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Commit 08bb4372 authored by Chao Liu's avatar Chao Liu
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clean up

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composable_kernel/include/tensor_operation/blockwise_gemm_v2.hpp
View file @ 08bb4372
...@@ -7,366 +7,6 @@ ...@@ -7,366 +7,6 @@
namespace ck { namespace ck {
#if 0
// blockwise GEMM: C[M, N] += transpose(A[K, M]) * B[K, N]
// A and B are visable to the whole block, C is distributed among each thread
// If following number are power of 2, index calculation shall be greatly reduced:
// MPerThreadSubC, NPerThreadSubC, MLevel0ThreadCluster, NLevel0ThreadCluster,
// MLevel1ThreadCluster, NLevel1ThreadCluster
template <index_t BlockSize,
typename FloatA,
typename FloatB,
typename FloatC,
typename BlockMatrixA,
typename BlockMatrixB,
typename ThreadMatrixC,
index_t MPerThreadSubC,
index_t NPerThreadSubC,
index_t KPerThreadLoop,
index_t MLevel0ThreadCluster,
index_t NLevel0ThreadCluster,
index_t MLevel1ThreadCluster,
index_t NLevel1ThreadCluster,
index_t ThreadGemmADataPerRead_M,
index_t ThreadGemmBDataPerRead_N>
struct BlockwiseGemm_km_kn_m0m1n0n1_v1
{
struct MatrixIndex
{
index_t row;
index_t col;
};
index_t mMyThreadOffsetA;
index_t mMyThreadOffsetB;
__device__ BlockwiseGemm_km_kn_m0m1n0n1_v1()
{
static_assert(BlockMatrixA::IsKnownAtCompileTime() &&
BlockMatrixB::IsKnownAtCompileTime() &&
ThreadMatrixC::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr index_t ThreadPerLevel1Cluster = MLevel0ThreadCluster * NLevel0ThreadCluster *
MLevel1ThreadCluster * NLevel1ThreadCluster;
static_assert(BlockSize == ThreadPerLevel1Cluster, "wrong! wrong blocksize\n");
static_assert(BlockMatrixA{}.GetLength(I0) == BlockMatrixB{}.GetLength(I0),
"wrong! K dimension not consistent\n");
constexpr index_t M = BlockMatrixA{}.GetLength(I1); // A is transposed
constexpr index_t N = BlockMatrixB{}.GetLength(I1);
static_assert(M % (MPerThreadSubC * MLevel0ThreadCluster * MLevel1ThreadCluster) == 0 &&
N % (NPerThreadSubC * NLevel0ThreadCluster * NLevel1ThreadCluster) == 0,
"wrong! Cannot evenly divide work among\n");
static_assert(ThreadMatrixC{}.GetLength(I0) == GetThreadMatrixCLengths()[I0] &&
ThreadMatrixC{}.GetLength(I1) == GetThreadMatrixCLengths()[I1],
"wrong! ThreadMatrixC lengths is wrong");
auto c_thread_mtx_index = GetBeginOfThreadMatrixC(get_thread_local_1d_id());
mMyThreadOffsetA = BlockMatrixA{}.CalculateOffset(make_tuple(0, c_thread_mtx_index.row));
mMyThreadOffsetB = BlockMatrixB{}.CalculateOffset(make_tuple(0, c_thread_mtx_index.col));
}
__device__ static constexpr auto GetThreadMatrixCLengths()
{
constexpr auto I1 = Number<1>{};
constexpr index_t M = BlockMatrixA{}.GetLength(I1); // A is transposed
constexpr index_t N = BlockMatrixB{}.GetLength(I1);
constexpr index_t MRepeat =
M / (MPerThreadSubC * MLevel0ThreadCluster * MLevel1ThreadCluster);
constexpr index_t NRepeat =
N / (NPerThreadSubC * NLevel0ThreadCluster * NLevel1ThreadCluster);
return Sequence<MRepeat * MPerThreadSubC, NRepeat * NPerThreadSubC>{};
}
__device__ static MatrixIndex GetBeginOfThreadMatrixC(index_t thread_id)
{
constexpr index_t ThreadPerLevel0Cluster = MLevel0ThreadCluster * NLevel0ThreadCluster;
index_t level1_id = thread_id / ThreadPerLevel0Cluster;
index_t level1_m_id = level1_id / NLevel1ThreadCluster;
index_t level1_n_id = level1_id % NLevel1ThreadCluster;
index_t level0_id = thread_id % ThreadPerLevel0Cluster;
index_t level0_m_id = level0_id / NLevel0ThreadCluster;
index_t level0_n_id = level0_id % NLevel0ThreadCluster;
constexpr index_t MPerLevel0Cluster = MPerThreadSubC * MLevel0ThreadCluster;
constexpr index_t NPerLevel0Cluster = NPerThreadSubC * NLevel0ThreadCluster;
return MatrixIndex{level1_m_id * MPerLevel0Cluster + level0_m_id * MPerThreadSubC,
level1_n_id * NPerLevel0Cluster + level0_n_id * NPerThreadSubC};
}
__device__ void
Run_naive(const FloatA* p_a_block, const FloatB* p_b_block, FloatC* p_c_thread) const
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto a_block_mtx = BlockMatrixA{};
constexpr auto b_block_mtx = BlockMatrixB{};
constexpr auto c_thread_mtx = ThreadMatrixC{};
constexpr auto K = a_block_mtx.GetLength(I0);
constexpr auto MPerThread = c_thread_mtx.GetLength(I0);
constexpr auto NPerThread = c_thread_mtx.GetLength(I1);
constexpr index_t MPerLevel1Cluster =
MPerThreadSubC * MLevel0ThreadCluster * MLevel1ThreadCluster;
constexpr index_t NPerLevel1Cluster =
NPerThreadSubC * NLevel0ThreadCluster * NLevel1ThreadCluster;
constexpr index_t MRepeat = MPerThread / MPerThreadSubC;
constexpr index_t NRepeat = NPerThread / NPerThreadSubC;
// thread A, B for GEMM
constexpr auto a_thread_mtx = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<KPerThreadLoop>{}, Number<MPerThread>{}));
constexpr auto b_thread_mtx = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<KPerThreadLoop>{}, Number<NPerThread>{}));
FloatA p_a_thread[a_thread_mtx.GetElementSpaceSize()];
FloatB p_b_thread[b_thread_mtx.GetElementSpaceSize()];
constexpr auto a_thread_copy = ThreadwiseMatrixSliceCopy_v2<BlockMatrixA,
decltype(a_thread_mtx),
KPerThreadLoop,
MPerThreadSubC,
ThreadGemmADataPerRead_M>{};
constexpr auto b_thread_copy = ThreadwiseMatrixSliceCopy_v2<BlockMatrixB,
decltype(b_thread_mtx),
KPerThreadLoop,
NPerThreadSubC,
ThreadGemmBDataPerRead_N>{};
constexpr auto threadwise_gemm = ThreadwiseGemm_km_kn_mn_v1<FloatA,
FloatB,
FloatC,
decltype(a_thread_mtx),
decltype(b_thread_mtx),
decltype(c_thread_mtx)>{};
// loop over k
static_for<0, K, KPerThreadLoop>{}([&](auto k_begin) {
// read A
static_for<0, MRepeat, 1>{}([&](auto m_repeat) {
a_thread_copy.Run(p_a_block +
a_block_mtx.CalculateOffset(
make_tuple(k_begin, m_repeat * MPerLevel1Cluster)) +
mMyThreadOffsetA,
p_a_thread + a_thread_mtx.CalculateOffset(
make_tuple(0, m_repeat * MPerThreadSubC)));
});
// read B
static_for<0, NRepeat, 1>{}([&](auto n_repeat) {
b_thread_copy.Run(p_b_block +
b_block_mtx.CalculateOffset(
make_tuple(k_begin, n_repeat * NPerLevel1Cluster)) +
mMyThreadOffsetB,
p_b_thread + b_thread_mtx.CalculateOffset(
make_tuple(0, n_repeat * NPerThreadSubC)));
});
// C += A * B
threadwise_gemm.Run(p_a_thread, p_b_thread, p_c_thread);
});
}
__device__ void
Run_pipelined_2x2(const FloatA* p_a_block, const FloatB* p_b_block, FloatC* p_c_thread) const
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto a_block_mtx = BlockMatrixA{};
constexpr auto b_block_mtx = BlockMatrixB{};
constexpr auto c_thread_mtx = ThreadMatrixC{};
constexpr auto K = a_block_mtx.GetLength(I0);
constexpr auto MPerThread = c_thread_mtx.GetLength(I0);
constexpr auto NPerThread = c_thread_mtx.GetLength(I1);
constexpr index_t MPerLevel1Cluster =
MPerThreadSubC * MLevel0ThreadCluster * MLevel1ThreadCluster;
constexpr index_t NPerLevel1Cluster =
NPerThreadSubC * NLevel0ThreadCluster * NLevel1ThreadCluster;
constexpr index_t MRepeat = MPerThread / MPerThreadSubC;
constexpr index_t NRepeat = NPerThread / NPerThreadSubC;
static_assert(MRepeat == 2 && NRepeat == 2,
"wrong! inline asm cannot deal with this GEMM config yet");
// thread A, B
constexpr auto a_thread_mtx = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<KPerThreadLoop>{}, Number<MPerThread>{}));
constexpr auto b_thread_mtx = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<KPerThreadLoop>{}, Number<NPerThread>{}));
// thread A-sub, B-sub
constexpr auto a_thread_sub_mtx = make_dynamic_naive_tensor_descriptor_v2(
make_tuple(Number<KPerThreadLoop>{}, Number<MPerThreadSubC>{}),
make_tuple(Number<MPerThread>{}, Number<1>{}));
constexpr auto b_thread_sub_mtx = make_dynamic_naive_tensor_descriptor_v2(
make_tuple(Number<KPerThreadLoop>{}, Number<NPerThreadSubC>{}),
make_tuple(Number<NPerThread>{}, Number<1>{}));
constexpr auto c_thread_sub_mtx = make_dynamic_naive_tensor_descriptor_v2(
make_tuple(Number<MPerThreadSubC>{}, Number<NPerThreadSubC>{}),
make_tuple(Number<NPerThread>{}, Number<1>{}));
FloatA p_a_thread[a_thread_mtx.GetElementSpaceSize()];
FloatB p_b_thread[b_thread_mtx.GetElementSpaceSize()];
constexpr auto a_thread_copy = ThreadwiseMatrixSliceCopy_v2<BlockMatrixA,
decltype(a_thread_mtx),
KPerThreadLoop,
MPerThreadSubC,
ThreadGemmADataPerRead_M>{};
constexpr auto b_thread_copy = ThreadwiseMatrixSliceCopy_v2<BlockMatrixB,
decltype(b_thread_mtx),
KPerThreadLoop,
NPerThreadSubC,
ThreadGemmBDataPerRead_N>{};
constexpr auto threadwise_gemm = ThreadwiseGemm_km_kn_mn_v1<FloatA,
FloatB,
FloatC,
decltype(a_thread_sub_mtx),
decltype(b_thread_sub_mtx),
decltype(c_thread_sub_mtx)>{};
const FloatA* p_a_block_off = p_a_block + mMyThreadOffsetA;
const FloatB* p_b_block_off = p_b_block + mMyThreadOffsetB;
// read A_sub_0
a_thread_copy.Run(p_a_block_off, p_a_thread);
// read B_sub_0
b_thread_copy.Run(p_b_block_off, p_b_thread);
// read B_sub_1
b_thread_copy.Run(p_b_block_off +
b_block_mtx.CalculateOffset(make_tuple(0, NPerLevel1Cluster)),
p_b_thread + b_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)));
// read A_sub_1
a_thread_copy.Run(p_a_block_off +
a_block_mtx.CalculateOffset(make_tuple(0, MPerLevel1Cluster)),
p_a_thread + a_thread_mtx.CalculateOffset(make_tuple(0, MPerThreadSubC)));
// C_sub_00 += transpose(A_sub_0) * B_sub_0
threadwise_gemm.Run(p_a_thread, p_b_thread, p_c_thread);
// C_sub_01 += transpose(A_sub_0) * B_sub_1
threadwise_gemm.Run(
p_a_thread,
p_b_thread + b_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)),
p_c_thread + c_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)));
// loop over rest of k
static_for<KPerThreadLoop, K, KPerThreadLoop>{}([&](auto k) {
// read A_sub_0
a_thread_copy.Run(p_a_block_off + a_block_mtx.CalculateOffset(make_tuple(k, 0)),
p_a_thread);
// C_sub_10 += transpose(A_sub_1) * B_sub_0
threadwise_gemm.Run(
p_a_thread + a_thread_mtx.CalculateOffset(make_tuple(0, MPerThreadSubC)),
p_b_thread,
p_c_thread + c_thread_mtx.CalculateOffset(make_tuple(MPerThreadSubC, 0)));
// read B_sub_0
b_thread_copy.Run(p_b_block_off + b_block_mtx.CalculateOffset(make_tuple(k, 0)),
p_b_thread);
// C_sub_11 += transpose(A_sub_1) * B_sub_1
threadwise_gemm.Run(
p_a_thread + a_thread_mtx.CalculateOffset(make_tuple(0, MPerThreadSubC)),
p_b_thread + b_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)),
p_c_thread +
c_thread_mtx.CalculateOffset(make_tuple(MPerThreadSubC, NPerThreadSubC)));
// read B_sub_1
b_thread_copy.Run(
p_b_block_off + b_block_mtx.CalculateOffset(make_tuple(k, NPerLevel1Cluster)),
p_b_thread + b_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)));
// read A_sub_1
a_thread_copy.Run(
p_a_block_off + a_block_mtx.CalculateOffset(make_tuple(k, MPerLevel1Cluster)),
p_a_thread + a_thread_mtx.CalculateOffset(make_tuple(0, MPerThreadSubC)));
// C_sub_00 += transpose(A_sub_0) * B_sub_0
threadwise_gemm.Run(p_a_thread, p_b_thread, p_c_thread);
// C_sub_01 += transpose(A_sub_0) * B_sub_1
threadwise_gemm.Run(
p_a_thread,
p_b_thread + b_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)),
p_c_thread + c_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)));
});
// C_sub_10 += transpose(A_sub_1) * B_sub_0
threadwise_gemm.Run(
p_a_thread + a_thread_mtx.CalculateOffset(make_tuple(0, MPerThreadSubC)),
p_b_thread,
p_c_thread + c_thread_mtx.CalculateOffset(make_tuple(MPerThreadSubC, 0)));
// C_sub_11 += transpose(A_sub_1) * B_sub_1
threadwise_gemm.Run(
p_a_thread + a_thread_mtx.CalculateOffset(make_tuple(0, MPerThreadSubC)),
p_b_thread + b_thread_mtx.CalculateOffset(make_tuple(0, NPerThreadSubC)),
p_c_thread + c_thread_mtx.CalculateOffset(make_tuple(MPerThreadSubC, NPerThreadSubC)));
}
__device__ void Run(const FloatA* p_a_block, const FloatB* p_b_block, FloatC* p_c_thread) const
{
#if CK_EXPERIMENTAL_BLOCKWISE_GEMM_USE_PIPELINE
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr index_t MPerThread = ThreadMatrixC{}.GetLength(I0);
constexpr index_t NPerThread = ThreadMatrixC{}.GetLength(I1);
constexpr index_t MRepeat = MPerThread / MPerThreadSubC;
constexpr index_t NRepeat = NPerThread / NPerThreadSubC;
if constexpr(MRepeat == 2 && NRepeat == 2)
{
Run_pipelined_2x2(p_a_block, p_b_block, p_c_thread);
}
else
{
Run_naive(p_a_block, p_b_block, p_c_thread);
}
#else
Run_naive(p_a_block, p_b_block, p_c_thread);
#endif
}
};
#endif
// C[M, N] += transpose(A[K, M]) * B[K, N] // C[M, N] += transpose(A[K, M]) * B[K, N]
// A and B are visable to the whole block, C is distributed among each thread // A and B are visable to the whole block, C is distributed among each thread
// Assume: // Assume:
......
composable_kernel/include/tensor_operation/blockwise_gemm_v3.hpp
View file @ 08bb4372
...@@ -6,201 +6,6 @@ ...@@ -6,201 +6,6 @@
namespace ck { namespace ck {
#if 0
// blockwise GEMM: C[M, N] += transpose(A[K, M]) * B[K, N]
// A and B are visable to the whole block, C is distributed among each thread
// If following number are power of 2, index calculation shall be greatly reduced:
// KPerThread, HPerThread, MLevel0ThreadCluster, NLevel0ThreadCluster,
// MLevel1ThreadCluster, NLevel1ThreadCluster
template <index_t BlockSize,
typename BlockMatrixA,
typename BlockMatrixB,
typename ThreadMatrixC,
index_t KPerThread,
index_t HPerThread,
index_t WPerThread,
index_t EPerThreadLoop,
index_t ThreadGemmADataPerRead_K,
index_t ThreadGemmBDataPerRead_W>
struct BlockwiseGemm_km_kn_m0m1n0n1_v3
{
struct MatrixIndex
{
index_t k;
index_t h;
index_t w;
};
index_t mMyThreadOffsetA;
__device__ BlockwiseGemm_km_kn_m0m1n0n1_v3()
{
static_assert(BlockMatrixA::IsKnownAtCompileTime() &&
BlockMatrixB::IsKnownAtCompileTime() &&
ThreadMatrixC::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
static_assert(BlockMatrixA{}.GetLength(I0) == BlockMatrixB{}.GetLength(I0),
"wrong! K dimension not consistent\n");
constexpr index_t K = BlockMatrixA{}.GetLength(I1); // A is transposed
constexpr index_t N = BlockMatrixB{}.GetLength(I1);
constexpr index_t H = BlockMatrixB{}.GetLength(I2);
constexpr index_t W = BlockMatrixB{}.GetLength(I3);
static_assert(K % KPerThread == 0 && H % HPerThread == 0 && W % WPerThread == 0,
"wrong! Cannot evenly divide work among\n");
constexpr auto KThreadCluster = K / KPerThread;
constexpr auto HThreadCluster = H / HPerThread;
constexpr auto WThreadCluster = W / WPerThread;
static_assert(BlockSize == KThreadCluster * HThreadCluster * WThreadCluster,
"wrong! wrong blocksize\n");
auto c_thread_mtx_index = GetBeginOfThreadMatrixC(get_thread_local_1d_id());
mMyThreadOffsetA =
BlockMatrixA{}.CalculateOffset(make_tuple(0, c_thread_mtx_index.k * KPerThread));
}
__device__ static constexpr auto GetThreadMatrixCLengths()
{
return Sequence<KPerThread, 1, HPerThread, WPerThread>{};
}
__device__ static MatrixIndex GetBeginOfThreadMatrixC(index_t thread_id)
{
constexpr index_t H = BlockMatrixB{}.GetLength(Number<2>{});
constexpr index_t W = BlockMatrixB{}.GetLength(Number<3>{});
constexpr auto num_w_threads = W / WPerThread;
constexpr auto num_h_threads = H / HPerThread;
constexpr auto num_hw_threads = num_w_threads * num_h_threads;
index_t k_thread_id = thread_id / num_hw_threads;
index_t hw_thread_id = thread_id % num_hw_threads;
index_t h_thread_id = hw_thread_id / num_w_threads;
index_t w_thread_id = hw_thread_id % num_w_threads;
return MatrixIndex{k_thread_id, h_thread_id, w_thread_id};
}
template <typename SrcDesc,
typename DstDesc,
index_t NSliceRow,
index_t NSliceCol,
index_t DataPerAccess>
struct ThreadwiseSliceCopy_a
{
template <typename Data>
__device__ static void Run(const Data* p_src, Data* p_dst)
{
static_assert(SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
using vector_t = typename vector_type_maker<Data, DataPerAccess>::type::type;
static_for<0, NSliceRow, 1>{}([&](auto i) {
static_for<0, NSliceCol, DataPerAccess>{}([&](auto j) {
constexpr auto src_offset = SrcDesc{}.CalculateOffset(make_tuple(i, j));
constexpr auto dst_offset = DstDesc{}.CalculateOffset(make_tuple(i, j));
*reinterpret_cast<vector_t*>(&p_dst[dst_offset]) =
*reinterpret_cast<const vector_t*>(&p_src[src_offset]);
});
});
}
};
template <typename FloatA, typename FloatB, typename FloatC>
__device__ void
Run_naive(const FloatA* p_a_block, const FloatB* p_b_thread, FloatC* p_c_thread) const
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto a_block_mtx = BlockMatrixA{};
constexpr auto EPerBlock = a_block_mtx.GetLength(I0);
constexpr auto KPerThreadSubC = 4;
constexpr auto HoPerThreadSubC = 2;
constexpr auto WoPerThreadSubC = 2;
static_assert(KPerThread % KPerThreadSubC == 0, "");
static_assert(HPerThread % HoPerThreadSubC == 0, "");
static_assert(WPerThread % WoPerThreadSubC == 0, "");
// thread A, B for GEMM
constexpr auto a_thread_mtx = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<EPerThreadLoop>{}, Number<KPerThreadSubC>{}));
constexpr auto b_thread_mtx = make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
Number<EPerThreadLoop>{}, Number<1>{}, Number<HPerThread>{}, Number<WPerThread>{}));
constexpr auto c_thread_mtx = make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
Number<KPerThreadSubC>{}, Number<1>{}, Number<HPerThread>{}, Number<WPerThread>{}));
FloatA p_a_thread[a_thread_mtx.GetElementSpaceSize()];
constexpr auto a_thread_copy = ThreadwiseSliceCopy_a<BlockMatrixA,
decltype(a_thread_mtx),
EPerThreadLoop,
KPerThreadSubC,
ThreadGemmADataPerRead_K>{};
constexpr auto threadwise_gemm = ThreadwiseGemm_km_kn_mn_v3<decltype(a_thread_mtx),
decltype(b_thread_mtx),
decltype(c_thread_mtx),
HoPerThreadSubC,
WoPerThreadSubC>{};
// loop over k
#pragma unroll
for(index_t e_begin = 0; e_begin < EPerBlock; e_begin += EPerThreadLoop)
{
#pragma unroll
for(index_t k_begin = 0; k_begin < KPerThread; k_begin += KPerThreadSubC)
{
a_thread_copy.Run(p_a_block +
a_block_mtx.CalculateOffset(make_tuple(e_begin, k_begin)) +
mMyThreadOffsetA,
p_a_thread);
#pragma unroll
for(index_t h_begin = 0; h_begin < HPerThread; h_begin += HoPerThreadSubC)
{
#pragma unroll
for(index_t w_begin = 0; w_begin < WPerThread; w_begin += WoPerThreadSubC)
{
threadwise_gemm.Run(p_a_thread,
p_b_thread + b_thread_mtx.CalculateOffset(make_tuple(
e_begin, 0, h_begin, w_begin)),
p_c_thread + c_thread_mtx.CalculateOffset(make_tuple(
k_begin, 0, h_begin, w_begin)));
}
}
}
}
}
template <typename FloatA, typename FloatB, typename FloatC>
__device__ void Run(const FloatA* p_a_block, const FloatB* p_b_thread, FloatC* p_c_thread) const
{
Run_naive(p_a_block, p_b_thread, p_c_thread);
}
};
#else
// blockwise GEMM: C[M, N] += transpose(A[K, M]) * B[K, N] // blockwise GEMM: C[M, N] += transpose(A[K, M]) * B[K, N]
// A and B are visable to the whole block, C is distributed among each thread // A and B are visable to the whole block, C is distributed among each thread
// If following number are power of 2, index calculation shall be greatly reduced: // If following number are power of 2, index calculation shall be greatly reduced:
...@@ -379,7 +184,6 @@ struct BlockwiseGemm_km_kn_m0m1n0n1_v3 ...@@ -379,7 +184,6 @@ struct BlockwiseGemm_km_kn_m0m1n0n1_v3
}); });
} }
}; };
#endif
} // namespace ck } // namespace ck
#endif #endif
composable_kernel/include/tensor_operation/threadwise_gemm_v2.hpp
View file @ 08bb4372
...@@ -6,170 +6,6 @@ ...@@ -6,170 +6,6 @@
namespace ck { namespace ck {
#if 0
template <typename Float, typename Desc>
__device__ void threadwise_matrix_set_zero_v2(Desc, Float* __restrict__ p_thread)
{
static_assert(Desc::IsKnownAtCompileTime(), "wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto desc = Desc{};
constexpr auto M = desc.GetLength(I0);
constexpr auto N = desc.GetLength(I1);
static_for<0, M, 1>{}([&](auto i) {
static_for<0, N, 1>{}([&](auto j) {
constexpr auto offset = desc.CalculateOffset(make_tuple(i, j));
p_thread[offset] = Float(0);
});
});
}
template <typename SrcDesc,
typename DstDesc,
index_t NSliceRow,
index_t NSliceCol,
index_t DataPerAccess>
struct ThreadwiseMatrixSliceCopy_v2
{
template <typename Data>
__device__ static void Run(const Data* p_src, Data* p_dst)
{
static_assert(SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
using vector_t = typename vector_type_maker<Data, DataPerAccess>::type::type;
static_for<0, NSliceRow, 1>{}([&](auto i) {
static_for<0, NSliceCol, DataPerAccess>{}([&](auto j) {
constexpr auto src_offset = SrcDesc{}.CalculateOffset(make_tuple(i, j));
constexpr auto dst_offset = DstDesc{}.CalculateOffset(make_tuple(i, j));
*reinterpret_cast<vector_t*>(&p_dst[dst_offset]) =
*reinterpret_cast<const vector_t*>(&p_src[src_offset]);
});
});
}
};
// C[M, N] += transpose(A[K, M]) * B[K, N]
// Element of matrix can be vectorized data
template <typename FloatA,
typename FloatB,
typename FloatC,
typename ADesc,
typename BDesc,
typename CDesc,
typename std::enable_if<ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseGemm_km_kn_mn_v1
{
__device__ static void Run_source(const FloatA* p_a, const FloatB* p_b, FloatC* p_c)
{
static_assert(ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto M = CDesc{}.GetLength(I0);
constexpr auto N = CDesc{}.GetLength(I1);
constexpr auto K = ADesc{}.GetLength(I0);
static_for<0, K, 1>{}([&](auto k) {
static_for<0, M, 1>{}([&](auto m) {
static_for<0, N, 1>{}([&](auto n) {
constexpr auto a_offset = ADesc{}.CalculateOffset(make_tuple(k, m));
constexpr auto b_offset = BDesc{}.CalculateOffset(make_tuple(k, n));
constexpr auto c_offset = CDesc{}.CalculateOffset(make_tuple(m, n));
p_c[c_offset] +=
inner_product_with_conversion<FloatC>{}(p_a[a_offset], p_b[b_offset]);
});
});
});
}
#if CK_THREADWISE_GEMM_USE_AMD_INLINE_ASM
__device__ static void Run_amd_asm(const FloatA* p_a, const FloatB* p_b, FloatC* p_c)
{
static_assert(ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto M = CDesc{}.GetLength(I0);
constexpr auto N = CDesc{}.GetLength(I1);
constexpr auto K = ADesc{}.GetLength(I0);
static_assert(N == 4 || N == 2, "wrong! this config not supported by asm yet");
static_for<0, K, 1>{}([&](auto k) {
static_for<0, M, 1>{}([&](auto m) {
constexpr auto a_offset = ADesc{}.CalculateOffset(make_tuple(k, m));
if constexpr(N == 2)
{
constexpr auto b_offset_0 = BDesc{}.CalculateOffset(make_tuple(k, I0));
constexpr auto b_offset_1 = BDesc{}.CalculateOffset(make_tuple(k, I1));
constexpr auto c_offset_0 = CDesc{}.CalculateOffset(make_tuple(m, I0));
constexpr auto c_offset_1 = CDesc{}.CalculateOffset(make_tuple(m, I1));
amd_assembly_outer_product_1x2(p_a[a_offset],
p_b[b_offset_0],
p_b[b_offset_1],
p_c[c_offset_0],
p_c[c_offset_1]);
}
else if constexpr(N == 4)
{
constexpr auto b_offset_0 = BDesc{}.CalculateOffset(make_tuple(k, I0));
constexpr auto b_offset_1 = BDesc{}.CalculateOffset(make_tuple(k, I1));
constexpr auto b_offset_2 = BDesc{}.CalculateOffset(make_tuple(k, I2));
constexpr auto b_offset_3 = BDesc{}.CalculateOffset(make_tuple(k, I3));
constexpr auto c_offset_0 = CDesc{}.CalculateOffset(make_tuple(m, I0));
constexpr auto c_offset_1 = CDesc{}.CalculateOffset(make_tuple(m, I1));
constexpr auto c_offset_2 = CDesc{}.CalculateOffset(make_tuple(m, I2));
constexpr auto c_offset_3 = CDesc{}.CalculateOffset(make_tuple(m, I3));
amd_assembly_outer_product_1x4(p_a[a_offset],
p_b[b_offset_0],
p_b[b_offset_1],
p_b[b_offset_2],
p_b[b_offset_3],
p_c[c_offset_0],
p_c[c_offset_1],
p_c[c_offset_2],
p_c[c_offset_3]);
}
});
});
}
#endif
__device__ static void Run(const FloatA* p_a, const FloatB* p_b, FloatC* p_c)
{
#if CK_THREADWISE_GEMM_USE_AMD_INLINE_ASM
Run_amd_asm(p_a, p_b, p_c);
#else
Run_source(p_a, p_b, p_c);
#endif
}
};
#endif
// C[M, N] += transpose(A[K, M]) * B[K, N] // C[M, N] += transpose(A[K, M]) * B[K, N]
// Element of matrix can be vectorized data // Element of matrix can be vectorized data
// Assume: // Assume:
......
composable_kernel/include/tensor_operation/threadwise_gemm_v3.hpp
View file @ 08bb4372
...@@ -6,139 +6,6 @@ ...@@ -6,139 +6,6 @@
namespace ck { namespace ck {
#if 0
template <typename Float, typename Desc>
__device__ void threadwise_matrix_set_zero_v3(Desc, Float* __restrict__ p_thread)
{
static_assert(Desc::IsKnownAtCompileTime(), "wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto desc = Desc{};
constexpr auto K = desc.GetLength(I0);
constexpr auto H = desc.GetLength(I2);
constexpr auto W = desc.GetLength(I3);
static_for<0, K, 1>{}([&](auto i) {
static_for<0, H, 1>{}([&](auto j) {
static_for<0, W, 1>{}([&](auto k) {
constexpr auto offset = desc.CalculateOffset(make_tuple(i, 0, j, k));
p_thread[offset] = Float(0);
});
});
});
}
#endif
#if 0
// C[M, N] += transpose(A[K, M]) * B[K, N]
// Element of matrix can be vectorized data
template <typename FloatA,
typename FloatB,
typename FloatC,
typename ADesc,
typename BDesc,
typename CDesc,
index_t H,
index_t W,
typename std::enable_if<ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseGemm_km_kn_mn_v3
{
__device__ static void Run(const FloatA* p_a, const FloatB* p_b, FloatC* p_c)
{
static_assert(ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto E = ADesc{}.GetLength(I0);
constexpr auto K = ADesc{}.GetLength(I1);
static_for<0, E, 1>{}([&](auto e) {
static_for<0, K, 1>{}([&](auto k) {
constexpr index_t a_offset = ADesc{}.CalculateOffset(make_tuple(e, k));
if constexpr(H == 2 && W == 2)
{
constexpr index_t b_offset_0 = BDesc{}.CalculateOffset(make_tuple(e, 0, 0, 0));
constexpr index_t b_offset_1 = BDesc{}.CalculateOffset(make_tuple(e, 0, 0, 1));
constexpr index_t b_offset_2 = BDesc{}.CalculateOffset(make_tuple(e, 0, 1, 0));
constexpr index_t b_offset_3 = BDesc{}.CalculateOffset(make_tuple(e, 0, 1, 1));
constexpr index_t c_offset_0 = CDesc{}.CalculateOffset(make_tuple(k, 0, 0, 0));
constexpr index_t c_offset_1 = CDesc{}.CalculateOffset(make_tuple(k, 0, 0, 1));
constexpr index_t c_offset_2 = CDesc{}.CalculateOffset(make_tuple(k, 0, 1, 0));
constexpr index_t c_offset_3 = CDesc{}.CalculateOffset(make_tuple(k, 0, 1, 1));
amd_assembly_outer_product_1x4(p_a[Number<a_offset>{}],
p_b[Number<b_offset_0>{}],
p_b[Number<b_offset_1>{}],
p_b[Number<b_offset_2>{}],
p_b[Number<b_offset_3>{}],
p_c[Number<c_offset_0>{}],
p_c[Number<c_offset_1>{}],
p_c[Number<c_offset_2>{}],
p_c[Number<c_offset_3>{}]);
}
else if constexpr(H == 4 && W == 1)
{
constexpr index_t b_offset_0 = BDesc{}.CalculateOffset(make_tuple(e, 0, 0, 0));
constexpr index_t b_offset_1 = BDesc{}.CalculateOffset(make_tuple(e, 0, 1, 0));
constexpr index_t b_offset_2 = BDesc{}.CalculateOffset(make_tuple(e, 0, 2, 0));
constexpr index_t b_offset_3 = BDesc{}.CalculateOffset(make_tuple(e, 0, 3, 0));
constexpr index_t c_offset_0 = CDesc{}.CalculateOffset(make_tuple(k, 0, 0, 0));
constexpr index_t c_offset_1 = CDesc{}.CalculateOffset(make_tuple(k, 0, 1, 0));
constexpr index_t c_offset_2 = CDesc{}.CalculateOffset(make_tuple(k, 0, 2, 0));
constexpr index_t c_offset_3 = CDesc{}.CalculateOffset(make_tuple(k, 0, 3, 0));
amd_assembly_outer_product_1x4(p_a[Number<a_offset>{}],
p_b[Number<b_offset_0>{}],
p_b[Number<b_offset_1>{}],
p_b[Number<b_offset_2>{}],
p_b[Number<b_offset_3>{}],
p_c[Number<c_offset_0>{}],
p_c[Number<c_offset_1>{}],
p_c[Number<c_offset_2>{}],
p_c[Number<c_offset_3>{}]);
}
else
{
static_for<0, H, 1>{}([&](auto h) {
static_for<0, W, 1>{}([&](auto w) {
constexpr index_t b_offset =
BDesc{}.CalculateOffset(make_tuple(e, 0, h, w));
constexpr index_t c_offset =
CDesc{}.CalculateOffset(make_tuple(k, 0, h, w));
#if 0
p_c[Number<c_offset>{}] += inner_product_with_conversion<FloatC>{}(p_a[Number<a_offset>{}],
p_b[Number<b_offset>{}]);
#else
amd_assembly_inner_product(p_a[Number<a_offset>{}],
p_b[Number<b_offset>{}],
p_c[Number<c_offset>{}]);
#endif
});
});
}
});
});
}
};
#else
// C[M, N] += transpose(A[K, M]) * B[K, N] // C[M, N] += transpose(A[K, M]) * B[K, N]
// Element of matrix can be vectorized data // Element of matrix can be vectorized data
// Assume: // Assume:
...@@ -277,8 +144,8 @@ struct ThreadwiseGemm_km_kn_mn_v3 ...@@ -277,8 +144,8 @@ struct ThreadwiseGemm_km_kn_mn_v3
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, h, w)); CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, h, w));
#if 0 #if 0
c_buf(Number<c_offset>{}) += inner_product_with_conversion<FloatC>{}(a_buf[Number<a_offset>{}], c_buf(Number<c_offset>{}) += inner_product_with_conversion<FloatC>{}(
b_buf[Number<b_offset>{}]); a_buf[Number<a_offset>{}], b_buf[Number<b_offset>{}]);
#else #else
amd_assembly_inner_product(a_buf[Number<a_offset>{}], amd_assembly_inner_product(a_buf[Number<a_offset>{}],
b_buf[Number<b_offset>{}], b_buf[Number<b_offset>{}],
...@@ -291,7 +158,6 @@ struct ThreadwiseGemm_km_kn_mn_v3 ...@@ -291,7 +158,6 @@ struct ThreadwiseGemm_km_kn_mn_v3
}); });
} }
}; };
#endif
} // namespace ck } // namespace ck
#endif #endif
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