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Commit bee4e344 authored by aska-0096's avatar aska-0096
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(5/5) attention pass, todo: debug lds perf bug

parent fd4ff3a7
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example/30_grouped_conv_fwd_multiple_d/run_grouped_conv_fwd_bias_relu_add_wmma_example.inc
View file @ bee4e344
......@@ -53,8 +53,8 @@ using DeviceConvFwdInstance =
GemmSpec, // GemmSpecialization
1, // Prefetch stage
128, // BlockSize
64, // MPerBlock
64, // NPerBlock
64, // MPerBlock
64, // NPerBlock
64, // KPerBlock
4, // K1
16, // MPerWMMA
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_wmma.hpp
View file @ bee4e344
......@@ -305,7 +305,7 @@ struct BlockwiseGemmWMMA
static_for<0, KPerBlock / WmmaK, 1>{}(
[&](auto k) { // k=0,1,2 instead of k=0,kpack*1, ...
static_for<0, MRepeat, 1>{}([&](auto m0) {
// read A
// read A
a_thread_copy_.Run(
a_block_desc_k0_m0_m1_m2_k1,
make_tuple(Number<k * WmmaK / A_K1 / A_KRow>{}, m0, I0, I0, I0, I0),
......@@ -365,58 +365,57 @@ struct BlockwiseGemmWMMA
static_for<0, NRepeat, 1>{}([&](auto n0) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KPerBlock / WmmaK, 1>{}(
[&](auto k) { // k=0,1,2 instead of k=0,kpack*1, ...
// read B
b_thread_copy_.Run(
b_block_desc_k0_n0_n1_n2_k1,
make_tuple(Number<k * WmmaK / B_K1 / B_KRow>{}, n0, I0, I0, I0, I0),
b_block_buf,
b_thread_desc_,
make_tuple(I0, n0, I0, I0, I0, I0),
b_thread_buf);
// read A
a_thread_copy_.Run(
a_block_desc_k0_m0_m1_m2_k1,
make_tuple(Number<k * WmmaK / A_K1 / A_KRow>{}, m0, I0, I0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(I0, m0, I0, I0, I0, I0),
a_thread_buf);
vector_type<FloatA, WmmaK> a_thread_vec;
vector_type<FloatB, WmmaK> b_thread_vec;
static_for<0, KPerBlock / WmmaK, 1>{}([&](auto k) { // k=0,1,2 instead of
// k=0,kpack*1, ... read B
b_thread_copy_.Run(
b_block_desc_k0_n0_n1_n2_k1,
make_tuple(Number<k * WmmaK / B_K1 / B_KRow>{}, n0, I0, I0, I0, I0),
b_block_buf,
b_thread_desc_,
make_tuple(I0, n0, I0, I0, I0, I0),
b_thread_buf);
// read A
a_thread_copy_.Run(
a_block_desc_k0_m0_m1_m2_k1,
make_tuple(Number<k * WmmaK / A_K1 / A_KRow>{}, m0, I0, I0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(I0, m0, I0, I0, I0, I0),
a_thread_buf);
static_for<0, WmmaK, 1>{}([&](auto i) {
b_thread_vec.template AsType<FloatB>()(i) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(i / B_K1 / B_KRow,
n0,
0,
(i / B_K1) % B_KRow,
0,
i % B_K1))>{}];
a_thread_vec.template AsType<FloatA>()(i) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(i / A_K1 / A_KRow,
m0,
0,
(i / A_K1) % A_KRow,
0,
i % A_K1))>{}];
});
vector_type<FloatA, WmmaK> a_thread_vec;
vector_type<FloatB, WmmaK> b_thread_vec;
static_for<0, WmmaK, 1>{}([&](auto i) {
b_thread_vec.template AsType<FloatB>()(i) =
b_thread_buf[Number<b_thread_desc_.CalculateOffset(
make_tuple(i / B_K1 / B_KRow,
n0,
0,
(i / B_K1) % B_KRow,
0,
i % B_K1))>{}];
a_thread_vec.template AsType<FloatA>()(i) =
a_thread_buf[Number<a_thread_desc_.CalculateOffset(
make_tuple(i / A_K1 / A_KRow,
m0,
0,
(i / A_K1) % A_KRow,
0,
i % A_K1))>{}];
});
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
using wmma_input_type_a = typename vector_type<FloatA, WmmaK>::type;
using wmma_input_type_b = typename vector_type<FloatB, WmmaK>::type;
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
constexpr index_t c_offset =
c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
wmma_gemm.template Run(
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
});
wmma_gemm.template Run(
a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
});
});
});
}
......
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_wmma_cshuffle.hpp
View file @ bee4e344
......@@ -136,6 +136,7 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
static constexpr auto I3 = Number<3>{};
static constexpr auto I4 = Number<4>{};
static constexpr auto I5 = Number<5>{};
static constexpr auto I6 = Number<6>{};
static constexpr auto WmmaK = 16;
......@@ -175,13 +176,15 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
}
else
{
return Transform::MakeAGridDescriptor_AKWmma_MBlockRepeat_MWaves_AKRow_MPerWmma_AK1(
Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec),
Number<WmmaK>{},
Number<MRepeat>{},
Number<MWaves>{},
Number<MPerWmma>{},
Number<K1>{});
return Transform::
MakeAGridDescriptor_AKWmma_MBlockRepeat_MWaves_AK0PerWmma_AKRow_MPerWmma_AK1(
Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths_vec,
a_gs_ms_ks_strides_vec),
Number<WmmaK>{},
Number<MRepeat>{},
Number<MWaves>{},
Number<MPerWmma>{},
Number<K1>{});
}
}
......@@ -197,14 +200,15 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
}
else
{
return Transform::MakeB0GridDescriptor_BKWmma_LBlockRepeat_LWaves_BKRow_LPerWmma_BK1(
Transform::MakeB0GridDescriptor_N_K(b0_gs_ls_ks_lengths_vec,
b0_gs_ls_ks_strides_vec),
Number<WmmaK>{},
Number<LRepeat>{},
Number<LWaves>{},
Number<LPerWmma>{},
Number<K1>{});
return Transform::
MakeB0GridDescriptor_BKWmma_LBlockRepeat_LWaves_BK0PerWmma_BKRow_LPerWmma_BK1(
Transform::MakeB0GridDescriptor_N_K(b0_gs_ls_ks_lengths_vec,
b0_gs_ls_ks_strides_vec),
Number<WmmaK>{},
Number<LRepeat>{},
Number<LWaves>{},
Number<LPerWmma>{},
Number<K1>{});
}
}
......@@ -220,14 +224,15 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
}
else
{
return Transform::MakeB1GridDescriptor_BLWmma_NBlockRepeat_NWaves_BLRow_NPerWmma_BL1(
Transform::MakeB1GridDescriptor_N_K(b1_gs_ns_ls_lengths_vec,
b1_gs_ns_ls_strides_vec),
Number<WmmaK>{},
Number<NRepeat>{},
Number<NWaves>{},
Number<NPerWmma>{},
Number<L1>{});
return Transform::
MakeB1GridDescriptor_BLWmma_NBlockRepeat_NWaves__BL0PerWmma_BLRow_NPerWmma_BL1(
Transform::MakeB1GridDescriptor_N_K(b1_gs_ns_ls_lengths_vec,
b1_gs_ns_ls_strides_vec),
Number<WmmaK>{},
Number<NRepeat>{},
Number<NWaves>{},
Number<NPerWmma>{},
Number<L1>{});
}
}
......@@ -521,7 +526,7 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
else
{
return arg.a_grid_desc.GetLength(I0) * arg.a_grid_desc.GetLength(I3) *
arg.a_grid_desc.GetLength(I5);
arg.a_grid_desc.GetLength(I4) * arg.a_grid_desc.GetLength(I6);
}
}();
......@@ -826,7 +831,13 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
<< "CSpec" << getTensorSpecializationString(CSpec) << ", "
<< getMaskingSpecializationString(MaskingSpec)
<< ">"
<< " NumPrefetch: "
<< " AEnableLds: "
<< AEnableLds << ", "
<< "B0EnableLds: "
<< B0EnableLds << ", "
<< "B1EnableLds: "
<< B1EnableLds << ", "
<< "NumPrefetch: "
<< NumPrefetch << ", "
<< "LoopScheduler: "
<< LoopSchedToString[LoopSched] << ", "
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp
View file @ bee4e344
......@@ -468,26 +468,25 @@ struct DeviceGemmMultipleD_Wmma_CShuffle : public DeviceGemmMultipleD<ALayout,
remove_reference_t<typename GridwiseOp::DefaultBlock2CTileMap>,
has_main_k_block_loop>; // Last Option is W/O
return
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_ds_grid_,
arg.p_e_grid_,
arg.a_grid_desc,
arg.b_grid_desc,
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.block_2_ctile_map_);
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_ds_grid_,
arg.p_e_grid_,
arg.a_grid_desc,
arg.b_grid_desc,
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.block_2_ctile_map_);
};
if(GridwiseOp::CalculateHasMainKBlockLoop(K))
{
return launch_kernel(integral_constant<bool, true>{});
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp
View file @ bee4e344
......@@ -398,21 +398,21 @@ struct DeviceGemmWmma_CShuffle : public DeviceGemm<ALayout,
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
has_main_k_block_loop>;
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_,
arg.b_grid_desc_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_,
arg.b_grid_desc_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_,
arg.block_2_ctile_map_);
};
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
......
include/ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp
View file @ bee4e344
......@@ -186,7 +186,7 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
typename MPerWmma,
typename AK1>
__host__ __device__ static constexpr auto
MakeAGridDescriptor_AKWmma_MBlockRepeat_MWaves_AKRow_MPerWmma_AK1(
MakeAGridDescriptor_AKWmma_MBlockRepeat_MWaves_AK0PerWmma_AKRow_MPerWmma_AK1(
const AGridDesc_M_K& a_grid_desc_m_k,
const WmmaK&,
const MRepeat&,
......@@ -194,17 +194,19 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
const MPerWmma&,
const AK1&)
{
const auto M0 = a_grid_desc_m_k.GetLength(I0) / MPerBlock;
const auto K = a_grid_desc_m_k.GetLength(I1);
const auto AKWmma = K / WmmaK{};
constexpr auto AKRow = WmmaK{} / AK1{};
const auto M0 = a_grid_desc_m_k.GetLength(I0) / MPerBlock;
const auto K = a_grid_desc_m_k.GetLength(I1);
const auto AKWmma = K / WmmaK{};
constexpr auto AKRow = 2;
constexpr auto AK0PerWmma = WmmaK{} / AKRow / AK1{};
return transform_tensor_descriptor(
a_grid_desc_m_k,
make_tuple(make_unmerge_transform(make_tuple(AKWmma, AKRow, AK1{})),
make_tuple(make_unmerge_transform(
make_tuple(AKWmma, Number<AK0PerWmma>{}, Number<AKRow>{}, AK1{})),
make_unmerge_transform(make_tuple(M0 * MRepeat{}, MWaves{}, MPerWmma{}))),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 3, 5>{}, Sequence<1, 2, 4>{}));
make_tuple(Sequence<0, 3, 4, 6>{}, Sequence<1, 2, 5>{}));
}
//
......@@ -254,7 +256,7 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
typename LPerWmma,
typename BK1>
__host__ __device__ static constexpr auto
MakeB0GridDescriptor_BKWmma_LBlockRepeat_LWaves_BKRow_LPerWmma_BK1(
MakeB0GridDescriptor_BKWmma_LBlockRepeat_LWaves_BK0PerWmma_BKRow_LPerWmma_BK1(
const BGridDesc_L_K& b_grid_desc_l_k,
const WmmaK&,
const LRepeat&,
......@@ -262,17 +264,19 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
const LPerWmma&,
const BK1&)
{
const auto L0 = b_grid_desc_l_k.GetLength(I0) / NPerBlock;
const auto K = b_grid_desc_l_k.GetLength(I1);
const auto BKWmma = K / WmmaK{};
constexpr auto BKRow = WmmaK{} / BK1{};
const auto L0 = b_grid_desc_l_k.GetLength(I0) / NPerBlock;
const auto K = b_grid_desc_l_k.GetLength(I1);
const auto BKWmma = K / WmmaK{};
constexpr auto BKRow = 2;
constexpr auto BK0PerWmma = WmmaK{} / BKRow / BK1{};
return transform_tensor_descriptor(
b_grid_desc_l_k,
make_tuple(make_unmerge_transform(make_tuple(BKWmma, BKRow, BK1{})),
make_tuple(make_unmerge_transform(
make_tuple(BKWmma, Number<BK0PerWmma>{}, Number<BKRow>{}, BK1{})),
make_unmerge_transform(make_tuple(L0 * LRepeat{}, LWaves{}, LPerWmma{}))),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 3, 5>{}, Sequence<1, 2, 4>{}));
make_tuple(Sequence<0, 3, 4, 6>{}, Sequence<1, 2, 5>{}));
}
//
......@@ -323,7 +327,7 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
typename NPerWmma,
typename BL1>
__host__ __device__ static constexpr auto
MakeB1GridDescriptor_BLWmma_NBlockRepeat_NWaves_BLRow_NPerWmma_BL1(
MakeB1GridDescriptor_BLWmma_NBlockRepeat_NWaves__BL0PerWmma_BLRow_NPerWmma_BL1(
const BGridDesc_N_L& b_grid_desc_n_l,
const WmmaL&,
const NRepeat&,
......@@ -331,17 +335,19 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
const NPerWmma&,
const BL1&)
{
const auto N0 = b_grid_desc_n_l.GetLength(I0) / OPerBlock;
const auto L = b_grid_desc_n_l.GetLength(I1);
const auto BLWmma = L / WmmaL{};
constexpr auto BLRow = WmmaL{} / BL1{};
const auto N0 = b_grid_desc_n_l.GetLength(I0) / OPerBlock;
const auto L = b_grid_desc_n_l.GetLength(I1);
const auto BLWmma = L / WmmaL{};
constexpr auto BLRow = 2;
constexpr auto BL0PerWmma = WmmaL{} / BLRow / BL1{};
return transform_tensor_descriptor(
b_grid_desc_n_l,
make_tuple(make_unmerge_transform(make_tuple(BLWmma, BLRow, BL1{})),
make_tuple(make_unmerge_transform(
make_tuple(BLWmma, Number<BL0PerWmma>{}, Number<BLRow>{}, BL1{})),
make_unmerge_transform(make_tuple(N0 * NRepeat{}, NWaves{}, NPerWmma{}))),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 3, 5>{}, Sequence<1, 2, 4>{}));
make_tuple(Sequence<0, 3, 4, 6>{}, Sequence<1, 2, 5>{}));
}
//
......
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