem_size;
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ExecutionConfig config;
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return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
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}
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int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
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