Skip to content

GitLab

"example/1_gemm_xdl/gemm_xdl_bf16.cpp" did not exist on "64350affc5767e7ce3fb211d8145b5c9d18017d8"
Commit 569ad66e authored by Chao Liu's avatar Chao Liu
Browse files

added implicit gemm v1r3 lds_double_buffer NCHW * CYXK = KNHW, reworked static functionals

parent 87d8740b
Hide whitespace changes
Inline Side-by-side
Showing with 32 additions and 245 deletions
src/include/threadwise_4d_tensor_op.hip.hpp
View file @ 569ad66e
......@@ -139,6 +139,7 @@ __device__ void threadwise_4d_tensor_copy_reorder_given_dst2src(SrcDesc,
SrcDesc{}, p_src, DstDesc{}, p_dst, SrcOpLengths{}, MapDst2Src{}, f_copy);
}
#if 0 // replaced threadwise_nd_tensor_copy
template <class SrcData, class DstData, class SrcDesc, class DstDesc, class SrcOpLengths>
__device__ void threadwise_4d_tensor_copy(
SrcDesc, const SrcData* __restrict__ p_src, DstDesc, DstData* __restrict__ p_dst, SrcOpLengths)
......@@ -210,6 +211,7 @@ __device__ void threadwise_4d_tensor_copy_v2(SrcDesc,
}
}
}
#endif
template <class SrcData,
class DstData,
......
src/include/threadwise_nd_tensor_op.hip.hpp
View file @ 569ad66e
......@@ -3,7 +3,7 @@
// need to assume src and dst is aligned
template <class Float, class SrcDesc, class DstDesc, class SrcOpLengths, index_t DataPerRead>
__device__ void threadwise_6d_tensor_copy(SrcDesc,
__device__ void threadwise_nd_tensor_copy(SrcDesc,
const Float* __restrict__ p_src,
DstDesc,
Float* __restrict__ p_dst,
......@@ -12,268 +12,53 @@ __device__ void threadwise_6d_tensor_copy(SrcDesc,
{
using vector_t = typename vector_type<Float, DataPerRead>::MemoryType;
static_assert(SrcDesc{}.GetDimension() == 6 && DstDesc{}.GetDimension() == 6 &&
SrcOpLengths::nDim == 6,
"wrong! should be 6 dimension");
constexpr index_t nDim = SrcOpLengths::GetSize();
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
static_assert(SrcDesc{}.GetDimension() == nDim && DstDesc{}.GetDimension() == nDim,
"wrong! dimension not consistent");
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto ref_desc = make_ConstantTensorDescriptor(SrcOpLengths{});
static_assert(SrcDesc{}.GetStride(I5) == 1 && DstDesc{}.GetStride(I5) == 1,
"wrong! only support stride5 == 1!\n");
static_assert(DataPerRead == 1 || DataPerRead == 2 || DataPerRead == 4,
"wrong! only support DataPerRead == 1, 2 or 4!\n");
static_assert(SrcDesc{}.GetStride(I4) % DataPerRead == 0 &&
DstDesc{}.GetStride(I4) % DataPerRead == 0,
"wrong! src and dst stride should be multiple of DataPerRead to keep alignment");
constexpr index_t L5 = SrcOpLengths{}.Get(I5);
static_assert(L5 % DataPerRead == 0, "wrong! L5 should be evenly divided by DataPerRead");
constexpr index_t nloop_d5 = L5 / DataPerRead;
for(index_t did0 = 0; did0 < ref_desc.GetLength(I0); ++did0)
#if 0
if(get_thread_local_1d_id() == 0 && get_block_1d_id() == 0)
{
for(index_t did1 = 0; did1 < ref_desc.GetLength(I1); ++did1)
{
for(index_t did2 = 0; did2 < ref_desc.GetLength(I2); ++did2)
{
for(index_t did3 = 0; did3 < ref_desc.GetLength(I3); ++did3)
{
for(index_t did4 = 0; did4 < ref_desc.GetLength(I4); ++did4)
{
for(index_t iloop_d5 = 0; iloop_d5 < nloop_d5; ++iloop_d5)
{
const index_t src_index = src_desc.Get1dIndex(
did0, did1, did2, did3, did4, iloop_d5 * DataPerRead);
const index_t dst_index = dst_desc.Get1dIndex(
did0, did1, did2, did3, did4, iloop_d5 * DataPerRead);
*(reinterpret_cast<vector_t*>(p_dst + dst_index)) =
*(reinterpret_cast<const vector_t*>(p_src + src_index));
}
}
}
}
}
print_ConstantTensorDescriptor(src_desc, "src_desc");
print_ConstantTensorDescriptor(dst_desc, "dst_desc");
print_ConstantTensorDescriptor(ref_desc, "ref_desc");
}
}
// need to assume src and dst is aligned
template <class Float, class SrcDesc, class DstDesc, class SrcOpLengths, index_t DataPerRead>
__device__ void threadwise_8d_tensor_copy(SrcDesc,
const Float* __restrict__ p_src,
DstDesc,
Float* __restrict__ p_dst,
SrcOpLengths,
Number<DataPerRead>)
{
using vector_t = typename vector_type<Float, DataPerRead>::MemoryType;
static_assert(SrcDesc{}.GetDimension() == 8 && DstDesc{}.GetDimension() == 8 &&
SrcOpLengths::nDim == 8,
"wrong! should be 8 dimension");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto ref_desc = make_ConstantTensorDescriptor(SrcOpLengths{});
#endif
static_assert(SrcDesc{}.GetStride(I7) == 1 && DstDesc{}.GetStride(I7) == 1,
"wrong! only support stride7 == 1!\n");
static_assert(DataPerRead == 1 || (SrcDesc{}.GetStride(Number<nDim - 1>{}) == 1 &&
DstDesc{}.GetStride(Number<nDim - 1>{}) == 1),
"wrong! only support stride[nDim-1] == 1!\n");
static_assert(DataPerRead == 1 || DataPerRead == 2 || DataPerRead == 4,
"wrong! only support DataPerRead == 1, 2 or 4!\n");
static_assert(SrcDesc{}.GetStride(I6) % DataPerRead == 0 &&
DstDesc{}.GetStride(I6) % DataPerRead == 0,
"wrong! src and dst stride should be multiple of DataPerRead to keep alignment");
static_assert(
SrcDesc{}.GetStride(Number<nDim - 2>{}) % DataPerRead == 0 &&
DstDesc{}.GetStride(Number<nDim - 2>{}) % DataPerRead == 0,
"wrong! src and dst stride[nDim-2] should be multiple of DataPerRead to keep alignment");
constexpr index_t L7 = SrcOpLengths{}.Get(I7);
constexpr index_t L_Back = SrcOpLengths{}.Back();
static_assert(L7 % DataPerRead == 0, "wrong! L7 should be evenly divided by DataPerRead");
static_assert(L_Back % DataPerRead == 0,
"wrong! lengths[nDim-1] should be evenly divided by DataPerRead");
constexpr index_t nloop_d7 = L7 / DataPerRead;
constexpr index_t nRead = L_Back / DataPerRead;
for(index_t did0 = 0; did0 < ref_desc.GetLength(I0); ++did0)
{
for(index_t did1 = 0; did1 < ref_desc.GetLength(I1); ++did1)
{
for(index_t did2 = 0; did2 < ref_desc.GetLength(I2); ++did2)
{
for(index_t did3 = 0; did3 < ref_desc.GetLength(I3); ++did3)
{
for(index_t did4 = 0; did4 < ref_desc.GetLength(I4); ++did4)
{
for(index_t did5 = 0; did5 < ref_desc.GetLength(I5); ++did5)
{
for(index_t did6 = 0; did6 < ref_desc.GetLength(I6); ++did6)
{
for(index_t iloop_d7 = 0; iloop_d7 < nloop_d7; ++iloop_d7)
{
const index_t src_index =
src_desc.Get1dIndex(did0,
did1,
did2,
did3,
did4,
did5,
did6,
iloop_d7 * DataPerRead);
const index_t dst_index =
dst_desc.Get1dIndex(did0,
did1,
did2,
did3,
did4,
did5,
did6,
iloop_d7 * DataPerRead);
*(reinterpret_cast<vector_t*>(p_dst + dst_index)) =
*(reinterpret_cast<const vector_t*>(p_src + src_index));
}
}
}
}
}
}
}
}
}
// need to assume src and dst is aligned
template <class Float, class SrcDesc, class DstDesc, class SrcOpLengths, index_t DataPerRead>
__device__ void threadwise_10d_tensor_copy(SrcDesc,
const Float* __restrict__ p_src,
DstDesc,
Float* __restrict__ p_dst,
SrcOpLengths,
Number<DataPerRead>)
{
using vector_t = typename vector_type<Float, DataPerRead>::MemoryType;
static_assert(SrcDesc{}.GetDimension() == 10 && DstDesc{}.GetDimension() == 10 &&
SrcOpLengths::GetSize() == 10,
"wrong! should be 10 dimension");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
constexpr auto I9 = Number<9>{};
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto ref_desc = make_ConstantTensorDescriptor(SrcOpLengths{});
static_assert(SrcDesc{}.GetStride(I9) == 1 && DstDesc{}.GetStride(I9) == 1,
"wrong! only support stride7 == 1!\n");
static_assert(DataPerRead == 1 || DataPerRead == 2 || DataPerRead == 4,
"wrong! only support DataPerRead == 1, 2 or 4!\n");
static_ford<decltype(ref_desc.GetLengths().PopBack())>{}([=](auto Ids) {
static_for<0, nRead, 1>{}([=](auto IRead) {
constexpr auto multi_id = decltype(Ids){}.PushBack(Number<IRead.Get() * DataPerRead>{});
static_assert(SrcDesc{}.GetStride(I8) % DataPerRead == 0 &&
DstDesc{}.GetStride(I8) % DataPerRead == 0,
"wrong! src and dst stride should be multiple of DataPerRead to keep alignment");
const index_t src_index = src_desc.Get1dIndex(multi_id);
constexpr index_t L9 = SrcOpLengths{}.Get(I9);
const index_t dst_index = dst_desc.Get1dIndex(multi_id);
static_assert(L9 % DataPerRead == 0, "wrong! L9 should be evenly divided by DataPerRead");
constexpr index_t nloop_d9 = L9 / DataPerRead;
#pragma unroll
for(index_t did0 = 0; did0 < ref_desc.GetLength(I0); ++did0)
{
#pragma unroll
for(index_t did1 = 0; did1 < ref_desc.GetLength(I1); ++did1)
{
#pragma unroll
for(index_t did2 = 0; did2 < ref_desc.GetLength(I2); ++did2)
{
#pragma unroll
for(index_t did3 = 0; did3 < ref_desc.GetLength(I3); ++did3)
{
#pragma unroll
for(index_t did4 = 0; did4 < ref_desc.GetLength(I4); ++did4)
{
#pragma unroll
for(index_t did5 = 0; did5 < ref_desc.GetLength(I5); ++did5)
{
#pragma unroll
for(index_t did6 = 0; did6 < ref_desc.GetLength(I6); ++did6)
{
#pragma unroll
for(index_t did7 = 0; did7 < ref_desc.GetLength(I7); ++did7)
{
#pragma unroll
for(index_t did8 = 0; did8 < ref_desc.GetLength(I8); ++did8)
{
#pragma unroll
for(index_t iloop_d9 = 0; iloop_d9 < nloop_d9; ++iloop_d9)
{
const index_t src_index =
src_desc.Get1dIndex(did0,
did1,
did2,
did3,
did4,
did5,
did6,
did7,
did8,
iloop_d9 * DataPerRead);
const index_t dst_index =
dst_desc.Get1dIndex(did0,
did1,
did2,
did3,
did4,
did5,
did6,
did7,
did8,
iloop_d9 * DataPerRead);
*(reinterpret_cast<vector_t*>(p_dst + dst_index)) =
*(reinterpret_cast<const vector_t*>(p_src +
src_index));
}
}
}
}
}
}
}
}
}
}
*(reinterpret_cast<vector_t*>(&p_dst[dst_index])) =
*(reinterpret_cast<const vector_t*>(&p_src[src_index]));
});
});
}
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment