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Commit f3ff55b6 authored by illsilin's avatar illsilin
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merge from public repo

parents 5e93fa9e 689a5ae4
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Showing with 2774 additions and 1164 deletions
example/ck_tile/12_smoothquant/example_smoothquant.cpp
View file @ f3ff55b6
......@@ -35,7 +35,8 @@ auto create_args(int argc, char* argv[])
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "3328", "m dimension")
.insert("n", "4096", "n dimension")
.insert("stride", "-1", "stride per row, if -1 then equal to n")
.insert("x_stride", "-1", "input stride per row, if -1 then equal to n")
.insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
.insert("e", "1e-5", "epsilon")
.insert("v", "1", "cpu validation or not")
.insert("prec", "fp16", "precision")
......@@ -49,11 +50,14 @@ auto create_args(int argc, char* argv[])
template <typename DataType>
bool run(const ck_tile::ArgParser& arg_parser)
{
ck_tile::index_t m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t stride = arg_parser.get_int("stride");
if(stride < 0)
stride = n;
ck_tile::index_t m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
if(x_stride < 0)
x_stride = n;
ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
if(y_stride < 0)
y_stride = n;
std::string data_type = arg_parser.get_str("prec");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
......@@ -68,14 +72,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
using ComputeDataType = float;
// host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
ck_tile::HostTensor<XScaleDataType> xscale_host({n});
ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_host);
......@@ -116,7 +120,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.GetDeviceBuffer(),
m,
n,
stride};
x_stride,
y_stride};
auto kargs = Kernel::MakeKargs(args);
......@@ -133,7 +138,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation)
{
using YDataType = ComputeDataType;
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {stride, 1});
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {y_stride, 1});
// smooth outlier
{
auto f = [&](auto n_) {
......@@ -183,7 +188,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.FromDevice(qy_host_dev.data());
auto [rtol, atol] = get_elimit<QYDataType>();
if(stride == n)
if(y_stride == n)
{
pass = ck_tile::check_err(qy_host_dev,
qy_host_ref,
......@@ -195,10 +200,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
for(int i_r = 0; i_r < m; i_r++)
{
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
qy_host_dev.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
qy_host_ref.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
qy_host_dev.begin() + i_r * y_stride +
n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
qy_host_ref.begin() + i_r * y_stride +
n);
pass &= ck_tile::check_err(qy_host_dev_row,
qy_host_ref_row,
std::string("qy[") + std::to_string(i_r) +
......@@ -210,8 +217,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride
<< ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride
<< ", y_stride:" << y_stride << ", valid:" << (pass ? "y" : "n") << std::flush
<< std::endl;
}
return pass;
......
example/ck_tile/12_smoothquant/smoothquant.cpp
View file @ f3ff55b6
......@@ -33,7 +33,8 @@ auto create_args(int argc, char* argv[])
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "3328", "m dimension")
.insert("n", "4096", "n dimension")
.insert("stride", "-1", "stride per row, if -1 then equal to n")
.insert("x_stride", "-1", "input stride per row, if -1 then equal to n")
.insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
.insert("v", "1", "cpu validation or not")
.insert("kname", "1", "print kernel name or not")
.insert("prec", "fp16", "precision")
......@@ -47,18 +48,21 @@ auto create_args(int argc, char* argv[])
template <typename DataType>
bool run(const ck_tile::ArgParser& arg_parser)
{
ck_tile::index_t m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t stride = arg_parser.get_int("stride");
if(stride < 0)
stride = n;
ck_tile::index_t m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
if(x_stride < 0)
x_stride = n;
ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
if(y_stride < 0)
y_stride = n;
std::string data_type = arg_parser.get_str("prec");
int kname = arg_parser.get_int("kname");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat");
assert(stride >= n);
assert(x_stride >= n);
using TypeConfig = SmoothquantTypeConfig<DataType>;
......@@ -69,14 +73,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
using ComputeDataType = typename TypeConfig::ComputeDataType;
// host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
ck_tile::HostTensor<XScaleDataType> xscale_host({n});
ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_host);
......@@ -90,7 +94,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
xscale_buf.ToDevice(xscale_host.data());
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride << std::flush;
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride << ", y_stride:" << y_stride
<< std::flush;
smoothquant_traits traits{data_type};
......@@ -100,7 +105,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.GetDeviceBuffer(),
m,
n,
stride};
x_stride,
y_stride};
float ave_time = smoothquant(
traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
......@@ -116,7 +122,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation)
{
using YDataType = ComputeDataType;
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {stride, 1});
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {y_stride, 1});
// smooth outlier
{
auto f = [&](auto n_) {
......@@ -166,7 +172,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.FromDevice(qy_host_dev.data());
auto [rtol, atol] = get_elimit<QYDataType>();
if(stride == n)
if(y_stride == n)
{
pass = ck_tile::check_err(qy_host_dev,
qy_host_ref,
......@@ -178,10 +184,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
for(int i_r = 0; i_r < m; i_r++)
{
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
qy_host_dev.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
qy_host_ref.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
qy_host_dev.begin() + i_r * y_stride +
n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
qy_host_ref.begin() + i_r * y_stride +
n);
pass &= ck_tile::check_err(qy_host_dev_row,
qy_host_ref_row,
std::string("qy[") + std::to_string(i_r) +
......
include/ck/config.h.in
View file @ f3ff55b6
......@@ -111,6 +111,22 @@
#cmakedefine CK_USE_WMMA @CK_USE_WMMA@
#endif
#ifndef CK_USE_GFX94
#cmakedefine CK_USE_GFX94 @CK_USE_GFX94@
#endif
#ifndef DCK_USE_OCP_FP8
#cmakedefine DCK_USE_OCP_FP8 @DCK_USE_OCP_FP8@
#endif
#ifndef CK_USE_FNUZ_FP8
#cmakedefine CK_USE_FNUZ_FP8 @CK_USE_FNUZ_FP8@
#endif
#ifndef CK_USE_FP8_ON_UNSUPPORTED_ARCH
#cmakedefine CK_USE_FP8_ON_UNSUPPORTED_ARCH @CK_USE_FP8_ON_UNSUPPORTED_ARCH@
#endif
// clang-format on
#endif // CK_CONFIG_H_IN
include/ck/tensor_operation/gpu/device/device_base.hpp
View file @ f3ff55b6
......@@ -5,6 +5,8 @@
#include <string>
#include <sstream>
#include <regex>
#include <optional>
#include "ck/stream_config.hpp"
......@@ -12,6 +14,34 @@ namespace ck {
namespace tensor_operation {
namespace device {
#define GET_OBJECT_NAME_IMLP \
std::optional<std::string> GetObjectName() const override \
{ \
std::string str = __PRETTY_FUNCTION__; \
static std::regex obj_name_expr{"<std::string> (.*)::GetObjectName"}; \
std::smatch match; \
if(!std::regex_search(str, match, obj_name_expr)) \
{ \
return str; \
} \
return std::string(match[1]) + ';'; \
}
#define GET_TEMPLATE_INFO_IMPL \
std::optional<std::string> GetTemplateInfo() const override \
{ \
std::string str = __PRETTY_FUNCTION__; \
static std::regex template_expr{"\\[(.*)\\]"}; \
std::smatch match; \
if(!std::regex_search(str, match, template_expr)) \
{ \
return std::nullopt; \
} \
return std::string(match[1]); \
}
#define REGISTER_EXTRA_PRINTING_METHODS GET_OBJECT_NAME_IMLP GET_TEMPLATE_INFO_IMPL
struct BaseArgument
{
BaseArgument() = default;
......@@ -48,6 +78,10 @@ struct BaseOperator
virtual std::string GetTypeIdName() const { return typeid(*this).name(); }
virtual std::optional<std::string> GetObjectName() const { return std::nullopt; }
virtual std::optional<std::string> GetTemplateInfo() const { return std::nullopt; }
virtual std::string GetTypeIdHashCode() const
{
std::ostringstream oss;
......
include/ck/tensor_operation/gpu/device/device_batched_gemm_multi_d.hpp
View file @ f3ff55b6
......@@ -89,7 +89,8 @@ struct DeviceBatchedGemmV2MultiD : public BaseOperator
index_t BatchStrideE,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op) = 0;
CDEElementwiseOperation cde_element_op,
index_t KBatch) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
......
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_xdl_cshuffle_v3.hpp
View file @ f3ff55b6
......@@ -41,12 +41,15 @@ __global__ void
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t g_idx = blockIdx.z % karg.Batch;
const index_t k_idx = blockIdx.z / karg.Batch;
const auto a_batch_offset = karg.compute_ptr_offset_of_batch.GetAPtrOffset(g_idx);
const auto b_batch_offset = karg.compute_ptr_offset_of_batch.GetBPtrOffset(g_idx);
const auto ds_batch_offset = karg.compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
const auto c_batch_offset = karg.compute_ptr_offset_of_batch.GetCPtrOffset(g_idx);
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, k_idx);
// populate pointer, desc for Ds
static_for<0, GridwiseGemm::NumDTensor, 1>{}([&](auto i) {
// D pointer
......@@ -54,8 +57,8 @@ __global__ void
});
GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
karg.p_a_grid + a_batch_offset,
karg.p_b_grid + b_batch_offset,
karg.p_a_grid + a_batch_offset + splitk_batch_offset.a_k_split_offset,
karg.p_b_grid + b_batch_offset + splitk_batch_offset.b_k_split_offset,
karg.p_ds_grid,
karg.p_c_grid + c_batch_offset,
p_shared,
......@@ -87,12 +90,15 @@ __global__ void
__shared__ char p_shared_1[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t g_idx = blockIdx.z % karg.Batch;
const index_t k_idx = blockIdx.z / karg.Batch;
const auto a_batch_offset = karg.compute_ptr_offset_of_batch.GetAPtrOffset(g_idx);
const auto b_batch_offset = karg.compute_ptr_offset_of_batch.GetBPtrOffset(g_idx);
const auto ds_batch_offset = karg.compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
const auto c_batch_offset = karg.compute_ptr_offset_of_batch.GetCPtrOffset(g_idx);
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, k_idx);
// populate pointer, desc for Ds
static_for<0, GridwiseGemm::NumDTensor, 1>{}([&](auto i) {
// D pointer
......@@ -100,8 +106,8 @@ __global__ void
});
GridwiseGemm::template Run_2Lds<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
karg.p_a_grid + a_batch_offset,
karg.p_b_grid + b_batch_offset,
karg.p_a_grid + a_batch_offset + splitk_batch_offset.a_k_split_offset,
karg.p_b_grid + b_batch_offset + splitk_batch_offset.b_k_split_offset,
karg.p_ds_grid,
karg.p_c_grid + c_batch_offset,
p_shared_0,
......@@ -303,7 +309,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
index_t Batch_,
AElementwiseOperation a_element_op_,
BElementwiseOperation b_element_op_,
CElementwiseOperation c_element_op_)
CElementwiseOperation c_element_op_,
index_t KBatch_)
: GridwiseGemm::Argument{p_a_grid_,
p_b_grid_,
p_ds_grid_,
......@@ -315,7 +322,7 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
StrideB_,
StrideDs_,
StrideE_,
1,
KBatch_,
a_element_op_,
b_element_op_,
c_element_op_},
......@@ -336,13 +343,14 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
arg.Print();
}
if(!GridwiseGemm::CheckValidity(arg) || arg.KBatch > 1)
if(!GridwiseGemm::CheckValidity(arg))
{
throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
}
index_t gdx, gdy, gdz;
std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.Batch);
std::tie(gdx, gdy, gdz) =
GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.Batch * arg.KBatch);
float ave_time = 0;
......@@ -387,10 +395,11 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
rotating_mem.Next();
// clear c mem
if(arg_.KBatch > 1)
hipGetErrorString(hipMemsetAsync(arg_.p_c_grid,
0,
arg_.M * arg_.N * sizeof(CDataType),
stream_config.stream_id_));
hipGetErrorString(
hipMemsetAsync(arg_.p_c_grid,
0,
arg.Batch * arg_.M * arg_.N * sizeof(CDataType),
stream_config.stream_id_));
};
ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
......@@ -889,7 +898,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
index_t BatchStrideE,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op)
CElementwiseOperation c_element_op,
index_t KBatch = 1)
{
return Argument{static_cast<const ADataType*>(p_a),
static_cast<const BDataType*>(p_b),
......@@ -909,7 +919,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
Batch,
a_element_op,
b_element_op,
c_element_op};
c_element_op,
KBatch};
}
static auto MakeInvoker() { return Invoker{}; }
......@@ -934,7 +945,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
index_t BatchStrideE,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op) override
CElementwiseOperation c_element_op,
index_t KBatch = 1) override
{
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
static_cast<const BDataType*>(p_b),
......@@ -954,7 +966,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
Batch,
a_element_op,
b_element_op,
c_element_op);
c_element_op,
KBatch);
}
// polymorphic
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp
View file @ f3ff55b6
......@@ -729,6 +729,7 @@ struct DeviceGemm_Xdl_CShuffleV3 : public DeviceGemmV2<ALayout,
return str.str();
}
REGISTER_EXTRA_PRINTING_METHODS
};
} // namespace device
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3_multi_d.hpp
View file @ f3ff55b6
......@@ -41,7 +41,7 @@ __global__ void
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);
GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
karg.p_a_grid + splitk_batch_offset.a_k_split_offset,
......@@ -76,7 +76,7 @@ __global__ void
__shared__ char p_shared_0[GridwiseGemm::GetSharedMemoryNumberOfByte()];
__shared__ char p_shared_1[GridwiseGemm::GetSharedMemoryNumberOfByte()];
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);
GridwiseGemm::template Run_2Lds<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
karg.p_a_grid + splitk_batch_offset.a_k_split_offset,
......@@ -639,27 +639,27 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
struct SplitKBatchOffset
{
__device__ SplitKBatchOffset(Argument& karg)
__device__ SplitKBatchOffset(Argument& karg, index_t k_id)
{
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
{
a_k_split_offset = blockIdx.z * karg.KRead;
a_k_split_offset = k_id * karg.KRead;
}
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
{
a_k_split_offset = blockIdx.z * karg.KRead * karg.StrideA;
a_k_split_offset = k_id * karg.KRead * karg.StrideA;
}
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
{
b_k_split_offset = blockIdx.z * karg.KRead * karg.StrideB;
b_k_split_offset = k_id * karg.KRead * karg.StrideB;
}
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
{
b_k_split_offset = blockIdx.z * karg.KRead;
b_k_split_offset = k_id * karg.KRead;
}
if(blockIdx.z < static_cast<uint32_t>(karg.KBatch - 1))
if(k_id < karg.KBatch - 1)
{
karg.K = karg.KRead;
}
......
include/ck/utility/amd_ck_fp8.hpp
View file @ f3ff55b6
......@@ -18,6 +18,20 @@
#define CK_USE_OCP_FP8 0
#endif
namespace {
// https://en.cppreference.com/w/cpp/types/conditional
template <bool B, class T, class F>
struct conditional
{
using type = T;
};
template <class T, class F>
struct conditional<false, T, F>
{
using type = F;
};
} // namespace
namespace ck {
using f8_fnuz_t = _BitInt(8);
......@@ -191,11 +205,10 @@ __host__ __device__ static inline T cast_from_f8(fp8_storage_t x)
}
}
typename __hip_internal::conditional<
typename conditional<
sizeof(T) == 2,
unsigned short int,
typename __hip_internal::conditional<sizeof(T) == 4, unsigned int, unsigned long long>::
type>::type retval;
typename conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type retval;
if constexpr(we == 5 && is_half && !is_fnuz)
{
......@@ -538,11 +551,10 @@ __host__ __device__ static inline fp8_storage_t cast_to_f8(T _x, unsigned int rn
constexpr int mfmt = (sizeof(T) == 8) ? 52 : ((sizeof(T) == 4) ? 23 : 10);
using T_bitwise = typename __hip_internal::conditional<
using T_bitwise = typename conditional<
sizeof(T) == 2,
unsigned short int,
typename __hip_internal::conditional<sizeof(T) == 4, unsigned int, unsigned long long>::
type>::type;
typename conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type;
T_bitwise x_bitwise = bit_cast<T_bitwise>(_x);
unsigned long long x{x_bitwise};
......
include/ck/utility/math_v2.hpp
View file @ f3ff55b6
......@@ -611,7 +611,7 @@ inline __device__ int8_t neg<int8_t>(int8_t x)
template <>
inline __device__ half_t neg<half_t>(half_t x)
{
return __hneg(x);
return __hneg(static_cast<__half>(x));
};
template <typename T>
......
include/ck_tile/README.md
View file @ f3ff55b6
......@@ -45,5 +45,8 @@ our implementation of different device operators.
**[ops/epilogue]**
epilogue part of our kernel. We may extend this epilogue part to let users to build their own cutomized epilogues.
**[ref]**
reference implementation of cpu or gpu. This folder is supposed to include a specific header on demand.
## examples
currently we put all ck_tile related example under [/example/ck_tile](/example/ck_tile/) folder. Please check each example's subfolder.
include/ck_tile/core.hpp
View file @ f3ff55b6
......@@ -54,6 +54,7 @@
#include "ck_tile/core/tensor/tile_window_linear.hpp"
#include "ck_tile/core/tensor/tile_window_utils.hpp"
#include "ck_tile/core/tensor/update_tile.hpp"
#include "ck_tile/core/utility/amd_address_space.hpp"
#include "ck_tile/core/utility/bit_cast.hpp"
#include "ck_tile/core/utility/functional.hpp"
#include "ck_tile/core/utility/functional_with_tuple.hpp"
......
include/ck_tile/ops/flatmm.hpp
View file @ f3ff55b6
......@@ -5,6 +5,7 @@
#include "ck_tile/ops/flatmm/block/flatmm_32x512x128_1x4x1_16x16x32.hpp"
#include "ck_tile/ops/flatmm/block/flatmm_sn_32x128x512_1x4x1_16x16x32.hpp"
#include "ck_tile/ops/flatmm/block/flatmm_sn_32x128x512_1x4x1_16x16x32_itl.hpp"
#include "ck_tile/ops/flatmm/block/flatmm_uk_config.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
include/ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_policy.hpp
View file @ f3ff55b6
......@@ -810,21 +810,46 @@ struct FusedMoeGemmPipelineFlatmmPolicy
CK_TILE_HOST_DEVICE static constexpr auto GetUK_1()
{
using S_ = typename Problem::BlockShape;
using T_ = typename Problem::Traits;
if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::bf16_t> &&
std::is_same_v<typename Problem::DDataType, ck_tile::bf16_t> &&
std::is_same_v<typename Problem::TopkWeightDataType, float> &&
S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32)
S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
T_::PipeInterleave == false)
{
return FlatmmSn_32x128x512_1x4x1_16x16x32_BF16{};
// return FlatmmSn_32x128x512_1x4x1_16x16x32_BF16_itl{};
}
else if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::fp16_t> &&
std::is_same_v<typename Problem::DDataType, ck_tile::fp16_t> &&
std::is_same_v<typename Problem::TopkWeightDataType, float> &&
S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32)
S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
T_::PipeInterleave == false)
{
return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16{};
// return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16_itl{};
}
else if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::bf16_t> &&
std::is_same_v<typename Problem::DDataType, ck_tile::bf16_t> &&
std::is_same_v<typename Problem::TopkWeightDataType, float> &&
S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
T_::PipeInterleave == true)
{
// return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16{};
return FlatmmSn_32x128x512_1x4x1_16x16x32_BF16_itl{};
}
else if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::fp16_t> &&
std::is_same_v<typename Problem::DDataType, ck_tile::fp16_t> &&
std::is_same_v<typename Problem::TopkWeightDataType, float> &&
S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
T_::PipeInterleave == true)
{
// return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16{};
return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16_itl{};
}
}
};
......
include/ck_tile/ops/fused_moe/pipeline/fused_moegemm_traits.hpp
View file @ f3ff55b6
......@@ -22,7 +22,8 @@ template <bool IsGateOnly_,
FusedMoeGemmWeightPermuteEnum PermuteEnum_ =
FusedMoeGemmWeightPermuteEnum::b_nr_kr_waveflatten,
bool PadHiddenSize_ = false,
bool PadIntermediateSize_ = false>
bool PadIntermediateSize_ = false,
bool PipeInterleave_ = true>
struct FusedMoeGemmTraits
{
// Gate+Up or Gate only
......@@ -32,6 +33,7 @@ struct FusedMoeGemmTraits
static constexpr FusedMoeGemmWeightPermuteEnum PermuteEnum = PermuteEnum_;
static constexpr bool PadHiddenSize = PadHiddenSize_;
static constexpr bool PadIntermediateSize = PadIntermediateSize_;
static constexpr bool PipeInterleave = PipeInterleave_;
};
// Note: this need to be a bit mask
......
include/ck_tile/ops/gemm.hpp
View file @ f3ff55b6
......@@ -23,10 +23,10 @@
#include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1_default_policy.hpp"
#include "ck_tile/ops/gemm/block/block_gemm_problem.hpp"
#include "ck_tile/ops/gemm/block/block_universal_gemm_as_bs_cr.hpp"
#include "ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp"
#include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
#include "ck_tile/ops/gemm/kernel/gemm_tile_partitioner.hpp"
#include "ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp"
#include "ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v3.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_mem.hpp"
......
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