mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-06-30 11:47:48 +00:00
update
This commit is contained in:
yadai
@@ -564,7 +564,7 @@ include_directories(BEFORE
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${HIP_INCLUDE_DIRS}
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)
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SET(BUILD_DEV ON CACHE BOOL "BUILD_DEV")
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SET(BUILD_DEV OFF CACHE BOOL "BUILD_DEV")
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if(BUILD_DEV)
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add_compile_options(-Werror)
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add_compile_options(-Weverything)
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@@ -13,6 +13,56 @@
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#include "flatmm_basic.hpp"
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#include <type_traits>
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "256", "m dimension")
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.insert("n", "256", "n dimension")
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.insert("k", "128", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "C", "B tensor data layout - Row by default")
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.insert("c_layout", "R", "C tensor data layout - Row by default")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("prec", "fp8", "data type. fp16/bf16/fp8/bf8")
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.insert("wave_tile", "16", "only support 16(16x16) or 32(32x32)")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("split_k", "1", "splitK value")
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.insert("init", "0", "0:random, 1:linear, 2:constant(1)")
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.insert("scale", "0", "0:without scale, 1:per-token/channel scale, only for fp8/bf8")
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.insert("persistent", "0", "0: no persistent, 1: persistent kernel")
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.insert("warp_tile",
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"0",
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"0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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template <typename FlatmmConfig, typename T>
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auto shuffle_b_v1(const ck_tile::HostTensor<T>& t)
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{
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assert(t.get_lengths().size() == 2);
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int n_ = t.get_lengths()[1];
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int k_ = t.get_lengths()[0];
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constexpr int divisor = FlatmmConfig::N_Warp_Tile == 32 ? 2 : 4;
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constexpr int NRepeat = FlatmmConfig::N_Tile / FlatmmConfig::N_Warp_Tile / FlatmmConfig::N_Warp;
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ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Tile,
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FlatmmConfig::N_Warp,
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FlatmmConfig::N_Warp_Tile,
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NRepeat,
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k_ / FlatmmConfig::K_Warp_Tile,
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divisor,
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FlatmmConfig::K_Warp_Tile / divisor});
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std::copy(t.begin(), t.end(), t_view.begin());
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return ck_tile::reference_permute(t_view, {0, 3, 1, 4, 5, 2, 6});
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}
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#include "run_flatmm_example.inc"
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/*
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template <typename T>
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constexpr const char* DataTypeToString()
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{
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@@ -62,25 +112,6 @@ auto shuffle_b(const ck_tile::HostTensor<T>& t)
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return ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
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}
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template <typename FlatmmConfig, typename T>
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auto shuffle_b_v1(const ck_tile::HostTensor<T>& t)
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{
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assert(t.get_lengths().size() == 2);
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int n_ = t.get_lengths()[1];
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int k_ = t.get_lengths()[0];
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constexpr int divisor = FlatmmConfig::N_Warp_Tile == 32 ? 2 : 4;
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constexpr int NRepeat = FlatmmConfig::N_Tile / FlatmmConfig::N_Warp_Tile / FlatmmConfig::N_Warp;
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ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Tile,
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FlatmmConfig::N_Warp,
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FlatmmConfig::N_Warp_Tile,
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NRepeat,
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k_ / FlatmmConfig::K_Warp_Tile,
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divisor,
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FlatmmConfig::K_Warp_Tile / divisor});
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std::copy(t.begin(), t.end(), t_view.begin());
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return ck_tile::reference_permute(t_view, {0, 3, 1, 4, 5, 2, 6});
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}
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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auto calculate_rtol_atol(const ck_tile::index_t K,
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const ck_tile::index_t kbatch,
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@@ -101,6 +132,7 @@ auto calculate_rtol_atol(const ck_tile::index_t K,
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// Use higher threshold
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return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
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}
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*/
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template <typename FlatmmConfig,
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typename ADataType,
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@@ -299,6 +331,7 @@ float flatmm_calc(const ck_tile::ScaleFlatmmHostArgs<ScaleM, ScaleN>& args,
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return ave_time;
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}
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/*
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template <typename FlatmmConfig,
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typename ADataType,
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typename BDataType,
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@@ -372,37 +405,8 @@ float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
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return ave_time;
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}
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*/
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auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "256", "m dimension")
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.insert("n", "256", "n dimension")
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.insert("k", "128", "k dimension")
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.insert("a_layout", "R", "A tensor data layout - Row by default")
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.insert("b_layout", "C", "B tensor data layout - Row by default")
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.insert("c_layout", "R", "C tensor data layout - Row by default")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("prec", "fp8", "data type. fp16/bf16/fp8/bf8")
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.insert("wave_tile", "16", "only support 16(16x16) or 32(32x32)")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("split_k", "1", "splitK value")
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.insert("init", "0", "0:random, 1:linear, 2:constant(1)")
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.insert("scale", "0", "0:without scale, 1:per-token/channel scale, only for fp8/bf8")
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.insert("persistent", "0", "0: no persistent, 1: persistent kernel")
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.insert("warp_tile",
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"0",
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"0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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}
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#include "run_flatmm_example.inc"
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template <template <typename PreType> typename FlatmmConfig>
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int run_flatmm_example(int argc, char* argv[])
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@@ -97,7 +97,10 @@ template <typename FlatmmConfig,
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typename BLayout,
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typename DsLayout,
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typename CLayout,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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typename ScaleM,
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typename ScaleN,
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bool UsePersistentKernel = false,
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typename CDEElementWise = ck_tile::element_wise::PassThrough>
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float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
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ck_tile::DeviceMem& b_shuffle_dev_buf,
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ck_tile::DeviceMem& c_dev_buf,
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@@ -108,21 +111,25 @@ float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
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ck_tile::index_t stride_B,
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ck_tile::index_t stride_C,
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ck_tile::index_t kbatch,
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ScaleM scale_m,
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ScaleN scale_n,
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int n_warmup,
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int n_repeat)
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{
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ck_tile::FlatmmHostArgs<> args = {a_dev_buf.GetDeviceBuffer(),
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b_shuffle_dev_buf.GetDeviceBuffer(),
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{},
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c_dev_buf.GetDeviceBuffer(),
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kbatch,
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M,
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N,
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K,
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stride_A,
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stride_B,
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{},
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stride_C};
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ck_tile::ScaleFlatmmHostArgs<ScaleM, ScaleN> args = {a_dev_buf.GetDeviceBuffer(),
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b_shuffle_dev_buf.GetDeviceBuffer(),
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{},
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c_dev_buf.GetDeviceBuffer(),
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kbatch,
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M,
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N,
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K,
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stride_A,
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stride_B,
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{},
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stride_C,
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scale_m,
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scale_n};
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float ave_time = flatmm_calc<FlatmmConfig,
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ADataType,
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@@ -134,7 +141,9 @@ float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
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BLayout,
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DsLayout,
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CLayout,
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false,
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ScaleM,
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ScaleN,
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UsePersistentKernel,
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CDEElementWise>(
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args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
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@@ -152,6 +161,7 @@ float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
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return ave_time;
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}
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template <typename PrecType,
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typename FlatmmConfig,
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int ScaleGranularityM = -1,
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