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Unverified Commit 970fa3e9 authored by zjing14's avatar zjing14 Committed by GitHub
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v5r1 fusion kernels for inference (#49) 



* init

* refactor for 1x1

* rename e0_e1

* add e1 with bugs

* debug

* fixed

* fixed e1

* add timer

* imprve threadwise gemm with dot2

* add e2

* tuning

* seperate c2

* add nhwc

* restore nchwc

* clean

* opt

* fixed; tuning

* add BGlobalMoveSliceWindowStepHacks{}

* tuning

* repeat running

* adjust

* merge v5r1 nchwc

* add adaptors

* split k0 k1 in c_thread_grid

* split h and w

* remove v5r1 nhwc

* clean for pr

* remove host_conv_add

* clean code

* clean

* add dynamic support

* static mode

* test static

* add conv+add fusion

* fixed validation

* naming fix

* use activ_enum

* make static

* refactor conv_add for InMem::add

* add bias

* add conv_out

* add configurable makeddesc

* add maxpool fusion

* add maxpool host for validation

* enable static desc

* conv-only use v5r1_add

* test

* test

* for binary dumps

* fixed incorrect results due to typo

* clean

* debugging maxpool

* workaround with offset trick

* clean code

* modularize ops of fusion

* add gridwise_gemm_v3

* create seperate fusion fun

* enable dynamic mode of conv and conv+resize_add

* add dynamic mode of maxpool

* add pass by point

* add activ_type as arguments

* merge develop

* clean

* reset config to old default
Co-authored-by: default avatarChao Liu <chao.liu2@amd.com>
parent a651ea4f
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Showing with 5667 additions and 1139 deletions
composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v3.hpp
View file @ 970fa3e9
...@@ -10,99 +10,99 @@ template <index_t BlockSize, ...@@ -10,99 +10,99 @@ template <index_t BlockSize,
typename FloatA, typename FloatA,
typename FloatB, typename FloatB,
typename FloatC, typename FloatC,
typename BlockMatrixA, typename ABlockDesc_E1_K1_E2,
typename BlockMatrixB, typename BBlockDesc_E1_N_Ho_Wo_E2,
typename ThreadMatrixC, typename CThreadDesc_K_N_Ho_Wo,
index_t KPerThread,
index_t HPerThread,
index_t WPerThread,
index_t EPerThreadLoop, index_t EPerThreadLoop,
index_t ThreadGemmADataPerRead_K, index_t KPerThreadLoop>
index_t ThreadGemmBDataPerRead_W>
struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3 struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
{ {
struct MatrixIndex static constexpr auto I0 = Number<0>{};
{ static constexpr auto I1 = Number<1>{};
index_t k; static constexpr auto I2 = Number<2>{};
index_t h; static constexpr auto I3 = Number<3>{};
index_t w; static constexpr auto I4 = Number<4>{};
};
using AIndex = MultiIndex<3>;
using BIndex = MultiIndex<3>;
using CIndex = MultiIndex<4>;
static constexpr auto E1 = ABlockDesc_E1_K1_E2{}.GetLength(I0);
static constexpr auto KPerBlock = ABlockDesc_E1_K1_E2{}.GetLength(I1);
static constexpr auto E2 = ABlockDesc_E1_K1_E2{}.GetLength(I2);
// HACK: fix this @Jing Zhang static constexpr auto HoPerBlock = BBlockDesc_E1_N_Ho_Wo_E2{}.GetLength(I2);
static constexpr index_t KPerThreadSubC = 4; static constexpr auto WoPerBlock = BBlockDesc_E1_N_Ho_Wo_E2{}.GetLength(I3);
static constexpr auto KPerThread = CThreadDesc_K_N_Ho_Wo{}.GetLength(I0);
static constexpr auto HoPerThread = CThreadDesc_K_N_Ho_Wo{}.GetLength(I2);
static constexpr auto WoPerThread = CThreadDesc_K_N_Ho_Wo{}.GetLength(I3);
static constexpr auto a_thread_mtx_ = make_naive_tensor_descriptor_packed( static constexpr auto a_thread_mtx_ = make_naive_tensor_descriptor_packed(
make_tuple(Number<EPerThreadLoop>{}, Number<KPerThreadSubC>{})); make_tuple(Number<EPerThreadLoop>{}, Number<KPerThreadLoop>{}, Number<E2>{}));
static constexpr auto b_thread_mtx_ = make_naive_tensor_descriptor_packed(make_tuple( static constexpr auto b_thread_mtx_ =
Number<EPerThreadLoop>{}, Number<1>{}, Number<HPerThread>{}, Number<WPerThread>{})); make_naive_tensor_descriptor_packed(make_tuple(Number<EPerThreadLoop>{},
Number<1>{},
Number<HoPerThread>{},
Number<WoPerThread>{},
Number<E2>{}));
static constexpr auto c_thread_mtx_ = make_naive_tensor_descriptor_packed(make_tuple( static constexpr auto c_thread_mtx_ = make_naive_tensor_descriptor_packed(make_tuple(
Number<KPerThreadSubC>{}, Number<1>{}, Number<HPerThread>{}, Number<WPerThread>{})); Number<KPerThreadLoop>{}, Number<1>{}, Number<HoPerThread>{}, Number<WoPerThread>{}));
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatA,
FloatA,
BlockMatrixA,
decltype(a_thread_mtx_),
Sequence<EPerThreadLoop, KPerThreadSubC>,
Sequence<0, 1>,
1,
ThreadGemmADataPerRead_K,
1>;
__device__ BlockwiseGemmDlops_km_kn_m0m1n0n1_v3() __device__ BlockwiseGemmDlops_km_kn_m0m1n0n1_v3()
: c_thread_begin_mtx_idx_{GetBeginOfThreadMatrixC(get_thread_local_1d_id())}, : c_thread_origin_data_idx_{GetBeginOfCThreadDesc_K_N_Ho_Wo(get_thread_local_1d_id())},
a_thread_copy_{make_tuple(0, c_thread_begin_mtx_idx_.k * KPerThread)} a_thread_copy_{make_tuple(0, c_thread_origin_data_idx_[I0] * KPerThread, 0)}
{ {
static_assert(BlockMatrixA::IsKnownAtCompileTime() && static_assert(ABlockDesc_E1_K1_E2::IsKnownAtCompileTime() &&
BlockMatrixB::IsKnownAtCompileTime() && BBlockDesc_E1_N_Ho_Wo_E2::IsKnownAtCompileTime() &&
ThreadMatrixC::IsKnownAtCompileTime(), CThreadDesc_K_N_Ho_Wo::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time"); "wrong! Desc should be known at compile-time");
constexpr auto I0 = Number<0>{}; static_assert(
constexpr auto I1 = Number<1>{}; ABlockDesc_E1_K1_E2{}.GetLength(I0) == BBlockDesc_E1_N_Ho_Wo_E2{}.GetLength(I0) &&
constexpr auto I2 = Number<2>{}; ABlockDesc_E1_K1_E2{}.GetLength(I2) == BBlockDesc_E1_N_Ho_Wo_E2{}.GetLength(I4),
constexpr auto I3 = Number<3>{}; "wrong! E dimension not consistent\n");
static_assert(BlockMatrixA{}.GetLength(I0) == BlockMatrixB{}.GetLength(I0),
"wrong! K dimension not consistent\n");
constexpr index_t K = BlockMatrixA{}.GetLength(I1); // A is transposed static_assert(E1 % EPerThreadLoop == 0, "");
constexpr index_t H = BlockMatrixB{}.GetLength(I2); static_assert(KPerThread % KPerThreadLoop == 0, "");
constexpr index_t W = BlockMatrixB{}.GetLength(I3);
static_assert(K % KPerThread == 0 && H % HPerThread == 0 && W % WPerThread == 0, static_assert(KPerBlock % KPerThread == 0 && HoPerBlock % HoPerThread == 0 &&
WoPerBlock % WoPerThread == 0,
"wrong! Cannot evenly divide work among\n"); "wrong! Cannot evenly divide work among\n");
constexpr auto KThreadCluster = K / KPerThread; constexpr auto KThreadCluster = KPerBlock / KPerThread;
constexpr auto HThreadCluster = H / HPerThread; constexpr auto HThreadCluster = HoPerBlock / HoPerThread;
constexpr auto WThreadCluster = W / WPerThread; constexpr auto WThreadCluster = WoPerBlock / WoPerThread;
static_assert(BlockSize == KThreadCluster * HThreadCluster * WThreadCluster, static_assert(BlockSize == KThreadCluster * HThreadCluster * WThreadCluster,
"wrong! wrong blocksize\n"); "wrong! wrong blocksize\n");
} }
__device__ static constexpr auto GetThreadMatrixCLengths() __device__ static constexpr auto GetCThreadDesc_K_N_Ho_WoLengths()
{ {
return Sequence<KPerThread, 1, HPerThread, WPerThread>{}; return Sequence<KPerThread, I1, HoPerThread, WoPerThread>{};
} }
__device__ static MatrixIndex GetBeginOfThreadMatrixC(index_t thread_id) __device__ static CIndex GetBeginOfCThreadDesc_K_N_Ho_Wo(index_t thread_id)
{ {
constexpr index_t H = BlockMatrixB{}.GetLength(Number<2>{}); constexpr auto K0 = KPerBlock / KPerThread;
constexpr index_t W = BlockMatrixB{}.GetLength(Number<3>{}); constexpr auto N0 = I1;
constexpr auto H0 = HoPerBlock / HoPerThread;
constexpr auto num_w_threads = W / WPerThread; constexpr auto W0 = WoPerBlock / WoPerThread;
constexpr auto num_h_threads = H / HPerThread;
constexpr auto num_hw_threads = num_w_threads * num_h_threads; constexpr auto c_threadid_to_k_n_h_w_thread_cluster_adaptor =
make_single_stage_tensor_adaptor(
index_t k_thread_id = thread_id / num_hw_threads; make_tuple(make_merge_transform(make_tuple(K0, N0, H0, W0))),
index_t hw_thread_id = thread_id % num_hw_threads; make_tuple(Sequence<0, 1, 2, 3>{}),
make_tuple(Sequence<0>{}));
index_t h_thread_id = hw_thread_id / num_w_threads;
index_t w_thread_id = hw_thread_id % num_w_threads; const auto c_k_n_h_w_thread_cluster_idx =
c_threadid_to_k_n_h_w_thread_cluster_adaptor.CalculateBottomIndex(
return MatrixIndex{k_thread_id, h_thread_id, w_thread_id}; make_multi_index(thread_id));
return c_k_n_h_w_thread_cluster_idx;
} }
template <typename ABlockBuffer, typename BThreadBuffer, typename CThreadBuffer> template <typename ABlockBuffer, typename BThreadBuffer, typename CThreadBuffer>
...@@ -116,19 +116,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3 ...@@ -116,19 +116,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
is_same<remove_cvref_t<typename CThreadBuffer::type>, remove_cvref_t<FloatC>>::value && is_same<remove_cvref_t<typename CThreadBuffer::type>, remove_cvref_t<FloatC>>::value &&
"wrong! inconsistent type"); "wrong! inconsistent type");
constexpr auto I0 = Number<0>{}; constexpr auto a_block_mtx = ABlockDesc_E1_K1_E2{};
constexpr auto a_block_mtx = BlockMatrixA{};
constexpr auto EPerBlock = a_block_mtx.GetLength(I0);
// HACK: fix this @Jing Zhang
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 buffer for GEMM // thread A buffer for GEMM
StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatA, a_thread_mtx_.GetElementSpaceSize(), true> StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatA, a_thread_mtx_.GetElementSpaceSize(), true>
...@@ -139,42 +127,46 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3 ...@@ -139,42 +127,46 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
FloatC, FloatC,
decltype(a_thread_mtx_), decltype(a_thread_mtx_),
decltype(b_thread_mtx_), decltype(b_thread_mtx_),
decltype(c_thread_mtx_), decltype(c_thread_mtx_)>{};
HoPerThreadSubC,
WoPerThreadSubC>{};
static_for<0, EPerBlock, EPerThreadLoop>{}([&](auto e_begin) { static_for<0, E1, EPerThreadLoop>{}([&](auto e_begin) {
static_for<0, KPerThread, KPerThreadSubC>{}([&](auto k_begin) { static_for<0, KPerThread, KPerThreadLoop>{}([&](auto k_begin) {
a_thread_copy_.Run(a_block_mtx, a_thread_copy_.Run(a_block_mtx,
make_tuple(e_begin, k_begin), make_tuple(e_begin, k_begin, I0),
a_block_buf, a_block_buf,
a_thread_mtx_, a_thread_mtx_,
make_tuple(I0, I0), make_tuple(I0, I0, I0),
a_thread_buf); a_thread_buf);
static_for<0, HPerThread, HoPerThreadSubC>{}([&](auto h_begin) {
static_for<0, WPerThread, WoPerThreadSubC>{}([&](auto w_begin) {
threadwise_gemm.Run(a_thread_buf, threadwise_gemm.Run(a_thread_buf,
make_tuple(I0, I0), make_tuple(I0, I0, I0),
b_thread_buf, b_thread_buf,
make_tuple(e_begin, I0, h_begin, w_begin), make_tuple(e_begin, I0, I0, I0, I0),
c_thread_buf, c_thread_buf,
make_tuple(k_begin, I0, h_begin, w_begin)); make_tuple(k_begin, I0, I0, I0));
});
});
}); });
}); });
} }
template <typename ABlockSliceMoveStepIdx> template <typename ABlockSliceMoveStepIdx>
__device__ void MoveASliceWindow(const BlockMatrixA&, __device__ void MoveABlockSliceWindow(const ABlockSliceMoveStepIdx& a_block_slice_move_step_idx)
const ABlockSliceMoveStepIdx& a_block_slice_move_step_idx)
{ {
a_thread_copy_.MoveSrcSliceWindow(BlockMatrixA{}, a_block_slice_move_step_idx); a_thread_copy_.MoveSrcSliceWindow(ABlockDesc_E1_K1_E2{}, a_block_slice_move_step_idx);
} }
private: private:
MatrixIndex c_thread_begin_mtx_idx_; using AThreadCopy =
ThreadwiseTensorSliceTransfer_v4<FloatA,
FloatA,
ABlockDesc_E1_K1_E2,
decltype(a_thread_mtx_),
Sequence<EPerThreadLoop, KPerThreadLoop, E2>,
Sequence<0, 1, 2>,
2,
E2,
E2>;
CIndex c_thread_origin_data_idx_;
AThreadCopy a_thread_copy_; AThreadCopy a_thread_copy_;
}; };
......
composable_kernel/include/tensor_operation/threadwise_gemm_dlops_v3.hpp
View file @ 970fa3e9
...@@ -9,21 +9,22 @@ namespace ck { ...@@ -9,21 +9,22 @@ namespace ck {
// 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:
// 1. ADesc, BDesc, CDesc are known at compile-time // 1. AThreadDesc_E1_K_E2, BThreadDesc_E1_N_Ho_Wo_E2, CThreadDesc_K_N_Ho_Wo are known at
// compile-time
// 2. AOriginIdx, BOriginIdx, COriginIdx are known at compile-time // 2. AOriginIdx, BOriginIdx, COriginIdx are known at compile-time
template <typename FloatA, template <typename FloatA,
typename FloatB, typename FloatB,
typename FloatC, typename FloatC,
typename ADesc, typename AThreadDesc_E1_K_E2,
typename BDesc, typename BThreadDesc_E1_N_Ho_Wo_E2,
typename CDesc, typename CThreadDesc_K_N_Ho_Wo,
index_t H, typename enable_if<AThreadDesc_E1_K_E2::IsKnownAtCompileTime() &&
index_t W, BThreadDesc_E1_N_Ho_Wo_E2::IsKnownAtCompileTime() &&
typename enable_if<ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() && CThreadDesc_K_N_Ho_Wo::IsKnownAtCompileTime(),
CDesc::IsKnownAtCompileTime(),
bool>::type = false> bool>::type = false>
struct ThreadwiseGemmDlops_km_kn_mn_v3 struct ThreadwiseGemmDlops_km_kn_mn_v3
{ {
template <typename ABuffer, template <typename ABuffer,
typename AOriginIdx, typename AOriginIdx,
typename BBuffer, typename BBuffer,
...@@ -37,8 +38,10 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3 ...@@ -37,8 +38,10 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3
CBuffer& c_buf, CBuffer& c_buf,
COriginIdx) COriginIdx)
{ {
static_assert(ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(), static_assert(AThreadDesc_E1_K_E2::IsKnownAtCompileTime() &&
BThreadDesc_E1_N_Ho_Wo_E2::IsKnownAtCompileTime() &&
CThreadDesc_K_N_Ho_Wo::IsKnownAtCompileTime(),
"wrong! Desc should be known at compile-time"); "wrong! Desc should be known at compile-time");
static_assert(is_known_at_compile_time<remove_cvref_t<AOriginIdx>>::value && static_assert(is_known_at_compile_time<remove_cvref_t<AOriginIdx>>::value &&
...@@ -54,102 +57,107 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3 ...@@ -54,102 +57,107 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3
constexpr auto I0 = Number<0>{}; constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{}; constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto E = ADesc{}.GetLength(I0); constexpr auto E1 = AThreadDesc_E1_K_E2{}.GetLength(I0);
constexpr auto K = ADesc{}.GetLength(I1); constexpr auto K = AThreadDesc_E1_K_E2{}.GetLength(I1);
constexpr auto E2 = AThreadDesc_E1_K_E2{}.GetLength(I2);
constexpr auto Ho = BThreadDesc_E1_N_Ho_Wo_E2{}.GetLength(I2);
constexpr auto Wo = BThreadDesc_E1_N_Ho_Wo_E2{}.GetLength(I3);
constexpr auto a_origin_idx = to_multi_index(AOriginIdx{}); constexpr auto a_origin_idx = to_multi_index(AOriginIdx{});
constexpr auto b_origin_idx = to_multi_index(BOriginIdx{}); constexpr auto b_origin_idx = to_multi_index(BOriginIdx{});
constexpr auto c_origin_idx = to_multi_index(COriginIdx{}); constexpr auto c_origin_idx = to_multi_index(COriginIdx{});
static_for<0, E, 1>{}([&](auto e) { if constexpr((Ho % 2 == 0) && (Wo % 2 == 0))
{
constexpr auto SubHW = 2;
static_for<0, K, 1>{}([&](auto k) { static_for<0, K, 1>{}([&](auto k) {
constexpr index_t a_offset = static_for<0, Ho, SubHW>{}([&](auto h) {
ADesc{}.CalculateOffset(a_origin_idx + make_tuple(e, k)); static_for<0, Wo, SubHW>{}([&](auto w) {
static_for<0, E1, 1>{}([&](auto e1) {
static_for<0, E2, 1>{}([&](auto e2) {
constexpr index_t a_offset = AThreadDesc_E1_K_E2{}.CalculateOffset(
a_origin_idx + make_tuple(e1, k, e2));
if constexpr(H == 2 && W == 2) constexpr index_t b0_offset =
{ BThreadDesc_E1_N_Ho_Wo_E2{}.CalculateOffset(
constexpr index_t b_offset_0 = b_origin_idx + make_tuple(e1, 0, h, w, e2));
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 0, 0));
constexpr index_t b_offset_1 =
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 0, 1));
constexpr index_t b_offset_2 =
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 1, 0));
constexpr index_t b_offset_3 =
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 1, 1));
constexpr index_t c_offset_0 =
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 0, 0));
constexpr index_t c_offset_1 =
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 0, 1));
constexpr index_t c_offset_2 =
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 1, 0));
constexpr index_t c_offset_3 =
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 1, 1));
amd_assembly_outer_product_1x4(a_buf[Number<a_offset>{}], constexpr index_t b1_offset =
b_buf[Number<b_offset_0>{}], BThreadDesc_E1_N_Ho_Wo_E2{}.CalculateOffset(
b_buf[Number<b_offset_1>{}], b_origin_idx + make_tuple(e1, 0, h, w + 1, e2));
b_buf[Number<b_offset_2>{}],
b_buf[Number<b_offset_3>{}], constexpr index_t b2_offset =
c_buf(Number<c_offset_0>{}), BThreadDesc_E1_N_Ho_Wo_E2{}.CalculateOffset(
c_buf(Number<c_offset_1>{}), b_origin_idx + make_tuple(e1, 0, h + 1, w, e2));
c_buf(Number<c_offset_2>{}),
c_buf(Number<c_offset_3>{})); constexpr index_t b3_offset =
} BThreadDesc_E1_N_Ho_Wo_E2{}.CalculateOffset(
else if constexpr(H == 4 && W == 1) b_origin_idx + make_tuple(e1, 0, h + 1, w + 1, e2));
{
constexpr index_t b_offset_0 = constexpr index_t c0_offset =
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 0, 0)); CThreadDesc_K_N_Ho_Wo{}.CalculateOffset(c_origin_idx +
constexpr index_t b_offset_1 = make_tuple(k, 0, h, w));
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 1, 0));
constexpr index_t b_offset_2 = constexpr index_t c1_offset =
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 2, 0)); CThreadDesc_K_N_Ho_Wo{}.CalculateOffset(
constexpr index_t b_offset_3 = c_origin_idx + make_tuple(k, 0, h, w + 1));
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, 3, 0));
constexpr index_t c2_offset =
constexpr index_t c_offset_0 = CThreadDesc_K_N_Ho_Wo{}.CalculateOffset(
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 0, 0)); c_origin_idx + make_tuple(k, 0, h + 1, w));
constexpr index_t c_offset_1 =
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 1, 0)); constexpr index_t c3_offset =
constexpr index_t c_offset_2 = CThreadDesc_K_N_Ho_Wo{}.CalculateOffset(
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 2, 0)); c_origin_idx + make_tuple(k, 0, h + 1, w + 1));
constexpr index_t c_offset_3 =
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, 3, 0));
amd_assembly_outer_product_1x4(a_buf[Number<a_offset>{}], amd_assembly_outer_product_1x4(a_buf[Number<a_offset>{}],
b_buf[Number<b_offset_0>{}], b_buf[Number<b0_offset>{}],
b_buf[Number<b_offset_1>{}], b_buf[Number<b1_offset>{}],
b_buf[Number<b_offset_2>{}], b_buf[Number<b2_offset>{}],
b_buf[Number<b_offset_3>{}], b_buf[Number<b3_offset>{}],
c_buf(Number<c_offset_0>{}), c_buf(Number<c0_offset>{}),
c_buf(Number<c_offset_1>{}), c_buf(Number<c1_offset>{}),
c_buf(Number<c_offset_2>{}), c_buf(Number<c2_offset>{}),
c_buf(Number<c_offset_3>{})); c_buf(Number<c3_offset>{}));
});
});
});
});
});
} }
else else
{ {
static_for<0, H, 1>{}([&](auto h) {
static_for<0, W, 1>{}([&](auto w) { static_for<0, K, 1>{}([&](auto k) {
static_for<0, Ho, 1>{}([&](auto h) {
static_for<0, Wo, 1>{}([&](auto w) {
static_for<0, E1, 1>{}([&](auto e1) {
static_for<0, E2, 1>{}([&](auto e2) {
constexpr index_t a_offset = AThreadDesc_E1_K_E2{}.CalculateOffset(
a_origin_idx + make_tuple(e1, k, e2));
constexpr index_t b_offset = constexpr index_t b_offset =
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, h, w)); BThreadDesc_E1_N_Ho_Wo_E2{}.CalculateOffset(
b_origin_idx + make_tuple(e1, 0, h, w, e2));
constexpr index_t c_offset = constexpr index_t c_offset =
CDesc{}.CalculateOffset(c_origin_idx + make_tuple(k, 0, h, w)); CThreadDesc_K_N_Ho_Wo{}.CalculateOffset(c_origin_idx +
make_tuple(k, 0, h, w));
#if 0 inner_product<FloatA, FloatB, FloatC>(a_buf[Number<a_offset>{}],
c_buf(Number<c_offset>{}) += inner_product_with_conversion<FloatC>{}(
a_buf[Number<a_offset>{}], b_buf[Number<b_offset>{}]);
#else
amd_assembly_inner_product(a_buf[Number<a_offset>{}],
b_buf[Number<b_offset>{}], b_buf[Number<b_offset>{}],
c_buf(Number<c_offset>{})); c_buf(Number<c_offset>{}));
#endif
}); });
}); });
}
}); });
}); });
});
}
} }
}; };
......
composable_kernel/include/tensor_operation/threadwise_tensor_slice_transfer.hpp
View file @ 970fa3e9
...@@ -217,6 +217,22 @@ struct ThreadwiseTensorSliceTransfer_v1r3 ...@@ -217,6 +217,22 @@ struct ThreadwiseTensorSliceTransfer_v1r3
is_dst_valid, is_dst_valid,
dst_vector.template AsType<dst_vector_t>()[Number<0>{}]); dst_vector.template AsType<dst_vector_t>()[Number<0>{}]);
} }
else if constexpr(DstInMemOp == InMemoryDataOperationEnum_t::Add)
{
typename vector_type_maker<DstData, DstScalarPerVector>::type tmp;
tmp.template AsType<dst_vector_t>()(Number<0>{}) =
dst_buf.template Get<dst_vector_t>(dst_coord_.GetOffset(), is_dst_valid);
static_for<0, DstScalarPerVector, 1>{}([&](auto t) {
dst_vector.template AsType<DstData>()(t) += tmp.template AsType<DstData>()[t];
});
dst_buf.template Set<dst_vector_t>(
dst_coord_.GetOffset(),
is_dst_valid,
dst_vector.template AsType<dst_vector_t>()[Number<0>{}]);
}
constexpr auto move_on_dim = [&]() constexpr constexpr auto move_on_dim = [&]() constexpr
{ {
...@@ -666,6 +682,25 @@ struct ThreadwiseTensorSliceTransfer_v2 ...@@ -666,6 +682,25 @@ struct ThreadwiseTensorSliceTransfer_v2
move_tensor_coordinate(src_desc, src_coord_, adjusted_step); move_tensor_coordinate(src_desc, src_coord_, adjusted_step);
} }
// src_slice_origin_step_idx need to be known at compile-time, for performance reason
template <typename SrcMoveSliceWindowStepHack>
__device__ void
MoveSrcSliceWindow(const SrcDesc& src_desc,
const Index& src_slice_origin_step_idx,
const SrcMoveSliceWindowStepHack& src_move_slice_window_step_hack)
{
// if src coord was not reset by RunRead(), then need to adjust the step here
const auto adjusted_step_idx =
SrcResetCoordinateAfterRun ? src_slice_origin_step_idx
: src_slice_origin_step_idx + GetSrcCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step = make_tensor_coordinate_step(
src_desc, adjusted_step_idx, src_move_slice_window_step_hack);
move_tensor_coordinate(src_desc, src_coord_, adjusted_step);
}
private: private:
SrcCoord src_coord_; SrcCoord src_coord_;
}; // namespace ck }; // namespace ck
......
composable_kernel/include/utility/amd_buffer_addressing.hpp
View file @ 970fa3e9
...@@ -591,6 +591,7 @@ __device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src ...@@ -591,6 +591,7 @@ __device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src
} }
else if constexpr(N == 8) else if constexpr(N == 8)
{ {
#if 0
vector_type<half_t, 8> tmp{src_thread_data}; vector_type<half_t, 8> tmp{src_thread_data};
llvm_amdgcn_raw_buffer_store_fp16x4(tmp.AsType<half4_t>()[Number<0>{}], llvm_amdgcn_raw_buffer_store_fp16x4(tmp.AsType<half4_t>()[Number<0>{}],
...@@ -604,6 +605,13 @@ __device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src ...@@ -604,6 +605,13 @@ __device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src
dst_thread_addr_offset, dst_thread_addr_offset,
dst_wave_addr_offset + 4 * sizeof(half_t), dst_wave_addr_offset + 4 * sizeof(half_t),
0); 0);
#else
llvm_amdgcn_raw_buffer_store_fp32x4(as_type<float4_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
0);
#endif
} }
} }
else if constexpr(is_same<T, ushort>::value) else if constexpr(is_same<T, ushort>::value)
......
composable_kernel/include/utility/config.hpp
View file @ 970fa3e9
...@@ -96,6 +96,7 @@ ...@@ -96,6 +96,7 @@
// pass tensor descriptor by value or void* // pass tensor descriptor by value or void*
#define CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE 1 #define CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE 1
#define CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER 0 #define CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER 0
#define CK_EXPERIMENTAL_STATIC_TENSOR_DESCRIPTOR 0
// merge transformation use magic number division // merge transformation use magic number division
#define CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION 1 #define CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION 1
...@@ -128,7 +129,15 @@ namespace ck { ...@@ -128,7 +129,15 @@ namespace ck {
enum InMemoryDataOperationEnum_t enum InMemoryDataOperationEnum_t
{ {
Set, Set,
AtomicAdd AtomicAdd,
Add
};
enum ActivTypeEnum_t
{
None = 0,
LeakyRelu,
Sigmoid
}; };
// index type // index type
......
host/driver_offline/CMakeLists.txt
View file @ 970fa3e9
...@@ -13,16 +13,25 @@ include_directories(BEFORE ...@@ -13,16 +13,25 @@ include_directories(BEFORE
) )
set(CONV_FWD_DRIVER_OFFLINE_SOURCE src/conv_fwd_driver_offline.cpp) set(CONV_FWD_DRIVER_OFFLINE_SOURCE src/conv_fwd_driver_offline.cpp)
set(CONV_FWD_DRIVER_OFFLINE_NCHWC_SOURCE src/conv_fwd_driver_offline_nchwc.cpp)
set(CONV_ADD_FWD_DRIVER_OFFLINE_NCHWC_SOURCE src/conv_add_fwd_driver_offline_nchwc.cpp)
set(CONV_MAXPOOL_FWD_DRIVER_OFFLINE_NCHWC_SOURCE src/conv_maxpool_fwd_driver_offline_nchwc.cpp)
set(CONV_BWD_DRIVER_OFFLINE_SOURCE src/conv_bwd_driver_offline.cpp) set(CONV_BWD_DRIVER_OFFLINE_SOURCE src/conv_bwd_driver_offline.cpp)
set(CONV_WRW_DRIVER_OFFLINE_SOURCE src/conv_wrw_driver_offline.cpp) set(CONV_WRW_DRIVER_OFFLINE_SOURCE src/conv_wrw_driver_offline.cpp)
set(GEMM_DRIVER_OFFLINE_SOURCE src/gemm_driver_offline.cpp) set(GEMM_DRIVER_OFFLINE_SOURCE src/gemm_driver_offline.cpp)
add_executable(conv_fwd_driver_offline ${CONV_FWD_DRIVER_OFFLINE_SOURCE}) add_executable(conv_fwd_driver_offline ${CONV_FWD_DRIVER_OFFLINE_SOURCE})
add_executable(conv_fwd_driver_offline_nchwc ${CONV_FWD_DRIVER_OFFLINE_NCHWC_SOURCE})
add_executable(conv_add_fwd_driver_offline_nchwc ${CONV_ADD_FWD_DRIVER_OFFLINE_NCHWC_SOURCE})
add_executable(conv_maxpool_fwd_driver_offline_nchwc ${CONV_MAXPOOL_FWD_DRIVER_OFFLINE_NCHWC_SOURCE})
add_executable(conv_bwd_driver_offline ${CONV_BWD_DRIVER_OFFLINE_SOURCE}) add_executable(conv_bwd_driver_offline ${CONV_BWD_DRIVER_OFFLINE_SOURCE})
add_executable(conv_wrw_driver_offline ${CONV_WRW_DRIVER_OFFLINE_SOURCE}) add_executable(conv_wrw_driver_offline ${CONV_WRW_DRIVER_OFFLINE_SOURCE})
add_executable(gemm_driver_offline ${GEMM_DRIVER_OFFLINE_SOURCE}) add_executable(gemm_driver_offline ${GEMM_DRIVER_OFFLINE_SOURCE})
target_link_libraries(conv_fwd_driver_offline PRIVATE host_tensor) target_link_libraries(conv_fwd_driver_offline PRIVATE host_tensor)
target_link_libraries(conv_fwd_driver_offline_nchwc PRIVATE host_tensor)
target_link_libraries(conv_add_fwd_driver_offline_nchwc PRIVATE host_tensor)
target_link_libraries(conv_maxpool_fwd_driver_offline_nchwc PRIVATE host_tensor)
target_link_libraries(conv_bwd_driver_offline PRIVATE host_tensor) target_link_libraries(conv_bwd_driver_offline PRIVATE host_tensor)
target_link_libraries(conv_wrw_driver_offline PRIVATE host_tensor) target_link_libraries(conv_wrw_driver_offline PRIVATE host_tensor)
target_link_libraries(gemm_driver_offline PRIVATE host_tensor) target_link_libraries(gemm_driver_offline PRIVATE host_tensor)
host/driver_offline/include/device_convolution_add_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp 0 → 100644
View file @ 970fa3e9
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_convolution_add_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
ck::ActivTypeEnum_t activ_type,
typename InLengths,
typename WeiLengths,
typename AddLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_add_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1(
const InLengths& in_n_c0_hi_wi_c1_lengths,
const WeiLengths& wei_k_c0_y_x_c1_lengths,
const AddLengths& add_n_k0_hox2_wox2_k1_lengths,
const OutLengths& out_n_k0_ho_wo_k1_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c0_hi_wi_c1,
const Tensor<TInWei>& wei_k_c0_y_x_c1,
const Tensor<TOut>& bias_k0_k1,
const Tensor<TOut>& add_n_k0_hox2_wox2_k1,
Tensor<TOut>& add_n_k0_hox2_wox2_k1_out,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
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>{};
const auto N = out_n_k0_ho_wo_k1_lengths[I0];
const auto K0 = out_n_k0_ho_wo_k1_lengths[I1];
const auto Ho = out_n_k0_ho_wo_k1_lengths[I2];
const auto Wo = out_n_k0_ho_wo_k1_lengths[I3];
const auto K1 = out_n_k0_ho_wo_k1_lengths[I4];
const auto C0 = in_n_c0_hi_wi_c1_lengths[I1];
const auto Hi = in_n_c0_hi_wi_c1_lengths[I2];
const auto Wi = in_n_c0_hi_wi_c1_lengths[I3];
const auto C1 = in_n_c0_hi_wi_c1_lengths[I4];
const auto K = wei_k_c0_y_x_c1_lengths[I0];
const auto Y = wei_k_c0_y_x_c1_lengths[I2];
const auto X = wei_k_c0_y_x_c1_lengths[I3];
const auto Hox2 = add_n_k0_hox2_wox2_k1_lengths[I2];
const auto Wox2 = add_n_k0_hox2_wox2_k1_lengths[I3];
DeviceMem in_n_c0_hi_wi_c1_device_buf(sizeof(TInWei) *
in_n_c0_hi_wi_c1.mDesc.GetElementSpace());
DeviceMem wei_k_c0_y_x_c1_device_buf(sizeof(TInWei) * wei_k_c0_y_x_c1.mDesc.GetElementSpace());
DeviceMem bias_k0_k1_device_buf(sizeof(TOut) * bias_k0_k1.mDesc.GetElementSpace());
DeviceMem add_n_k0_hox2_wox2_k1_device_buf(sizeof(TOut) *
add_n_k0_hox2_wox2_k1.mDesc.GetElementSpace());
in_n_c0_hi_wi_c1_device_buf.ToDevice(in_n_c0_hi_wi_c1.mData.data());
wei_k_c0_y_x_c1_device_buf.ToDevice(wei_k_c0_y_x_c1.mData.data());
bias_k0_k1_device_buf.ToDevice(bias_k0_k1.mData.data());
add_n_k0_hox2_wox2_k1_device_buf.ToDevice(add_n_k0_hox2_wox2_k1.mData.data());
constexpr index_t InWeiVectorSize = 8;
if(C1 % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
#if 0
constexpr index_t BlockSize = 256;
constexpr index_t KPerBlock = 32;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 64;
constexpr index_t E1 = C0 * 9;
constexpr index_t E2 = 1;
constexpr index_t E1PerBlock = C0;
constexpr index_t KPerThread = 16;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 = Sequence<1, E1PerBlock, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = K1;
#elif 1
constexpr auto BlockSize = 64;
constexpr auto KPerBlock = 8;
constexpr auto HoPerBlock = 8;
constexpr auto WoPerBlock = 32;
constexpr auto E1 = 2 * 9;
constexpr auto E2 = 1;
constexpr auto K2 = 2;
constexpr auto E1PerBlock = 2;
constexpr auto KPerThread = KPerBlock;
constexpr auto HoPerThread = 2;
constexpr auto WoPerThread = 2;
constexpr auto EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 =
Sequence<1, E1PerBlock, 1, KPerBlock, 1>;
constexpr auto ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr auto ABlockTransferDstScalarPerVector_E2 = E2;
constexpr auto BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr auto CThreadTransferDstScalarPerVector_K = InWeiVectorSize;
#endif
const auto in_n_c0_hi_wi_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2));
const auto wei_k_c0_y_x_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(K, C0, Y, X, E2));
const auto add_n_k0_hox2_wox2_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Hox2, Wox2, K1));
const auto out_n_k0_ho_wo_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1));
constexpr auto conv_driver =
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_add<
BlockSize,
typename vector_type<TInWei, InWeiVectorSize>::type,
TAcc,
TOut,
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
BThreadTransferSrcScalarPerVector_E2,
CThreadTransferDstScalarPerVector_K,
activ_type>{};
std::cerr << "conv_bias_activ_resize_add_input_"
<< "n" << N << "c" << C0 << "h" << Hi << "w" << Wi << "c" << C1 << "_filter_k" << K
<< "c" << C0 << "y" << Y << "x" << X << "c" << C1 << "_addout_n" << N << "k" << K0
<< "h" << Ho * 2 << "w" << Wo * 2 << "k" << K1 << std::endl;
for(int i = 0; i < 5; i++)
{
const auto ave_time =
conv_driver.Run(wei_k_c0_y_x_c1_desc,
in_n_c0_hi_wi_c1_desc,
out_n_k0_ho_wo_k1_desc,
add_n_k0_hox2_wox2_k1_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
wei_k_c0_y_x_c1_device_buf.GetDeviceBuffer()),
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
in_n_c0_hi_wi_c1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(bias_k0_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(add_n_k0_hox2_wox2_k1_device_buf.GetDeviceBuffer()),
nrepeat);
{
float perf = static_cast<float>(std::size_t(2) * N * K * Ho * Wo * C0 * C1 * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
}
add_n_k0_hox2_wox2_k1_device_buf.ToDevice(add_n_k0_hox2_wox2_k1.mData.data());
conv_driver.Run(wei_k_c0_y_x_c1_desc,
in_n_c0_hi_wi_c1_desc,
out_n_k0_ho_wo_k1_desc,
add_n_k0_hox2_wox2_k1_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
wei_k_c0_y_x_c1_device_buf.GetDeviceBuffer()),
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
in_n_c0_hi_wi_c1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(bias_k0_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(add_n_k0_hox2_wox2_k1_device_buf.GetDeviceBuffer()),
0);
add_n_k0_hox2_wox2_k1_device_buf.FromDevice(add_n_k0_hox2_wox2_k1_out.mData.data());
}
host/driver_offline/include/device_convolution_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp 0 → 100644
View file @ 970fa3e9
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_convolution_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
ck::ActivTypeEnum_t activ_type,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1(
const InLengths& in_n_c0_hi_wi_c1_lengths,
const WeiLengths& wei_k_c0_y_x_c1_lengths,
const OutLengths& out_n_k0_ho_wo_k1_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c0_hi_wi_c1,
const Tensor<TInWei>& wei_k_c0_y_x_c1,
const Tensor<TOut>& bias_k0_k1,
Tensor<TOut>& out_n_k0_ho_wo_k1,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
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>{};
const auto N = out_n_k0_ho_wo_k1_lengths[I0];
const auto K0 = out_n_k0_ho_wo_k1_lengths[I1];
const auto Ho = out_n_k0_ho_wo_k1_lengths[I2];
const auto Wo = out_n_k0_ho_wo_k1_lengths[I3];
const auto K1 = out_n_k0_ho_wo_k1_lengths[I4];
const auto C0 = in_n_c0_hi_wi_c1_lengths[I1];
const auto Hi = in_n_c0_hi_wi_c1_lengths[I2];
const auto Wi = in_n_c0_hi_wi_c1_lengths[I3];
const auto C1 = in_n_c0_hi_wi_c1_lengths[I4];
const auto K = wei_k_c0_y_x_c1_lengths[I0];
const auto Y = wei_k_c0_y_x_c1_lengths[I2];
const auto X = wei_k_c0_y_x_c1_lengths[I3];
DeviceMem in_n_c0_hi_wi_c1_device_buf(sizeof(TInWei) *
in_n_c0_hi_wi_c1.mDesc.GetElementSpace());
DeviceMem wei_k_c0_y_x_c1_device_buf(sizeof(TInWei) * wei_k_c0_y_x_c1.mDesc.GetElementSpace());
DeviceMem bias_k0_k1_device_buf(sizeof(TOut) * bias_k0_k1.mDesc.GetElementSpace());
DeviceMem out_n_k0_ho_wo_k1_device_buf(sizeof(TOut) *
out_n_k0_ho_wo_k1.mDesc.GetElementSpace());
in_n_c0_hi_wi_c1_device_buf.ToDevice(in_n_c0_hi_wi_c1.mData.data());
wei_k_c0_y_x_c1_device_buf.ToDevice(wei_k_c0_y_x_c1.mData.data());
bias_k0_k1_device_buf.ToDevice(bias_k0_k1.mData.data());
constexpr index_t InWeiVectorSize = 8;
if(C1 % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
#if 0
constexpr index_t BlockSize = 256;
constexpr index_t KPerBlock = 32;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 64;
constexpr index_t E1 = C0 * 9;
constexpr index_t E2 = 1;
constexpr index_t E1PerBlock = C0;
constexpr index_t KPerThread = 16;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 = Sequence<1, E1PerBlock, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = K1;
#elif 1
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 8;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 32;
constexpr index_t E1 = 2 * 9;
constexpr index_t E2 = 1;
constexpr index_t K2 = 2;
constexpr index_t E1PerBlock = 2;
constexpr index_t KPerThread = KPerBlock;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 =
Sequence<1, E1PerBlock, 1, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = InWeiVectorSize;
#endif
if(KPerThread % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
const auto in_n_c0_hi_wi_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2));
const auto wei_k_c0_y_x_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(K, C0, Y, X, E2));
const auto out_n_k0_ho_wo_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1));
constexpr auto conv_driver =
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_outpad<
BlockSize,
typename vector_type<TInWei, InWeiVectorSize>::type,
TAcc,
TOut,
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
BThreadTransferSrcScalarPerVector_E2,
CThreadTransferDstScalarPerVector_K,
activ_type>{};
std::cerr << "conv_bias_activ_input_"
<< "n" << N << "c" << C0 << "h" << Hi << "w" << Wi << "c" << C1 << "_filter_k" << K
<< "c" << C0 << "y" << Y << "x" << X << "c" << C1 << "_convout_n" << N << "k" << K0
<< "h" << Ho << "w" << Wo << "k" << K1 << std::endl;
for(int i = 0; i < 5; i++)
{
const auto ave_time =
conv_driver.Run(wei_k_c0_y_x_c1_desc,
in_n_c0_hi_wi_c1_desc,
out_n_k0_ho_wo_k1_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
wei_k_c0_y_x_c1_device_buf.GetDeviceBuffer()),
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
in_n_c0_hi_wi_c1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(bias_k0_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k0_ho_wo_k1_device_buf.GetDeviceBuffer()),
nrepeat);
{
float perf = static_cast<float>(std::size_t(2) * N * K * Ho * Wo * C0 * C1 * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
}
out_n_k0_ho_wo_k1_device_buf.FromDevice(out_n_k0_ho_wo_k1.mData.data());
}
host/driver_offline/include/device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ a651ea4f
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "driver_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw_outpad.hpp"
template <typename TInWei,
ck::index_t InWeiVectorSize,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
ck::index_t /* nrepeat */)
{
using namespace ck;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = out_n_k_ho_wo_lengths[I0];
const auto K = out_n_k_ho_wo_lengths[I1];
const auto C = wei_k_c_y_x_lengths[I1];
const auto Hi = in_n_c_hi_wi_lengths[I2];
const auto Wi = in_n_c_hi_wi_lengths[I3];
const auto Ho = out_n_k_ho_wo_lengths[I2];
const auto Wo = out_n_k_ho_wo_lengths[I3];
const auto Y = wei_k_c_y_x_lengths[I2];
const auto X = wei_k_c_y_x_lengths[I3];
const auto C0 = C / Number<InWeiVectorSize>{};
const auto C1 = Number<InWeiVectorSize>{};
const auto K0 = K / Number<InWeiVectorSize>{};
const auto K1 = Number<InWeiVectorSize>{};
Tensor<TInWei> in_n_c0_hi_wi_c1(
HostTensorDescriptor(std::initializer_list<index_t>{N, C0, Hi, Wi, C1}));
Tensor<TInWei> wei_k_c0_y_x_c1(
HostTensorDescriptor(std::initializer_list<index_t>{K, C0, Y, X, C1}));
Tensor<TOut> out_n_k0_ho_wo_k1(
HostTensorDescriptor(std::initializer_list<index_t>{N, K0, Ho, Wo, K1}));
auto f_nchw2nc0hwc1 = [&](auto n, auto hi, auto wi, auto c) {
in_n_c0_hi_wi_c1(n, c / InWeiVectorSize, hi, wi, c % InWeiVectorSize) =
in_n_c_hi_wi(n, c, hi, wi);
};
auto f_kcyx2kc0yxc1 = [&](auto k, auto y, auto x, auto c) {
wei_k_c0_y_x_c1(k, c / InWeiVectorSize, y, x, c % InWeiVectorSize) =
wei_k_c_y_x(k, c, y, x);
};
make_ParallelTensorFunctor(f_nchw2nc0hwc1, N, Hi, Wi, C)();
make_ParallelTensorFunctor(f_kcyx2kc0yxc1, K, Y, X, C)();
DeviceMem in_n_c0_hi_wi_c1_device_buf(sizeof(TInWei) *
in_n_c0_hi_wi_c1.mDesc.GetElementSpace());
DeviceMem wei_k_c0_y_x_c1_device_buf(sizeof(TInWei) * wei_k_c0_y_x_c1.mDesc.GetElementSpace());
DeviceMem out_n_k0_ho_wo_k1_device_buf(sizeof(TOut) *
out_n_k0_ho_wo_k1.mDesc.GetElementSpace());
in_n_c0_hi_wi_c1_device_buf.ToDevice(in_n_c0_hi_wi_c1.mData.data());
wei_k_c0_y_x_c1_device_buf.ToDevice(wei_k_c0_y_x_c1.mData.data());
const auto in_n_c0_hi_wi_desc = make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi));
const auto wei_k_c0_y_x_desc = make_naive_tensor_descriptor_packed(make_tuple(K, C0, Y, X));
const auto out_n_k0_ho_wo_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1));
#if 1
// cdata = 64, BlockSize = 64, 16x8x32x4
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 16;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 32;
constexpr index_t EPerBlock = 1;
constexpr index_t KPerThread = KPerBlock;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = EPerBlock;
using ABlockTransferThreadSliceLengths_E_K = Sequence<3, 1>;
using ABlockTransferThreadClusterLengths_E_K = Sequence<3 * EPerBlock, KPerBlock>;
constexpr index_t ABlockTransferSrcScalarPerVector_E = 1;
constexpr index_t ABlockTransferDstScalarPerVector_K = 1;
constexpr index_t BThreadTransferSrcScalarPerVector_W = 1;
constexpr index_t CThreadTransferDstScalarPerVector_W = 16;
static_assert(KPerThread % CThreadTransferDstScalarPerVector_W == 0, "");
#else
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 16;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 32;
constexpr index_t EPerBlock = 1;
constexpr index_t KPerThread = 16;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = EPerBlock;
using ABlockTransferThreadSliceLengths_E_K = Sequence<9, 1>;
using ABlockTransferThreadClusterLengths_E_K = Sequence<EPerBlock, 16>;
constexpr index_t ABlockTransferSrcScalarPerVector_E = 1;
constexpr index_t ABlockTransferDstScalarPerVector_K = 1;
constexpr index_t BThreadTransferSrcScalarPerVector_W = 1;
constexpr index_t CThreadTransferDstScalarPerVector_W = K1;
static_assert(KPerThread % CThreadTransferDstScalarPerVector_W == 0, "");
#endif
constexpr auto conv_driver =
#if 0
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
#else
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outpad
#endif
<BlockSize,
typename vector_type<TInWei, InWeiVectorSize>::type,
TAcc,
TOut,
KPerBlock,
HoPerBlock,
WoPerBlock,
EPerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E_K,
ABlockTransferThreadClusterLengths_E_K,
ABlockTransferSrcScalarPerVector_E,
ABlockTransferDstScalarPerVector_K,
BThreadTransferSrcScalarPerVector_W,
CThreadTransferDstScalarPerVector_W>{};
conv_driver.Run(wei_k_c0_y_x_desc,
in_n_c0_hi_wi_desc,
out_n_k0_ho_wo_k1_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
wei_k_c0_y_x_c1_device_buf.GetDeviceBuffer()),
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
in_n_c0_hi_wi_c1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k0_ho_wo_k1_device_buf.GetDeviceBuffer()));
out_n_k0_ho_wo_k1_device_buf.FromDevice(out_n_k0_ho_wo_k1.mData.data());
auto f_nk0hwk1_to_nkhw = [&](auto n, auto k, auto ho, auto wo) {
out_n_k_ho_wo(n, k, ho, wo) =
out_n_k0_ho_wo_k1(n, k / InWeiVectorSize, ho, wo, k % InWeiVectorSize);
};
make_ParallelTensorFunctor(f_nk0hwk1_to_nkhw, N, K, Ho, Wo)();
}
host/driver_offline/include/device_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp 0 → 100644
View file @ 970fa3e9
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
ck::ActivTypeEnum_t activ_type,
typename InLengths,
typename WeiLengths,
typename MaxLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1(
const InLengths& in_n_c0_hi_wi_c1_lengths,
const WeiLengths& wei_k_c0_y_x_c1_lengths,
const MaxLengths& max_n_k0_hx_wx_k1_lengths,
const OutLengths& out_n_k0_ho_wo_k1_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c0_hi_wi_c1,
const Tensor<TInWei>& wei_k_c0_y_x_c1,
const Tensor<TOut>& bias_k0_k1,
Tensor<TOut>& out_n_k0_ho_wo_k1,
Tensor<TOut>& max_n_k0_hx_wx_k1,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
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>{};
const auto N = out_n_k0_ho_wo_k1_lengths[I0];
const auto K0 = out_n_k0_ho_wo_k1_lengths[I1];
const auto Ho = out_n_k0_ho_wo_k1_lengths[I2];
const auto Wo = out_n_k0_ho_wo_k1_lengths[I3];
const auto K1 = out_n_k0_ho_wo_k1_lengths[I4];
const auto C0 = in_n_c0_hi_wi_c1_lengths[I1];
const auto Hi = in_n_c0_hi_wi_c1_lengths[I2];
const auto Wi = in_n_c0_hi_wi_c1_lengths[I3];
const auto C1 = in_n_c0_hi_wi_c1_lengths[I4];
const auto K = wei_k_c0_y_x_c1_lengths[I0];
const auto Y = wei_k_c0_y_x_c1_lengths[I2];
const auto X = wei_k_c0_y_x_c1_lengths[I3];
const auto Hx = max_n_k0_hx_wx_k1_lengths[I2];
const auto Wx = max_n_k0_hx_wx_k1_lengths[I3];
DeviceMem in_n_c0_hi_wi_c1_device_buf(sizeof(TInWei) *
in_n_c0_hi_wi_c1.mDesc.GetElementSpace());
DeviceMem wei_k_c0_y_x_c1_device_buf(sizeof(TInWei) * wei_k_c0_y_x_c1.mDesc.GetElementSpace());
DeviceMem bias_k0_k1_device_buf(sizeof(TOut) * bias_k0_k1.mDesc.GetElementSpace());
DeviceMem out_n_k0_ho_wo_k1_device_buf(sizeof(TOut) *
out_n_k0_ho_wo_k1.mDesc.GetElementSpace());
DeviceMem max_n_k0_hx_wx_k1_device_buf(sizeof(TOut) *
max_n_k0_hx_wx_k1.mDesc.GetElementSpace());
in_n_c0_hi_wi_c1_device_buf.ToDevice(in_n_c0_hi_wi_c1.mData.data());
wei_k_c0_y_x_c1_device_buf.ToDevice(wei_k_c0_y_x_c1.mData.data());
bias_k0_k1_device_buf.ToDevice(bias_k0_k1.mData.data());
max_n_k0_hx_wx_k1_device_buf.ToDevice(max_n_k0_hx_wx_k1.mData.data());
constexpr index_t InWeiVectorSize = 8;
if(C1 % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
#if 0
constexpr index_t BlockSize = 256;
constexpr index_t KPerBlock = 32;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 64;
constexpr index_t E1 = C0 * 9;
constexpr index_t E2 = 1;
constexpr index_t E1PerBlock = C0;
constexpr index_t KPerThread = 16;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 = Sequence<1, E1PerBlock, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = K1;
#elif 1
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 8;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 32;
constexpr index_t E1 = 2 * 9;
constexpr index_t E2 = 1;
constexpr index_t K2 = 2;
constexpr index_t E1PerBlock = 2;
constexpr index_t KPerThread = KPerBlock;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 =
Sequence<1, E1PerBlock, 1, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = InWeiVectorSize;
#endif
if(KPerThread % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
const auto in_n_c0_hi_wi_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2));
const auto wei_k_c0_y_x_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(K, C0, Y, X, E2));
const auto max_n_k0_hx_wx_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Hx, Wx, K1));
const auto out_n_k0_ho_wo_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1));
constexpr auto conv_driver =
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_maxpool<
BlockSize,
typename vector_type<TInWei, InWeiVectorSize>::type,
TAcc,
TOut,
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
BThreadTransferSrcScalarPerVector_E2,
CThreadTransferDstScalarPerVector_K,
activ_type>{};
std::cerr << "conv_bias_activ_maxpool_input_"
<< "n" << N << "c" << C0 << "h" << Hi << "w" << Wi << "c" << C1 << "_filter_k" << K
<< "c" << C0 << "y" << Y << "x" << X << "c" << C1 << "_convout_n" << N << "k" << K0
<< "h" << Ho << "w" << Wo << "k" << K1 << "_maxpoolout_n" << N << "k" << K0 << "h"
<< Ho / 2 << "w" << Wo / 2 << "k" << K1 << std::endl;
for(int i = 0; i < 5; i++)
{
const auto ave_time =
conv_driver.Run(wei_k_c0_y_x_c1_desc,
in_n_c0_hi_wi_c1_desc,
out_n_k0_ho_wo_k1_desc,
max_n_k0_hx_wx_k1_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
wei_k_c0_y_x_c1_device_buf.GetDeviceBuffer()),
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
in_n_c0_hi_wi_c1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(bias_k0_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k0_ho_wo_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(max_n_k0_hx_wx_k1_device_buf.GetDeviceBuffer()),
nrepeat);
{
float perf = static_cast<float>(std::size_t(2) * N * K * Ho * Wo * C0 * C1 * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
}
out_n_k0_ho_wo_k1_device_buf.FromDevice(out_n_k0_ho_wo_k1.mData.data());
max_n_k0_hx_wx_k1_device_buf.FromDevice(max_n_k0_hx_wx_k1.mData.data());
}
host/driver_offline/include/driver_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ a651ea4f
#ifndef DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_NCHW_KCYX_NKHW_HPP
#define DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v2.hpp"
#include "gridwise_operation_wrapper.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::index_t KPerBlock,
ck::index_t HoPerBlock,
ck::index_t WoPerBlock,
ck::index_t EPerBlock,
ck::index_t KPerThread,
ck::index_t HoPerThread,
ck::index_t WoPerThread,
ck::index_t EPerThread,
typename ABlockTransferThreadSliceLengths_E_K,
typename ABlockTransferThreadClusterLengths_E_K,
ck::index_t ABlockTransferSrcScalarPerVector_E,
ck::index_t ABlockTransferDstScalarPerVector_K,
ck::index_t BThreadTransferSrcScalarPerVector_W,
ck::index_t CThreadTransferDstScalarPerVector_W>
struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
{
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ void Run(const ck::TensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const ck::TensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const ck::TensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const FloatAB* __restrict__ p_wei_global,
const FloatAB* __restrict__ p_in_global,
FloatC* __restrict__ p_out_global) const
{
using namespace ck;
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>{};
const auto N = in_n_c_hi_wi_global_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_global_desc.GetLength(I1);
const auto K0 = out_n_k0_ho_wo_k1_global_desc.GetLength(I1);
const auto Hi = in_n_c_hi_wi_global_desc.GetLength(I2);
const auto Wi = in_n_c_hi_wi_global_desc.GetLength(I3);
const auto Ho = out_n_k0_ho_wo_k1_global_desc.GetLength(I2);
const auto Wo = out_n_k0_ho_wo_k1_global_desc.GetLength(I3);
const auto K1 = out_n_k0_ho_wo_k1_global_desc.GetLength(I4);
const auto K = wei_k_c_y_x_global_desc.GetLength(I0);
const auto Y = wei_k_c_y_x_global_desc.GetLength(I2);
const auto X = wei_k_c_y_x_global_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
// weight tensor
const auto wei_e_k_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(C * Y * X)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
// input tensor
const auto in_n_c_hip_wip_global_desc = transform_tensor_descriptor(
in_n_c_hi_wi_global_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_c_y_ho_x_wo_global_desc = transform_tensor_descriptor(
in_n_c_hip_wip_global_desc,
make_tuple(
make_pass_through_transform(N),
make_pass_through_transform(C),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_e_n_ho_wo_global_desc = transform_tensor_descriptor(
in_n_c_y_ho_x_wo_global_desc,
make_tuple(make_merge_transform(make_tuple(C, Y, X)),
make_pass_through_transform(N),
make_pass_through_transform(Ho),
make_pass_through_transform(Wo)),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0>{}, Sequence<3>{}, Sequence<5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// output tensor
const auto out_k_n_ho_wo_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pass_through_transform(Ho),
make_pass_through_transform(Wo)),
make_tuple(Sequence<1, 4>{}, Sequence<0>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto E = C * Y * X;
if(!((K % KPerBlock) == 0 && (Ho % HoPerBlock) == 0 && (Wo % WoPerBlock) == 0 &&
(E % EPerBlock) == 0))
{
throw std::runtime_error("wrong! GEMM size no divisible");
}
// hack to control index calculation when iterating over a_k_m_global tensor
constexpr auto a_e_k_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto a_e_k_global_move_slice_window_step_hack = Sequence<0, 0, 0>{};
constexpr auto b_e_n_ho_wo_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto b_e_n_ho_wo_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
// hack for NKHW format
constexpr auto c_k_n_ho_wo_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}));
#if 1
// GEMM
using gridwise_gemm = GridwiseGemmDlops_km_kn_mn_v3<
BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
decltype(wei_e_k_global_desc),
decltype(in_e_n_ho_wo_global_desc),
decltype(out_k_n_ho_wo_global_desc),
KPerBlock,
HoPerBlock,
WoPerBlock,
EPerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E_K,
ABlockTransferThreadClusterLengths_E_K,
Sequence<1, 0>,
Sequence<1, 0>,
0,
ABlockTransferSrcScalarPerVector_E,
ABlockTransferDstScalarPerVector_K,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 2, 3, 1>,
3,
BThreadTransferSrcScalarPerVector_W,
false, // don't move back src coordinate after threadwise copy, which will be fused with
// MoveSrcSliceWindow() to save addr computation
Sequence<0, 2, 3, 1>,
0,
CThreadTransferDstScalarPerVector_W,
decltype(a_e_k_global_step_hacks),
decltype(b_e_n_ho_wo_global_step_hacks),
decltype(c_k_n_ho_wo_global_tensor_step_hacks),
decltype(a_e_k_global_move_slice_window_step_hack),
decltype(b_e_n_ho_wo_global_move_slice_window_step_hack)>;
const auto GridSize = (K / KPerBlock) * (Ho / HoPerBlock) * (Wo / WoPerBlock) * N;
const bool has_main_k_block_loop = (E + EPerBlock) / (2 * EPerBlock) > 1;
const bool has_double_tail_k_block_loop = (E / EPerBlock) % 2 == 0;
index_t nrepeat = 100;
for(index_t i = 0; i < 5; ++i)
{
std::cout << "Start running " << nrepeat << " times..." << std::endl;
KernelTimer timer;
timer.Start();
std::cout << "has_main_k_block_loop: " << has_main_k_block_loop
<< " has_double_tail_k_block_loop: " << has_double_tail_k_block_loop
<< std::endl;
for(index_t j = 0; j < nrepeat; ++j)
{
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = run_gridwise_operation<gridwise_gemm,
decltype(wei_e_k_global_desc),
const FloatAB*,
decltype(in_e_n_ho_wo_global_desc),
const FloatAB*,
decltype(out_k_n_ho_wo_global_desc),
FloatC*,
integral_constant<bool, true>,
integral_constant<bool, true>>;
launch_kernel(kernel,
dim3(GridSize),
dim3(BlockSize),
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
p_in_global,
out_k_n_ho_wo_global_desc,
p_out_global,
integral_constant<bool, true>{},
integral_constant<bool, true>{});
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel = run_gridwise_operation<gridwise_gemm,
decltype(wei_e_k_global_desc),
const FloatAB*,
decltype(in_e_n_ho_wo_global_desc),
const FloatAB*,
decltype(out_k_n_ho_wo_global_desc),
FloatC*,
integral_constant<bool, true>,
integral_constant<bool, false>>;
launch_kernel(kernel,
dim3(GridSize),
dim3(BlockSize),
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
p_in_global,
out_k_n_ho_wo_global_desc,
p_out_global,
integral_constant<bool, true>{},
integral_constant<bool, false>{});
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = run_gridwise_operation<gridwise_gemm,
decltype(wei_e_k_global_desc),
const FloatAB*,
decltype(in_e_n_ho_wo_global_desc),
const FloatAB*,
decltype(out_k_n_ho_wo_global_desc),
FloatC*,
integral_constant<bool, false>,
integral_constant<bool, true>>;
launch_kernel(kernel,
dim3(GridSize),
dim3(BlockSize),
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
p_in_global,
out_k_n_ho_wo_global_desc,
p_out_global,
integral_constant<bool, false>{},
integral_constant<bool, true>{});
}
else
{
const auto kernel = run_gridwise_operation<gridwise_gemm,
decltype(wei_e_k_global_desc),
const FloatAB*,
decltype(in_e_n_ho_wo_global_desc),
const FloatAB*,
decltype(out_k_n_ho_wo_global_desc),
FloatC*,
integral_constant<bool, false>,
integral_constant<bool, false>>;
launch_kernel(kernel,
dim3(GridSize),
dim3(BlockSize),
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
p_in_global,
out_k_n_ho_wo_global_desc,
p_out_global,
integral_constant<bool, false>{},
integral_constant<bool, false>{});
}
}
timer.End();
float ave_time = timer.GetElapsedTime() / nrepeat;
float perf =
static_cast<float>(calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
#endif
}
};
#endif
host/driver_offline/src/conv_add_fwd_driver_offline_nchwc.cpp 0 → 100644
View file @ 970fa3e9
This diff is collapsed.
host/driver_offline/src/conv_fwd_driver_offline.cpp
View file @ 970fa3e9
...@@ -15,17 +15,15 @@ ...@@ -15,17 +15,15 @@
#include "device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp" #include "device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp" #include "device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp" #include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_DYNAMIC_MODE 1 #define USE_DYNAMIC_MODE 0
#define USE_CONV_FWD_V4R4_NCHW 0 #define USE_CONV_FWD_V4R4_NCHW 0
#define USE_CONV_FWD_V4R4R2_NHWC 0 #define USE_CONV_FWD_V4R4R2_NHWC 1
#define USE_CONV_FWD_V6R1_NCHW 0 #define USE_CONV_FWD_V6R1_NCHW 1
#define USE_CONV_FWD_V5R1_NCHW 0
#define USE_CONV_FWD_V4R4R2_XDL_NCHW 0 #define USE_CONV_FWD_V4R4R2_XDL_NCHW 0
#define USE_CONV_FWD_V4R4R4_XDL_NHWC 1 #define USE_CONV_FWD_V4R4R4_XDL_NHWC 0
enum ConvTensorLayout enum ConvTensorLayout
{ {
...@@ -41,9 +39,8 @@ enum ConvForwardAlgo ...@@ -41,9 +39,8 @@ enum ConvForwardAlgo
V4R4NCHW, // 0 V4R4NCHW, // 0
V4R4R2NHWC, // 1 V4R4R2NHWC, // 1
V6R1NCHW, // 2 V6R1NCHW, // 2
V5R1NCHW, // 3 V4R4R2XDLNCHW, // 3
V4R4R2XDLNCHW, // 4 V4R4R4XDLNHWC // 4
V4R4R4XDLNHWC // 5
}; };
template <typename TIn, template <typename TIn,
...@@ -237,8 +234,8 @@ int main(int argc, char* argv[]) ...@@ -237,8 +234,8 @@ int main(int argc, char* argv[])
constexpr auto Y = Number<3>{}; constexpr auto Y = Number<3>{};
constexpr auto X = Number<3>{}; constexpr auto X = Number<3>{};
constexpr auto conv_stride_h = I2; constexpr auto conv_stride_h = I1;
constexpr auto conv_stride_w = I2; constexpr auto conv_stride_w = I1;
constexpr auto conv_dilation_h = I1; constexpr auto conv_dilation_h = I1;
constexpr auto conv_dilation_w = I1; constexpr auto conv_dilation_w = I1;
constexpr auto in_left_pad_h = I1; constexpr auto in_left_pad_h = I1;
...@@ -253,7 +250,7 @@ int main(int argc, char* argv[]) ...@@ -253,7 +250,7 @@ int main(int argc, char* argv[])
constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1; constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
#endif #endif
#if 0 #if 1
using in_data_t = float; using in_data_t = float;
using acc_data_t = float; using acc_data_t = float;
using out_data_t = float; using out_data_t = float;
...@@ -472,33 +469,6 @@ int main(int argc, char* argv[]) ...@@ -472,33 +469,6 @@ int main(int argc, char* argv[])
} }
#endif #endif
#if USE_CONV_FWD_V5R1_NCHW
if(algo == ConvForwardAlgo::V5R1NCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw<in_data_t,
16,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4R2_XDL_NCHW #if USE_CONV_FWD_V4R4R2_XDL_NCHW
if(algo == ConvForwardAlgo::V4R4R2XDLNCHW) if(algo == ConvForwardAlgo::V4R4R2XDLNCHW)
{ {
......
host/driver_offline/src/conv_fwd_driver_offline_nchwc.cpp 0 → 100644
View file @ 970fa3e9
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