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[CK TILE ENGINE] Adding GEMM Preshuffle to CK Tile Engine (#2712)
* Partial Progress : Completed ListBlob * Additional changes in Listbob * Partial Progress : Generate Blobs Completed * Partial Progress : Added Host side code for Preshuffle * Working code for Preshuffle before Cleanup * Partial Progress : Cleanup * Partial Progress : Datatype Validation * Partial Progress : Warptiles for preshuffle changed from hardcoding to take from config * Partial Progress : Cleanup * Partial Progress : Code Cleanup * Partial Progress : Passing all valid tiles failing for unsupported tiles * Partial Progress : Working code, testing pending for edge cases * Partial Progress for testing * Completed Code * kBlockPerCu as tunable parameter from config * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Update tile_engine/ops/gemm_preshuffle/README.md Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com> * Partial Progress : Working listkernels * Partial Progress : Cleanup Working listkernels * Partial Progress : Single instance * Partial Progress : Working single instance code * Partial Progress : Working generate individual instance code * Partial Progress : Working rewamped code for given config file needed validation and edge case testing * Partial Progress : Working Code, testing pending * Removing LOGS file * Working code * Minor changes to GEMM Preshuffle : Restructured * Minor Changes in Preshuffle * Changes to Jenkins File * Changes to Jenkins file to consider new architecture * Changes to Jenkins file for fixing CI --------- Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>
This commit is contained in:
Thrupti Raj Lakshmana Gowda
committed by
GitHub
GitHub
parent
1894a0dbc3
commit
f6ba94fb5c
275
tile_engine/ops/gemm_preshuffle/gemm_preshuffle_profiler.hpp
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275
tile_engine/ops/gemm_preshuffle/gemm_preshuffle_profiler.hpp
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#pragma once
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#include "ck_tile/host/device_prop.hpp"
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#include "ck_tile/ops/gemm.hpp"
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#include "benchmark_gemm_preshuffle.hpp"
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class GemmProfiler
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{
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public:
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static GemmProfiler& instance(Setting setting)
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{
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static GemmProfiler instance{setting};
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return instance;
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}
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// Overload for single kernel benchmarking
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void benchmark(GemmProblem& gemm_problem,
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std::function<float(const ck_tile::GemmHostArgs&, const ck_tile::stream_config&)>
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kernel_func,
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const std::tuple<int, int, int>& warp_tile_dims)
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{
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// Create a vector with a single callable that returns both name and time
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std::vector<std::function<std::tuple<std::string, float>(ck_tile::GemmHostArgs&,
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const ck_tile::stream_config&)>>
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callables;
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callables.push_back(
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[kernel_func](ck_tile::GemmHostArgs& args, const ck_tile::stream_config& stream) {
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float time = kernel_func(args, stream);
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return std::make_tuple(std::string(KERNEL_NAME), time);
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});
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benchmark(gemm_problem, callables, warp_tile_dims);
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}
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void benchmark(GemmProblem& gemm_problem,
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std::vector<std::function<std::tuple<std::string, float>(
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ck_tile::GemmHostArgs&, const ck_tile::stream_config&)>>& callables,
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const std::tuple<int, int, int>& warp_tile_dims)
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{
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const ALayout layout_a = ALayout{};
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const BLayout layout_b = BLayout{};
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const CLayout layout_c = CLayout{};
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gemm_problem.stride_a_ = ck_tile::get_default_stride(
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gemm_problem.m_, gemm_problem.k_, gemm_problem.stride_a_, is_row_major(layout_a));
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gemm_problem.stride_b_ = ck_tile::get_default_stride(
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gemm_problem.k_, gemm_problem.n_, gemm_problem.stride_b_, is_row_major(layout_b));
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gemm_problem.stride_c_ = ck_tile::get_default_stride(
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gemm_problem.m_, gemm_problem.n_, gemm_problem.stride_c_, is_row_major(layout_c));
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ck_tile::HostTensor<ADataType> a_m_k(ck_tile::host_tensor_descriptor(
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gemm_problem.m_, gemm_problem.k_, gemm_problem.stride_a_, is_row_major(layout_a)));
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ck_tile::HostTensor<BDataType> b_k_n(ck_tile::host_tensor_descriptor(
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gemm_problem.k_, gemm_problem.n_, gemm_problem.stride_b_, is_row_major(layout_b)));
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ck_tile::HostTensor<CDataType> c_m_n_dev_result(ck_tile::host_tensor_descriptor(
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gemm_problem.m_, gemm_problem.n_, gemm_problem.stride_c_, is_row_major(layout_c)));
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if(setting_.init_method_ == 0)
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{
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ck_tile::FillUniformDistribution<ADataType>{-.5f, .5f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataType>{-.5f, .5f}(b_k_n);
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}
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else if(setting_.init_method_ == 1)
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{
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ck_tile::FillMonotonicSeq<ADataType>{}(a_m_k);
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ck_tile::FillMonotonicSeq<BDataType>{}(b_k_n);
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}
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else if(setting_.init_method_ == 2)
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{
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ck_tile::FillUniformDistribution<ADataType>{1.f, 1.f}(a_m_k);
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ck_tile::FillUniformDistribution<BDataType>{1.f, 1.f}(b_k_n);
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}
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else
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{
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a_m_k.SetZero();
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b_k_n.SetZero();
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}
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ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
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ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
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ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
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// Reference Verification
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ck_tile::HostTensor<CDataType> c_m_n_ref(ck_tile::host_tensor_descriptor(
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gemm_problem.m_, gemm_problem.n_, gemm_problem.stride_c_, is_row_major(layout_c)));
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c_m_n_ref.SetZero();
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if(setting_.verify_)
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{
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gemm_host_reference(setting_.verify_,
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a_m_k,
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b_k_n,
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c_m_n_ref,
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a_m_k_dev_buf,
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b_k_n_dev_buf,
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gemm_problem.m_,
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gemm_problem.n_,
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gemm_problem.k_,
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gemm_problem.stride_a_,
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gemm_problem.stride_b_,
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gemm_problem.stride_c_);
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}
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// Kerenl Execution
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a_m_k_dev_buf.ToDevice(a_m_k.data());
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c_m_n_dev_buf.SetZero();
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c_m_n_dev_result.SetZero();
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for(const auto& callable : callables)
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{
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ck_tile::index_t N_Warp_Tile = std::get<1>(warp_tile_dims);
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ck_tile::index_t K_Warp_Tile = std::get<2>(warp_tile_dims);
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ck_tile::HostTensor<BDataType> b_shuffle_host =
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shuffle_b(b_k_n, N_Warp_Tile, K_Warp_Tile);
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b_k_n_dev_buf.ToDevice(b_shuffle_host.data());
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ck_tile::GemmHostArgs gemm_args = {
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a_m_k_dev_buf.GetDeviceBuffer(),
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b_k_n_dev_buf.GetDeviceBuffer(),
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c_m_n_dev_buf.GetDeviceBuffer(),
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gemm_problem.split_k_,
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gemm_problem.m_,
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gemm_problem.n_,
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gemm_problem.k_,
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gemm_problem.stride_a_,
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gemm_problem.stride_b_,
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gemm_problem.stride_c_,
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};
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auto kernel_run_result = callable(gemm_args,
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ck_tile::stream_config{nullptr,
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true,
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setting_.log_,
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setting_.n_warmup_,
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setting_.n_repeat_,
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setting_.is_gpu_timer_,
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setting_.flush_cache_,
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setting_.rotating_count_});
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process_result(
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gemm_problem, c_m_n_dev_buf, c_m_n_ref, c_m_n_dev_result, kernel_run_result);
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}
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}
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void process_result(const GemmProblem& gemm_problem,
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ck_tile::DeviceMem& c_m_n_dev_buf,
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ck_tile::HostTensor<CDataType>& c_m_n_ref,
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ck_tile::HostTensor<CDataType>& c_m_n_dev_result,
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const std::tuple<std::string, float>& kernel_run_result)
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{
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auto [name, avg_time] = kernel_run_result;
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KernelInstance kernel_instance{name, gemm_problem, {-1.0f, -1.0f, -1.0f}};
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// compute performance metric
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std::size_t flop = std::size_t(2) * gemm_problem.m_ * gemm_problem.n_ * gemm_problem.k_;
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std::size_t num_byte = sizeof(ADataType) * gemm_problem.m_ * gemm_problem.k_ +
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sizeof(BDataType) * gemm_problem.n_ * gemm_problem.k_ +
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sizeof(CDataType) * gemm_problem.m_ * gemm_problem.n_;
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// update
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kernel_instance.perf_result_.latency_ = avg_time;
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kernel_instance.perf_result_.tflops_ = static_cast<float>(flop) / 1.E9 / avg_time;
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kernel_instance.perf_result_.bandwidth_ = num_byte / 1.E6 / avg_time;
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if(setting_.log_ > 0 && !setting_.json_output_)
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{
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std::cout << kernel_instance << std::endl;
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}
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// verify result
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c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
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bool verified_correct =
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!setting_.verify_ ||
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compare(name, gemm_problem.k_, gemm_problem.split_k_, c_m_n_dev_result, c_m_n_ref);
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if(verified_correct)
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{
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kernel_instances_.emplace_back(kernel_instance);
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}
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else
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{
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std::cout << "Verification failed, skip kernel: " << name << std::endl;
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}
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// clear tensor
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c_m_n_dev_buf.SetZero();
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c_m_n_dev_result.SetZero();
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}
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KernelInstance select_best_instance(Metric metric)
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{
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if(kernel_instances_.empty())
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throw std::runtime_error("Empty instances");
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auto kernel_instance = *std::max_element(kernel_instances_.begin(),
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kernel_instances_.end(),
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[metric](const auto& a, const auto& b) {
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return PerformanceResult::compare(
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b.perf_result_, a.perf_result_, metric);
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});
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if(setting_.json_output_)
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{
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// Output clean JSON only
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std::cout << kernel_instance << std::endl;
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}
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else
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{
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std::cout << "**********************************" << std::endl;
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std::cout << "According to given metrics: " << get_metric_name(metric) << "\n"
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<< "Current kernel performance is: " << kernel_instance << std::endl;
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std::cout << "**********************************" << std::endl;
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}
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if(!setting_.csv_filename_.empty())
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{
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std::ofstream file(setting_.csv_filename_ + ".csv", std::ios::app);
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if(!file.is_open())
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{
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std::cerr << "Warning: Failed to open CSV file for writing." << std::endl;
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}
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else
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{
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if(file.tellp() == 0)
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{
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file << "rocm_version,device_name,"
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<< "split_k,m,n,k,stride_a,stride_b,stride_c,"
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<< "dtype_a,dtype_b,dtype_acc,dtype_c," << "layout_a,layout_b,layout_c,"
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<< "structured_sparsity," << "name,"
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<< "latency(ms),tflops(TFlops),bandwidth(GB/s),metric\n";
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}
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const auto& problem = kernel_instance.problem_;
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const auto& name = kernel_instance.name_;
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const auto& perf = kernel_instance.perf_result_;
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file << get_rocm_version() << "," << ck_tile::get_device_name() << ","
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<< problem.split_k_ << "," << problem.m_ << "," << problem.n_ << ","
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<< problem.k_ << "," << problem.stride_a_ << "," << problem.stride_b_ << ","
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<< problem.stride_c_ << "," << problem.dtype_a_ << "," << problem.dtype_b_
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<< "," << problem.dtype_acc_ << "," << problem.dtype_c_ << ","
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<< problem.layout_a_ << "," << problem.layout_b_ << "," << problem.layout_c_
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<< "," << problem.structured_sparsity_ << "," << name << "," << std::fixed
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<< std::setprecision(4) << perf.latency_ << "," << std::fixed
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<< std::setprecision(4) << perf.tflops_ << "," << std::fixed
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<< std::setprecision(4) << perf.bandwidth_ << "," << get_metric_name(metric)
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<< "\n";
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if(!file)
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{
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std::cerr << "Warning: Error occurred while writing to CSV file." << std::endl;
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}
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}
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}
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return kernel_instance;
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}
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GemmProfiler(const GemmProfiler&) = delete;
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GemmProfiler& operator=(const GemmProfiler&) = delete;
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private:
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~GemmProfiler() { kernel_instances_.clear(); }
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GemmProfiler(Setting setting) : setting_(setting) {}
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Setting setting_;
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std::vector<KernelInstance> kernel_instances_;
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};
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