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Commit f30e5975 authored by Jun Liu's avatar Jun Liu
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Merge branch 'develop' into amd-develop

parents 91b414cd bec84efb
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Showing with 941 additions and 989 deletions
include/ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_splitk_2nd.hpp
View file @ f30e5975
......@@ -17,11 +17,13 @@ template <typename MeanVarDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename SaveMeanInvStdDataType,
typename ComputeDataType,
typename YElementwiseOperation,
typename MeanVarGridDesc_M_KBlock,
typename CountGridDesc_M_KBlock,
typename XYGammaBetaGridDesc_M_K,
typename SaveMeanInvStdGridDesc_M,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
......@@ -34,7 +36,8 @@ template <typename MeanVarDataType,
index_t BetaSrcVectorDim,
index_t BetaSrcVectorSize,
index_t YDstVectorDim,
index_t YDstVectorSize>
index_t YDstVectorSize,
index_t SaveMeanInvStdDstVectorSize>
struct GridwiseNormalizationSplitK2nd
{
static_assert((XSrcVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
......@@ -45,6 +48,10 @@ struct GridwiseNormalizationSplitK2nd
(YDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static_assert(MThreadSliceSize % SaveMeanInvStdDstVectorSize == 0,
"Invalid thread slice sizes and/or save mean and inverse std vector sizes "
"configuration, please check!");
static_assert(XSrcVectorSize == YDstVectorSize);
static_assert(XSrcVectorSize == GammaSrcVectorSize);
static_assert(XSrcVectorSize == BetaSrcVectorSize);
......@@ -69,6 +76,10 @@ struct GridwiseNormalizationSplitK2nd
static constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{}));
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
static constexpr auto thread_buffer_desc_m =
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
static constexpr auto thread_buffer_desc_m_1 =
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, I1));
......@@ -99,6 +110,8 @@ struct GridwiseNormalizationSplitK2nd
const XYGammaBetaGridDesc_M_K& gamma_grid_desc_m_k,
const XYGammaBetaGridDesc_M_K& beta_grid_desc_m_k,
const XYGammaBetaGridDesc_M_K& y_grid_desc_m_k,
const SaveMeanInvStdGridDesc_M& save_mean_grid_desc_m,
const SaveMeanInvStdGridDesc_M& save_inv_std_grid_desc_m,
index_t num_k_mean_var_count_iteration,
index_t num_k_block_tile_iteration,
index_t k_grid_size,
......@@ -110,6 +123,8 @@ struct GridwiseNormalizationSplitK2nd
const GammaDataType* const __restrict__ p_gamma_global,
const BetaDataType* const __restrict__ p_beta_global,
YDataType* const __restrict__ p_y_global,
SaveMeanInvStdDataType* const __restrict__ p_save_mean_global,
SaveMeanInvStdDataType* const __restrict__ p_save_inv_std_global,
const YElementwiseOperation y_elementwise_op)
{
// Thread/Block id
......@@ -145,6 +160,12 @@ struct GridwiseNormalizationSplitK2nd
auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
auto save_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_save_mean_global, save_mean_grid_desc_m.GetElementSpaceSize());
auto save_inv_std_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_save_inv_std_global, save_inv_std_grid_desc_m.GetElementSpaceSize());
// VGPR
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
in_mean_thread_buf;
......@@ -158,6 +179,7 @@ struct GridwiseNormalizationSplitK2nd
var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true>
welford_count_thread_buf;
auto& inv_std_thread_buf = var_thread_buf;
auto x_thread_buf = generate_tuple(
[&](auto) {
......@@ -283,6 +305,42 @@ struct GridwiseNormalizationSplitK2nd
thread_k_cluster_id * YDstVectorSize),
y_elementwise_op);
auto threadwise_mean_store =
ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
SaveMeanInvStdDataType,
decltype(thread_buffer_desc_m),
SaveMeanInvStdGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>, // DimAccessOrder
0, // SrcVectorDim
SaveMeanInvStdDstVectorSize, // ScalarPerVector
InMemoryDataOperationEnum::Set,
1,
true>(
save_mean_grid_desc_m,
make_multi_index(block_m_cluster_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
auto threadwise_inv_std_store =
ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
SaveMeanInvStdDataType,
decltype(thread_buffer_desc_m),
SaveMeanInvStdGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>, // DimAccessOrder
0, // SrcVectorDim
SaveMeanInvStdDstVectorSize, // ScalarPerVector
InMemoryDataOperationEnum::Set,
1,
true>(
save_inv_std_grid_desc_m,
make_multi_index(block_m_cluster_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
// step1: Merge mean and variance
constexpr auto mean_var_count_thread_copy_step_I0_k =
make_multi_index(I0, KThreadClusterSize);
......@@ -332,9 +390,33 @@ struct GridwiseNormalizationSplitK2nd
BlockwiseWelford::Run(
mean_thread_buf(I), var_thread_buf(I), welford_count_thread_buf(I));
inv_std_thread_buf(I) =
type_convert<ComputeDataType>(1.0f) / ck::math::sqrt(var_thread_buf(I) + epsilon);
});
// step2: normalization
// step2: save mean and inverse std for backward (optional)
if(block_k_cluster_id == 0 && thread_k_cluster_id == 0)
{
if(p_save_mean_global != nullptr)
{
threadwise_mean_store.Run(thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf,
save_mean_grid_desc_m,
save_mean_global_val_buf);
}
if(p_save_inv_std_global != nullptr)
{
threadwise_inv_std_store.Run(thread_buffer_desc_m,
make_tuple(I0),
inv_std_thread_buf,
save_inv_std_grid_desc_m,
save_inv_std_global_val_buf);
}
}
// step3: normalization
constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
for(index_t k = 0; k < num_k_block_tile_iteration; ++k)
......@@ -360,7 +442,6 @@ struct GridwiseNormalizationSplitK2nd
});
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
constexpr auto offset_m_k =
......@@ -369,7 +450,7 @@ struct GridwiseNormalizationSplitK2nd
// normalize
y_thread_buf(iK0)(Number<offset_m_k>{}) =
(x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
divisor;
inv_std_thread_buf(iM);
// gamma
y_thread_buf(iK0)(Number<offset_m_k>{}) =
......
include/ck/tensor_operation/gpu/grid/normalization/gridwise_normalization_welford_variance.hpp
View file @ f30e5975
......@@ -16,9 +16,11 @@ template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename SaveMeanInvStdDataType,
typename ComputeDataType,
typename YElementwiseOperation,
typename GridDesc_M_K,
typename GridDesc_M,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
......@@ -32,6 +34,7 @@ template <typename XDataType,
index_t BetaSrcVectorSize,
index_t YDstVectorDim,
index_t YDstVectorSize,
index_t SaveMeanInvStdDstVectorSize,
bool SweepOnce>
struct GridwiseNormalizationWelfordVariance_mk_to_mk
{
......@@ -43,6 +46,10 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
(YDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static_assert(MThreadSliceSize % SaveMeanInvStdDstVectorSize == 0,
"Invalid thread slice sizes and/or save mean and inverse std vector sizes "
"configuration, please check!");
static_assert(XSrcVectorSize == YDstVectorSize);
static_assert(XSrcVectorSize == GammaSrcVectorSize);
static_assert(XSrcVectorSize == BetaSrcVectorSize);
......@@ -64,6 +71,10 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
static constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{}));
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
static constexpr auto thread_buffer_desc_m =
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<XSrcVectorSize>{})));
using ThreadReduceDstDesc_M =
......@@ -77,6 +88,8 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder>;
using PassThroughOp = tensor_operation::element_wise::PassThrough;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
......@@ -114,17 +127,18 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
const GridDesc_M_K& gamma_grid_desc_m_k,
const GridDesc_M_K& beta_grid_desc_m_k,
const GridDesc_M_K& y_grid_desc_m_k,
const GridDesc_M& save_mean_grid_desc_m,
const GridDesc_M& save_inv_std_grid_desc_m,
index_t num_k_block_tile_iteration,
ComputeDataType epsilon,
const XDataType* const __restrict__ p_x_global,
const GammaDataType* const __restrict__ p_gamma_global,
const BetaDataType* const __restrict__ p_beta_global,
YDataType* const __restrict__ p_y_global,
SaveMeanInvStdDataType* const __restrict__ p_save_mean_global,
SaveMeanInvStdDataType* const __restrict__ p_save_inv_std_global,
const YElementwiseOperation y_elementwise_op)
{
auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
auto x_thread_buf = generate_tuple(
[&](auto) {
return StaticBuffer<AddressSpaceEnum::Vgpr,
......@@ -150,6 +164,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
var_thread_buf;
auto& inv_std_thread_buf = var_thread_buf;
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
......@@ -226,6 +241,42 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
thread_k_cluster_id * YDstVectorSize),
y_elementwise_op);
auto threadwise_mean_store =
ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
SaveMeanInvStdDataType,
decltype(thread_buffer_desc_m),
GridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>, // DimAccessOrder
0, // SrcVectorDim
SaveMeanInvStdDstVectorSize, // ScalarPerVector
InMemoryDataOperationEnum::Set,
1,
true>(
save_mean_grid_desc_m,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
auto threadwise_inv_std_store =
ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
SaveMeanInvStdDataType,
decltype(thread_buffer_desc_m),
GridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>, // DimAccessOrder
0, // SrcVectorDim
SaveMeanInvStdDstVectorSize, // ScalarPerVector
InMemoryDataOperationEnum::Set,
1,
true>(
save_inv_std_grid_desc_m,
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
constexpr auto thread_copy_bwd_step_m_k =
make_multi_index(0, SweepOnce ? 0 : -K_BlockTileSize);
......@@ -239,6 +290,15 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
const auto beta_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_beta_global, beta_grid_desc_m_k.GetElementSpaceSize());
auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
auto save_mean_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_save_mean_global, save_mean_grid_desc_m.GetElementSpaceSize());
auto save_inv_std_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_save_inv_std_global, save_inv_std_grid_desc_m.GetElementSpaceSize());
auto threadwise_welford = ThreadwiseWelford();
threadwise_welford.max_count_ = GetKPerThread(x_grid_desc_m_k, thread_k_cluster_id);
......@@ -279,10 +339,33 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
int count = threadwise_welford.cur_count_;
BlockwiseWelford::Run(mean_thread_buf(I), var_thread_buf(I), count);
inv_std_thread_buf(I) = type_convert<ComputeDataType>(1.0f) /
ck::math::sqrt(var_thread_buf(I) + epsilon);
});
// save mean and inverse std for backward (optional)
if(thread_k_cluster_id == 0)
{
if(p_save_mean_global != nullptr)
{
threadwise_mean_store.Run(thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf,
save_mean_grid_desc_m,
save_mean_global_val_buf);
}
if(p_save_inv_std_global != nullptr)
{
threadwise_inv_std_store.Run(thread_buffer_desc_m,
make_tuple(I0),
inv_std_thread_buf,
save_inv_std_grid_desc_m,
save_inv_std_global_val_buf);
}
}
// normalization
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
constexpr auto offset_m_k =
......@@ -291,7 +374,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
// normalize
y_thread_buf(iK0)(Number<offset_m_k>{}) =
(x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
divisor;
inv_std_thread_buf(iM);
// gamma & beta
y_thread_buf(iK0)(Number<offset_m_k>{}) =
......@@ -360,8 +443,29 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
int count = threadwise_welford.cur_count_;
BlockwiseWelford::Run(mean_thread_buf(I), var_thread_buf(I), count);
inv_std_thread_buf(I) = 1 / ck::math::sqrt(var_thread_buf(I) + epsilon);
});
if(thread_k_cluster_id == 0)
{
if(p_save_mean_global != nullptr)
{
threadwise_mean_store.Run(thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf,
save_mean_grid_desc_m,
save_mean_global_val_buf);
}
if(p_save_inv_std_global != nullptr)
{
threadwise_inv_std_store.Run(thread_buffer_desc_m,
make_tuple(I0),
inv_std_thread_buf,
save_inv_std_grid_desc_m,
save_inv_std_global_val_buf);
}
}
auto thread_copy_tail_m_k =
(num_k_block_tile_iteration - 1) * ThreadBufferNumber * thread_copy_fwd_step_m_k;
......@@ -393,7 +497,6 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
});
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
auto divisor = 1 / ck::math::sqrt(var_thread_buf(iM) + epsilon);
static_for<0, ThreadBufferNumber, 1>{}([&](auto iK0) {
static_for<0, XSrcVectorSize, 1>{}([&](auto iK1) {
constexpr auto offset_m_k =
......@@ -402,7 +505,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
// normalize
y_thread_buf(iK0)(Number<offset_m_k>{}) =
(x_thread_buf(iK0)(Number<offset_m_k>{}) - mean_thread_buf(iM)) *
divisor;
inv_std_thread_buf(iM);
// gamma
y_thread_buf(iK0)(Number<offset_m_k>{}) =
......
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v3r1.hpp
View file @ f30e5975
......@@ -9,6 +9,7 @@
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor/static_tensor.hpp"
#include "ck/utility/is_detected.hpp"
namespace ck {
......@@ -211,10 +212,44 @@ struct ThreadwiseTensorSliceTransfer_v3r1
auto src_vector_container = src_vector_type{
src_buf.template Get<src_vector_t>(src_coord_.GetOffset(), is_src_valid)};
using dst_vector_type = vector_type_maker_t<DstData, SrcScalarPerVector>;
using dst_vector_t = typename dst_vector_type::type;
dst_vector_type op_r_v;
constexpr auto get_elem_op_vec_len = []() {
if constexpr(is_detected<is_pack8_invocable_t, decltype(src_element_op_)>::value)
{
if constexpr(decltype(src_element_op_)::is_pack8_invocable)
return math::min(8, SrcScalarPerVector);
}
if constexpr(is_detected<is_pack4_invocable_t, decltype(src_element_op_)>::value)
{
if constexpr(decltype(src_element_op_)::is_pack4_invocable)
return math::min(4, SrcScalarPerVector);
}
if constexpr(is_detected<is_pack2_invocable_t, decltype(src_element_op_)>::value)
{
if constexpr(decltype(src_element_op_)::is_pack2_invocable)
return math::min(2, SrcScalarPerVector);
}
return 1;
};
constexpr index_t elem_op_vec_len = get_elem_op_vec_len();
using src_elem_op_vec_t = typename vector_type<SrcData, elem_op_vec_len>::type;
using dst_elem_op_vec_t = typename vector_type<DstData, elem_op_vec_len>::type;
static_for<0, SrcScalarPerVector / elem_op_vec_len, 1>{}([&](auto idx) {
// apply the src elementwise op and convert to DstData under the hood if needed
src_element_op_(op_r_v.template AsType<dst_elem_op_vec_t>()(idx),
src_vector_container.template AsType<src_elem_op_vec_t>()[idx]);
});
// copy data from src_vector_container into src_thread_scratch_
src_thread_scratch_tuple_(thread_scratch_id)
.template SetAsType<src_vector_t>(
src_data_idx_seq, src_vector_container.template AsType<src_vector_t>()[I0]);
.template SetAsType<dst_vector_t>(src_data_idx_seq,
op_r_v.template AsType<dst_vector_t>()[I0]);
constexpr auto move_on_dim = [&]() constexpr
{
......@@ -267,19 +302,15 @@ struct ThreadwiseTensorSliceTransfer_v3r1
{
#if !CK_EXPERIMENTAL_USE_IN_REGISTER_SUB_DWORD_TRANSPOSE
static_ford<SliceLengths>{}([&](auto idx) {
// convert from SrcData to DstData here
dst_thread_scratch_(idx) =
type_convert<DstData>(src_thread_scratch_tuple_[thread_scratch_id][idx]);
dst_thread_scratch_(idx) = src_thread_scratch_tuple_[thread_scratch_id][idx];
});
#else
// sub-dword transpose between src_thread_scratch_ and dst_thread_scratch_
// TODO make this logic more generic for more sub-dword datatype
if constexpr(SrcVectorDim != DstVectorDim &&
((is_same<half_t, remove_cvref_t<SrcData>>::value &&
is_same<half_t, remove_cvref_t<DstData>>::value &&
((is_same<half_t, remove_cvref_t<DstData>>::value &&
SrcScalarPerVector % 2 == 0 && DstScalarPerVector % 2 == 0) ||
(is_same<int8_t, remove_cvref_t<SrcData>>::value &&
is_same<int8_t, remove_cvref_t<DstData>>::value &&
(is_same<int8_t, remove_cvref_t<DstData>>::value &&
SrcScalarPerVector % 4 == 0 && DstScalarPerVector % 4 == 0)))
{
// each transpose does
......@@ -313,7 +344,7 @@ struct ThreadwiseTensorSliceTransfer_v3r1
constexpr auto data_idx_seq = generate_sequence_v2(
[&](auto i) { return Number<data_idx[i]>{}; }, Number<nDim>{});
using src_vector_t = vector_type_maker_t<SrcData, SrcScalarPerVector>;
using src_vector_t = vector_type_maker_t<DstData, SrcScalarPerVector>;
using dst_vector_t = vector_type_maker_t<DstData, DstScalarPerVector>;
// get DstScalarPerVector # of read-only references to src vectors from
......@@ -336,17 +367,16 @@ struct ThreadwiseTensorSliceTransfer_v3r1
Number<num_dst_vector>{});
// do data transpose
transpose_vectors<SrcData, DstScalarPerVector, SrcScalarPerVector>{}(
transpose_vectors<DstData, DstScalarPerVector, SrcScalarPerVector>{}(
src_vector_refs, dst_vector_refs);
});
}
static_ford<SliceLengths>{}([&](auto idx) {
// apply the src elementwise op and convert to DstData under the hood if needed
DstData dst_v;
src_element_op_(dst_v, src_thread_scratch_tuple_[thread_scratch_id][idx]);
dst_thread_scratch_(idx) = dst_v;
});
else
{
static_ford<SliceLengths>{}([&](auto idx) {
dst_thread_scratch_(idx) = src_thread_scratch_tuple_[thread_scratch_id][idx];
});
}
#endif
}
......@@ -761,11 +791,12 @@ struct ThreadwiseTensorSliceTransfer_v3r1
static constexpr auto src_thread_scratch_desc_ = decltype(GetSrcThreadScratchDescriptor()){};
static constexpr auto dst_thread_scratch_desc_ = decltype(GetDstThreadScratchDescriptor()){};
using SrcThreadScratch = StaticTensorTupleOfVectorBuffer<AddressSpaceEnum::Vgpr,
SrcData,
SrcScalarPerVector,
decltype(src_thread_scratch_desc_),
true>;
using SrcThreadScratch =
StaticTensorTupleOfVectorBuffer<AddressSpaceEnum::Vgpr,
DstData, // apply data_convert with SrcThreadScratch
SrcScalarPerVector,
decltype(src_thread_scratch_desc_),
true>;
using DstThreadScratch = StaticTensorTupleOfVectorBuffer<AddressSpaceEnum::Vgpr,
DstData,
......
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v7r2.hpp
View file @ f30e5975
......@@ -132,9 +132,6 @@ struct ThreadwiseTensorSliceTransfer_v7r2
Number<num>{});
}
template <typename T>
using has_vec_len = decltype(std::declval<T&>().vec_len);
// SrcDescs: Tuple<const SrcDesc0&, const SrcDesc1&, ...>
// SrcBuffers: Tuple<const SrcBuffer0&, const SrcBuffer1&, ...>
template <typename SrcBuffers,
......@@ -159,94 +156,63 @@ struct ThreadwiseTensorSliceTransfer_v7r2
is_src_valid);
});
if constexpr(is_detected<has_vec_len, decltype(element_op_)>::value)
{
constexpr auto elem_op_vec_len = decltype(element_op_)::vec_len;
static_assert(is_same<remove_cvref_t<decltype(elem_op_vec_len)>, index_t>::value,
"vec_len in element_op_ type is not index_t");
constexpr auto get_elem_op_vec_len = []() {
if constexpr(is_detected<is_pack8_invocable_t, decltype(element_op_)>::value)
{
if constexpr(decltype(element_op_)::is_pack8_invocable)
return math::min(8, SrcScalarPerVector);
}
if constexpr(is_detected<is_pack4_invocable_t, decltype(element_op_)>::value)
{
if constexpr(decltype(element_op_)::is_pack4_invocable)
return math::min(4, SrcScalarPerVector);
}
if constexpr(is_detected<is_pack2_invocable_t, decltype(element_op_)>::value)
{
if constexpr(decltype(element_op_)::is_pack2_invocable)
return math::min(2, SrcScalarPerVector);
}
return 1;
};
constexpr index_t elem_op_vec_len = get_elem_op_vec_len();
// apply pointwise function
static_for<0, SrcScalarPerVector / elem_op_vec_len, 1>{}([&](auto i) {
// get reference to src data
const auto src_data_refs = generate_tie(
// return type should be lvalue
[&](auto iSrc) -> const auto& {
using SrcData = remove_cvref_t<tuple_element_t<iSrc.value, SrcDatas>>;
using elem_op_vec_t = typename vector_type<SrcData, elem_op_vec_len>::type;
return src_vectors[iSrc].template AsType<elem_op_vec_t>()[i];
},
Number<nSrc>{});
// get reference to dst data
auto dst_data_refs = generate_tie(
// return type should be lvalue
[&](auto iDst) -> auto& {
using DstData = remove_cvref_t<tuple_element_t<iDst.value, DstDatas>>;
using elem_op_vec_t = typename vector_type<DstData, elem_op_vec_len>::type;
return dst_vectors(iDst).template AsType<elem_op_vec_t>()(i);
},
Number<nDst>{});
static_assert(elem_op_vec_len == 1 || elem_op_vec_len == 2 ||
elem_op_vec_len == 4 || elem_op_vec_len == 8,
"vec_len in element_op_ must be 1, 2, 4, 8");
static_assert(SrcScalarPerVector % elem_op_vec_len == 0,
"vec_len in element_op_ cannot be divided by SrcScalarPerVector!");
// apply pointwise function
static_for<0, SrcScalarPerVector / elem_op_vec_len, 1>{}([&](auto i) {
// get reference to src data
const auto src_data_refs = generate_tie(
// return type should be lvalue
[&](auto iSrc) -> const auto& {
using SrcData = remove_cvref_t<tuple_element_t<iSrc.value, SrcDatas>>;
using elem_op_vec_t =
typename vector_type<SrcData, elem_op_vec_len>::type;
return src_vectors[iSrc].template AsType<elem_op_vec_t>()[i];
},
Number<nSrc>{});
// get reference to dst data
auto dst_data_refs = generate_tie(
// return type should be lvalue
[&](auto iDst) -> auto& {
using DstData = remove_cvref_t<tuple_element_t<iDst.value, DstDatas>>;
using elem_op_vec_t =
typename vector_type<DstData, elem_op_vec_len>::type;
return dst_vectors(iDst).template AsType<elem_op_vec_t>()(i);
},
Number<nDst>{});
// apply pointwise function
// pointwise function signature:
// element_op_(dst_data_refs[I0],
// dst_data_refs[I1],
// ...,
// src_data_refs[I0],
// src_data_refs[I1],
// ...)
unpack2(element_op_, dst_data_refs, src_data_refs);
});
}
else
{
// apply pointwise function
static_for<0, SrcScalarPerVector, 1>{}([&](auto i) {
// get reference to src data
const auto src_data_refs = generate_tie(
// return type should be lvalue
[&](auto iSrc) -> const auto& {
using SrcData = remove_cvref_t<tuple_element_t<iSrc.value, SrcDatas>>;
return src_vectors[iSrc].template AsType<SrcData>()[i];
},
Number<nSrc>{});
// get reference to dst data
auto dst_data_refs = generate_tie(
// return type should be lvalue
[&](auto iDst) -> auto& {
using DstData = remove_cvref_t<tuple_element_t<iDst.value, DstDatas>>;
return dst_vectors(iDst).template AsType<DstData>()(i);
},
Number<nDst>{});
// apply pointwise function
// pointwise function signature:
// element_op_(dst_data_refs[I0],
// dst_data_refs[I1],
// ...,
// src_data_refs[I0],
// src_data_refs[I1],
// ...)
unpack2(element_op_, dst_data_refs, src_data_refs);
});
}
// pointwise function signature:
// element_op_(dst_data_refs[I0],
// dst_data_refs[I1],
// ...,
// src_data_refs[I0],
// src_data_refs[I1],
// ...)
unpack2(element_op_, dst_data_refs, src_data_refs);
});
dst_vectors_tuple_(iAccess) = dst_vectors;
......
include/ck/tensor_operation/gpu/warp/xdlops_gemm.hpp
View file @ f30e5975
......@@ -462,7 +462,6 @@ struct mfma_type<MfmaInstr::mfma_f64_16x16x4f64>
}
};
#if defined CK_ENABLE_FP8
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x16f8f8>
{
......@@ -506,9 +505,7 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32f8f8>
intrin_mfma_f32_16x16x32f8f8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
#endif
#if defined CK_ENABLE_BF8
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x16bf8bf8>
{
......@@ -552,9 +549,7 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32bf8bf8>
intrin_mfma_f32_16x16x32bf8bf8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
#endif
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x16f8bf8>
{
......@@ -598,9 +593,7 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32f8bf8>
intrin_mfma_f32_16x16x32f8bf8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
#endif
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x16bf8f8>
{
......@@ -644,7 +637,6 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32bf8f8>
intrin_mfma_f32_16x16x32bf8f8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
#endif
template <typename base_type,
index_t MPerXdlops,
......@@ -792,7 +784,6 @@ struct MfmaSelector
}
#endif
#if defined CK_ENABLE_FP8
template <>
static constexpr auto GetMfma<f8_t, 32, 32>()
{
......@@ -804,9 +795,7 @@ struct MfmaSelector
{
return MfmaInstr::mfma_f32_16x16x32f8f8;
}
#endif
#if defined CK_ENABLE_BF8
template <>
static constexpr auto GetMfma<bf8_t, 32, 32>()
{
......@@ -818,9 +807,7 @@ struct MfmaSelector
{
return MfmaInstr::mfma_f32_16x16x32bf8bf8;
}
#endif
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
template <>
static constexpr auto GetMfma<f8_t, 32, 32, bf8_t>()
{
......@@ -832,9 +819,7 @@ struct MfmaSelector
{
return MfmaInstr::mfma_f32_16x16x32f8bf8;
}
#endif
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
template <>
static constexpr auto GetMfma<bf8_t, 32, 32, f8_t>()
{
......@@ -846,7 +831,6 @@ struct MfmaSelector
{
return MfmaInstr::mfma_f32_16x16x32bf8f8;
}
#endif
static constexpr auto selected_mfma =
mfma_type<GetMfma<base_type, MPerXdlops, NPerXdlops, additional_type>()>{};
......@@ -1051,18 +1035,10 @@ struct XdlopsGemm
static_assert(
is_same<base_type, double>::value || is_same<base_type, float>::value ||
is_same<base_type, half_t>::value || is_same<base_type, bhalf_t>::value ||
is_same<base_type, int8_t>::value
#if defined CK_ENABLE_FP8
|| is_same<base_type, f8_t>::value
#endif
#if defined CK_ENABLE_BF8
|| is_same<base_type, bf8_t>::value
#endif
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
|| (is_same<base_type, f8_t>::value && is_same<additional_type, bf8_t>::value) ||
(is_same<base_type, bf8_t>::value && is_same<additional_type, f8_t>::value)
#endif
,
is_same<base_type, int8_t>::value || is_same<base_type, f8_t>::value ||
is_same<base_type, bf8_t>::value ||
(is_same<base_type, f8_t>::value && is_same<additional_type, bf8_t>::value) ||
(is_same<base_type, bf8_t>::value && is_same<additional_type, f8_t>::value),
"base base_type must be double, float, half, bfloat16, int8_t, f8_t or bf8_t!");
static_for<0, KPack / mfma_instr.k_per_blk, 1>{}([&](auto k) {
......
include/ck/utility/amd_buffer_addressing.hpp
View file @ f30e5975
......@@ -299,584 +299,255 @@ enum struct AmdBufferCoherenceEnum
GLC_SLC = 3,
};
template <typename T,
index_t N,
AmdBufferCoherenceEnum coherence = AmdBufferCoherenceEnum::DefaultCoherence>
__device__ typename vector_type<T, N>::type amd_buffer_load_impl(int32x4_t src_wave_buffer_resource,
index_t src_thread_addr_offset,
index_t src_wave_addr_offset)
template <index_t N, AmdBufferCoherenceEnum coherence = AmdBufferCoherenceEnum::DefaultCoherence>
__device__ typename vector_type<int8_t, N>::type
amd_buffer_load_impl_raw(int32x4_t src_wave_buffer_resource,
index_t src_thread_addr_offset,
index_t src_wave_addr_offset)
{
static_assert(
(is_same<T, double>::value && (N == 1 || N == 2 || N == 4)) ||
(is_same<T, float>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, half_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, bhalf_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, int32_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, int8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)),
"wrong! not implemented");
static_assert(N == 1 || N == 2 || N == 4 || N == 8 || N == 16 || N == 32 || N == 64,
"wrong! not implemented");
if constexpr(is_same<T, double>::value)
if constexpr(N == 1)
{
// use fp32 load to mimic fp64 load
if constexpr(N == 1)
{
const float2_t tmp =
llvm_amdgcn_raw_buffer_load_fp32x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
return bit_cast<double>(tmp);
}
else if constexpr(N == 2)
{
const float4_t tmp =
llvm_amdgcn_raw_buffer_load_fp32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
return bit_cast<double2_t>(tmp);
}
else if constexpr(N == 4)
{
const float4_t f32_0 =
llvm_amdgcn_raw_buffer_load_fp32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
const float4_t f32_1 =
llvm_amdgcn_raw_buffer_load_fp32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 4 * sizeof(float),
static_cast<index_t>(coherence));
vector_type<double, 4> tmp;
tmp.AsType<double2_t>()(Number<0>{}) = bit_cast<double2_t>(f32_0);
tmp.AsType<double2_t>()(Number<1>{}) = bit_cast<double2_t>(f32_1);
return tmp.AsType<double4_t>()(Number<0>{});
}
return llvm_amdgcn_raw_buffer_load_i8(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(is_same<T, float>::value)
else if constexpr(N == 2)
{
if constexpr(N == 1)
{
return llvm_amdgcn_raw_buffer_load_fp32(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
return llvm_amdgcn_raw_buffer_load_fp32x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
return llvm_amdgcn_raw_buffer_load_fp32x4(src_wave_buffer_resource,
int16_t tmp = llvm_amdgcn_raw_buffer_load_i16(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 8)
{
vector_type<float, 8> tmp;
tmp.AsType<float4_t>()(Number<0>{}) =
llvm_amdgcn_raw_buffer_load_fp32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
tmp.AsType<float4_t>()(Number<1>{}) =
llvm_amdgcn_raw_buffer_load_fp32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 4 * sizeof(float),
static_cast<index_t>(coherence));
return tmp.AsType<float8_t>()(Number<0>{});
}
return bit_cast<int8x2_t>(tmp);
}
else if constexpr(is_same<T, half_t>::value)
else if constexpr(N == 4)
{
if constexpr(N == 1)
{
return llvm_amdgcn_raw_buffer_load_fp16(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
return llvm_amdgcn_raw_buffer_load_fp16x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
return llvm_amdgcn_raw_buffer_load_fp16x4(src_wave_buffer_resource,
int32_t tmp = llvm_amdgcn_raw_buffer_load_i32(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 8)
{
// use fp32 load to mimic fp16 load
float4_t tmp = llvm_amdgcn_raw_buffer_load_fp32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
return bit_cast<half8_t>(tmp);
}
}
else if constexpr(is_same<T, bhalf_t>::value)
{
if constexpr(N == 1)
{
return llvm_amdgcn_raw_buffer_load_i16(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
return llvm_amdgcn_raw_buffer_load_i16x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
return llvm_amdgcn_raw_buffer_load_i16x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 8)
{
int32x4_t tmp = llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
return bit_cast<bhalf8_t>(tmp);
}
return bit_cast<int8x4_t>(tmp);
}
else if constexpr(is_same<T, int32_t>::value)
else if constexpr(N == 8)
{
if constexpr(N == 1)
{
return llvm_amdgcn_raw_buffer_load_i32(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
return llvm_amdgcn_raw_buffer_load_i32x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
return llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 8)
{
vector_type<int32_t, 8> tmp;
tmp.AsType<int32x4_t>()(Number<0>{}) =
llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
tmp.AsType<int32x4_t>()(Number<1>{}) =
llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 4 * sizeof(int32_t),
static_cast<index_t>(coherence));
return tmp.AsType<int32x8_t>()(Number<0>{});
}
}
else if constexpr(is_same<T, int8_t>::value)
{
if constexpr(N == 1)
{
return llvm_amdgcn_raw_buffer_load_i8(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
return llvm_amdgcn_raw_buffer_load_i8x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
#else
int16_t tmp = llvm_amdgcn_raw_buffer_load_i16(src_wave_buffer_resource,
int32x2_t tmp = llvm_amdgcn_raw_buffer_load_i32x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
return bit_cast<int8x2_t>(tmp);
#endif
}
else if constexpr(N == 4)
{
#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
return llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
#else
int32_t tmp = llvm_amdgcn_raw_buffer_load_i32(src_wave_buffer_resource,
return bit_cast<int8x8_t>(tmp);
}
else if constexpr(N == 16)
{
int32x4_t tmp = llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
return bit_cast<int8x16_t>(tmp);
}
else if constexpr(N == 32)
{
int32x4_t tmp0 = llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
int32x4_t tmp1 =
llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 4 * sizeof(int32_t),
static_cast<index_t>(coherence));
vector_type<int32_t, 8> tmp;
return bit_cast<int8x4_t>(tmp);
#endif
}
else if constexpr(N == 8)
{
#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
vector_type<int8_t, 8> tmp;
tmp.AsType<int8x4_t>()(Number<0>{}) =
llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
tmp.AsType<int8x4_t>()(Number<1>{}) =
llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 4 * sizeof(int8_t),
static_cast<index_t>(coherence));
return tmp.AsType<int8x8_t>()(Number<0>{});
#else
int32x2_t tmp = llvm_amdgcn_raw_buffer_load_i32x2(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
tmp.AsType<int32x4_t>()(Number<0>{}) = tmp0;
tmp.AsType<int32x4_t>()(Number<1>{}) = tmp1;
return bit_cast<int8x8_t>(tmp);
#endif
}
else if constexpr(N == 16)
{
#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
vector_type<int8_t, 16> tmp;
tmp.AsType<int8x4_t>()(Number<0>{}) =
llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
tmp.AsType<int8x4_t>()(Number<1>{}) =
llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 4 * sizeof(int8_t),
static_cast<index_t>(coherence));
tmp.AsType<int8x4_t>()(Number<2>{}) =
llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 8 * sizeof(int8_t),
static_cast<index_t>(coherence));
tmp.AsType<int8x4_t>()(Number<3>{}) =
llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 12 * sizeof(int8_t),
static_cast<index_t>(coherence));
return tmp.AsType<int8x16_t>()(Number<0>{});
#else
int32x4_t tmp = llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
return bit_cast<int8x32_t>(tmp);
}
else if constexpr(N == 64)
{
int32x4_t tmp0 = llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset,
static_cast<index_t>(coherence));
int32x4_t tmp1 =
llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 4 * sizeof(int32_t),
static_cast<index_t>(coherence));
int32x4_t tmp2 =
llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 8 * sizeof(int32_t),
static_cast<index_t>(coherence));
int32x4_t tmp3 =
llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
src_thread_addr_offset,
src_wave_addr_offset + 12 * sizeof(int32_t),
static_cast<index_t>(coherence));
return bit_cast<int8x16_t>(tmp);
#endif
}
vector_type<int32_t, 16> tmp;
tmp.AsType<int32x4_t>()(Number<0>{}) = tmp0;
tmp.AsType<int32x4_t>()(Number<1>{}) = tmp1;
tmp.AsType<int32x4_t>()(Number<2>{}) = tmp2;
tmp.AsType<int32x4_t>()(Number<3>{}) = tmp3;
return bit_cast<int8x64_t>(tmp);
}
}
template <typename T,
index_t N,
AmdBufferCoherenceEnum coherence = AmdBufferCoherenceEnum::DefaultCoherence>
__device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src_thread_data,
int32x4_t dst_wave_buffer_resource,
index_t dst_thread_addr_offset,
index_t dst_wave_addr_offset)
__device__ typename vector_type<T, N>::type amd_buffer_load_impl(int32x4_t src_wave_buffer_resource,
index_t src_thread_addr_offset,
index_t src_wave_addr_offset)
{
static_assert(
(is_same<T, double>::value && (N == 1 || N == 2)) ||
(is_same<T, float>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, half_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, bhalf_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, int32_t>::value && (N == 1 || N == 2 || N == 4)) ||
(is_same<T, double>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, float>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, half_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, bhalf_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, int32_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, f8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, bf8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, int8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)),
"wrong! not implemented");
if constexpr(is_same<T, double>::value)
using r_t = typename vector_type<T, N>::type;
auto raw_data = amd_buffer_load_impl_raw<sizeof(T) * N, coherence>(
src_wave_buffer_resource, src_thread_addr_offset, src_wave_addr_offset);
return bit_cast<r_t>(raw_data);
}
template <index_t N, AmdBufferCoherenceEnum coherence = AmdBufferCoherenceEnum::DefaultCoherence>
__device__ void
amd_buffer_store_impl_raw(const typename vector_type<int8_t, N>::type src_thread_data,
int32x4_t dst_wave_buffer_resource,
index_t dst_thread_addr_offset,
index_t dst_wave_addr_offset)
{
static_assert(N == 1 || N == 2 || N == 4 || N == 8 || N == 16 || N == 32 || N == 64,
"wrong! not implemented");
if constexpr(N == 1)
{
// use fp32 store to mimic fp64 store
if constexpr(N == 1)
{
llvm_amdgcn_raw_buffer_store_fp32x2(bit_cast<float2_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
llvm_amdgcn_raw_buffer_store_fp32x4(bit_cast<float4_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
llvm_amdgcn_raw_buffer_store_i8(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(is_same<T, float>::value)
else if constexpr(N == 2)
{
if constexpr(N == 1)
{
llvm_amdgcn_raw_buffer_store_fp32(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
llvm_amdgcn_raw_buffer_store_fp32x2(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
llvm_amdgcn_raw_buffer_store_fp32x4(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 8)
{
vector_type<float, 8> tmp{src_thread_data};
llvm_amdgcn_raw_buffer_store_fp32x4(tmp.AsType<float4_t>()[Number<0>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
llvm_amdgcn_raw_buffer_store_fp32x4(tmp.AsType<float4_t>()[Number<1>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset + 4 * sizeof(float),
static_cast<index_t>(coherence));
}
llvm_amdgcn_raw_buffer_store_i16(bit_cast<int16_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(is_same<T, half_t>::value)
else if constexpr(N == 4)
{
if constexpr(N == 1)
{
llvm_amdgcn_raw_buffer_store_fp16(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
llvm_amdgcn_raw_buffer_store_fp16x2(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
llvm_amdgcn_raw_buffer_store_fp16x4(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 8)
{
#if 0
vector_type<half_t, 8> tmp{src_thread_data};
llvm_amdgcn_raw_buffer_store_fp16x4(tmp.AsType<half4_t>()[Number<0>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
llvm_amdgcn_raw_buffer_store_fp16x4(tmp.AsType<half4_t>()[Number<1>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset + 4 * sizeof(half_t),
static_cast<index_t>(coherence));
#else
llvm_amdgcn_raw_buffer_store_fp32x4(bit_cast<float4_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
#endif
}
llvm_amdgcn_raw_buffer_store_i32(bit_cast<int32_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(is_same<T, bhalf_t>::value)
else if constexpr(N == 8)
{
if constexpr(N == 1)
{
llvm_amdgcn_raw_buffer_store_i16(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
llvm_amdgcn_raw_buffer_store_i16x2(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
llvm_amdgcn_raw_buffer_store_i16x4(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 8)
{
vector_type<bhalf_t, 8> tmp{src_thread_data};
llvm_amdgcn_raw_buffer_store_i16x4(tmp.AsType<bhalf4_t>()[Number<0>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
llvm_amdgcn_raw_buffer_store_i16x4(tmp.AsType<bhalf4_t>()[Number<1>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset + 4 * sizeof(bhalf_t),
static_cast<index_t>(coherence));
}
llvm_amdgcn_raw_buffer_store_i32x2(bit_cast<int32x2_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(is_same<T, int32_t>::value)
else if constexpr(N == 16)
{
if constexpr(N == 1)
{
llvm_amdgcn_raw_buffer_store_i32(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
llvm_amdgcn_raw_buffer_store_i32x2(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 4)
{
llvm_amdgcn_raw_buffer_store_i32x4(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
llvm_amdgcn_raw_buffer_store_i32x4(bit_cast<int32x4_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(is_same<T, int8_t>::value)
else if constexpr(N == 32)
{
if constexpr(N == 1)
{
llvm_amdgcn_raw_buffer_store_i8(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 2)
{
#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
llvm_amdgcn_raw_buffer_store_i8x2(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
#else
llvm_amdgcn_raw_buffer_store_i16(bit_cast<int16_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
#endif
}
else if constexpr(N == 4)
{
#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
llvm_amdgcn_raw_buffer_store_i8x4(src_thread_data,
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
#else
llvm_amdgcn_raw_buffer_store_i32(bit_cast<int32_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
#endif
}
else if constexpr(N == 8)
{
llvm_amdgcn_raw_buffer_store_i32x2(bit_cast<int32x2_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
else if constexpr(N == 16)
{
llvm_amdgcn_raw_buffer_store_i32x4(bit_cast<int32x4_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
}
vector_type<int32_t, 8> tmp{bit_cast<int32x8_t>(src_thread_data)};
llvm_amdgcn_raw_buffer_store_i32x4(tmp.template AsType<int32x4_t>()[Number<0>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
llvm_amdgcn_raw_buffer_store_i32x4(tmp.template AsType<int32x4_t>()[Number<1>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset + sizeof(int32_t) * 4,
static_cast<index_t>(coherence));
}
else if constexpr(N == 64)
{
vector_type<int32_t, 16> tmp{bit_cast<int32x16_t>(src_thread_data)};
llvm_amdgcn_raw_buffer_store_i32x4(tmp.template AsType<int32x4_t>()[Number<0>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset,
static_cast<index_t>(coherence));
llvm_amdgcn_raw_buffer_store_i32x4(tmp.template AsType<int32x4_t>()[Number<1>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset + sizeof(int32_t) * 4,
static_cast<index_t>(coherence));
llvm_amdgcn_raw_buffer_store_i32x4(tmp.template AsType<int32x4_t>()[Number<2>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset + sizeof(int32_t) * 8,
static_cast<index_t>(coherence));
llvm_amdgcn_raw_buffer_store_i32x4(tmp.template AsType<int32x4_t>()[Number<3>{}],
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset + sizeof(int32_t) * 12,
static_cast<index_t>(coherence));
}
}
template <typename T,
index_t N,
AmdBufferCoherenceEnum coherence = AmdBufferCoherenceEnum::DefaultCoherence>
__device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src_thread_data,
int32x4_t dst_wave_buffer_resource,
index_t dst_thread_addr_offset,
index_t dst_wave_addr_offset)
{
static_assert(
(is_same<T, double>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
(is_same<T, float>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, half_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, bhalf_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, int32_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, f8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, bf8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
(is_same<T, int8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)),
"wrong! not implemented");
using r_t = typename vector_type<int8_t, sizeof(T) * N>::type;
amd_buffer_store_impl_raw<sizeof(T) * N, coherence>(bit_cast<r_t>(src_thread_data),
dst_wave_buffer_resource,
dst_thread_addr_offset,
dst_wave_addr_offset);
}
template <typename T, index_t N>
......@@ -1127,54 +798,14 @@ amd_buffer_load_invalid_element_return_zero(const T* p_src_wave,
#if CK_EXPERIMENTAL_USE_BUFFER_LOAD_OOB_CHECK_OFFSET_TRICK
uint32_t src_addr_shift = src_thread_element_valid ? 0 : 0x80000000;
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value || is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 && !defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value)
#endif
#if !defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
{
auto tmp = amd_buffer_load_impl<int8_t, vector_size, coherence>(
src_wave_buffer_resource, src_addr_shift + src_thread_addr_offset, 0);
return bit_cast<vector_t>(tmp);
}
else
{
#endif
return amd_buffer_load_impl<scalar_t, vector_size, coherence>(
src_wave_buffer_resource, src_addr_shift + src_thread_addr_offset, 0);
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
}
#endif
return amd_buffer_load_impl<scalar_t, vector_size, coherence>(
src_wave_buffer_resource, src_addr_shift + src_thread_addr_offset, 0);
#else
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value || is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 && !defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value)
#endif
#if !defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
{
auto tmp = amd_buffer_load_impl<int8_t, vector_size, coherence>(
src_wave_buffer_resource, src_thread_addr_offset, 0);
return src_thread_element_valid ? bit_cast<vector_t>(tmp) : vector_t(0);
}
else
{
#endif
vector_t tmp = amd_buffer_load_impl<scalar_t, vector_size, coherence>(
src_wave_buffer_resource, src_thread_addr_offset, 0);
return src_thread_element_valid ? tmp : vector_t(0);
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
}
#endif
vector_t tmp = amd_buffer_load_impl<scalar_t, vector_size, coherence>(
src_wave_buffer_resource, src_thread_addr_offset, 0);
return src_thread_element_valid ? tmp : vector_t(0);
#endif
}
......@@ -1232,62 +863,13 @@ __device__ void amd_buffer_store(const typename vector_type_maker<T, N>::type::t
#if CK_EXPERIMENTAL_USE_BUFFER_STORE_OOB_CHECK_OFFSET_TRICK
uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x80000000;
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value || is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 && !defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value)
#endif
#if !defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
{
auto tmp = bit_cast<typename vector_type_maker<int8_t, vector_size>::type::type>(
src_thread_data);
amd_buffer_store_impl<int8_t, vector_size, coherence>(
tmp, dst_wave_buffer_resource, dst_addr_shift + dst_thread_addr_offset, 0);
}
else
{
#endif
amd_buffer_store_impl<scalar_t, vector_size, coherence>(src_thread_data,
dst_wave_buffer_resource,
dst_addr_shift +
dst_thread_addr_offset,
0);
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
}
#endif
amd_buffer_store_impl<scalar_t, vector_size, coherence>(
src_thread_data, dst_wave_buffer_resource, dst_addr_shift + dst_thread_addr_offset, 0);
#else
if(dst_thread_element_valid)
{
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value || is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 && !defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, f8_t>::value)
#endif
#if !defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
if constexpr(is_same<scalar_t, bf8_t>::value)
#endif
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
{
auto tmp =
bit_cast<typename vector_type_maker<int8_t, vector_size>::type::type>(
src_thread_data);
amd_buffer_store_impl<int8_t, vector_size, coherence>(
tmp, dst_wave_buffer_resource, dst_thread_addr_offset, 0);
}
else
{
#endif
amd_buffer_store_impl<scalar_t, vector_size, coherence>(
src_thread_data, dst_wave_buffer_resource, dst_thread_addr_offset, 0);
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
}
#endif
amd_buffer_store_impl<scalar_t, vector_size, coherence>(
src_thread_data, dst_wave_buffer_resource, dst_thread_addr_offset, 0);
}
#endif
}
......
include/ck/utility/amd_xdlops.hpp
View file @ f30e5975
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#ifndef CK_AMD_XDLOPS_HPP
#define CK_AMD_XDLOPS_HPP
#include "data_type.hpp"
#pragma once
namespace ck {
......@@ -355,7 +352,6 @@ struct intrin_mfma_f64_16x16x4f64<16, 16>
}
};
#if defined CK_ENABLE_FP8
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x16f8f8;
......@@ -418,9 +414,7 @@ struct intrin_mfma_f32_16x16x32f8f8<16, 16>
#endif
}
};
#endif
#if defined CK_ENABLE_BF8
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x16bf8bf8;
......@@ -483,9 +477,7 @@ struct intrin_mfma_f32_16x16x32bf8bf8<16, 16>
#endif
}
};
#endif
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x16f8bf8;
......@@ -548,9 +540,7 @@ struct intrin_mfma_f32_16x16x32f8bf8<16, 16>
#endif
}
};
#endif
#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x16bf8f8;
......@@ -613,6 +603,5 @@ struct intrin_mfma_f32_16x16x32bf8f8<16, 16>
#endif
}
};
#endif
} // namespace ck
#endif
include/ck/utility/data_type.hpp
View file @ f30e5975
......@@ -9,15 +9,9 @@ namespace ck {
using bhalf_t = ushort;
using half_t = _Float16;
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
using int4_t = _BitInt(4);
#endif
#if defined CK_ENABLE_FP8
using f8_t = _BitInt(8);
#endif
#if defined CK_ENABLE_BF8
using bf8_t = unsigned _BitInt(8);
#endif
using int4_t = _BitInt(4);
using f8_t = _BitInt(8);
using bf8_t = unsigned _BitInt(8);
// vector_type
template <typename T, index_t N>
......@@ -148,23 +142,19 @@ struct scalar_type<int4_t>
};
#endif
#if defined CK_ENABLE_FP8
template <>
struct scalar_type<f8_t>
{
using type = f8_t;
static constexpr index_t vector_size = 1;
};
#endif
#if defined CK_ENABLE_BF8
template <>
struct scalar_type<bf8_t>
{
using type = bf8_t;
static constexpr index_t vector_size = 1;
};
#endif
template <typename T>
struct vector_type<T, 1>
......@@ -968,24 +958,20 @@ using int8x32_t = typename vector_type<int8_t, 32>::type;
using int8x64_t = typename vector_type<int8_t, 64>::type;
// f8
#if defined CK_ENABLE_FP8
using f8x2_t = typename vector_type<f8_t, 2>::type;
using f8x4_t = typename vector_type<f8_t, 4>::type;
using f8x8_t = typename vector_type<f8_t, 8>::type;
using f8x16_t = typename vector_type<f8_t, 16>::type;
using f8x32_t = typename vector_type<f8_t, 32>::type;
using f8x64_t = typename vector_type<f8_t, 64>::type;
#endif
// bf8
#if defined CK_ENABLE_BF8
using bf8x2_t = typename vector_type<bf8_t, 2>::type;
using bf8x4_t = typename vector_type<bf8_t, 4>::type;
using bf8x8_t = typename vector_type<bf8_t, 8>::type;
using bf8x16_t = typename vector_type<bf8_t, 16>::type;
using bf8x32_t = typename vector_type<bf8_t, 32>::type;
using bf8x64_t = typename vector_type<bf8_t, 64>::type;
#endif
template <typename T>
struct NumericLimits
......@@ -1033,7 +1019,6 @@ struct NumericLimits<int4_t>
};
#endif // CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
#if defined CK_ENABLE_FP8
template <>
struct NumericLimits<f8_t>
{
......@@ -1056,9 +1041,7 @@ struct NumericLimits<f8_t>
__host__ __device__ static constexpr f8_t QuietNaN() { return f8_t(binary_qnan); }
};
#endif
#if defined CK_ENABLE_BF8
template <>
struct NumericLimits<bf8_t>
{
......@@ -1081,7 +1064,6 @@ struct NumericLimits<bf8_t>
__host__ __device__ static constexpr bf8_t QuietNaN() { return bf8_t(binary_qnan); }
};
#endif
template <typename T>
struct NumericUtils
......@@ -1093,6 +1075,7 @@ struct NumericUtils<float>
{
static constexpr int exp = 8;
static constexpr int mant = 23;
static constexpr int bias = 127;
static constexpr uint32_t nan_mask = 0x7F800000;
static constexpr uint32_t head_mask = 0xFF800000;
static constexpr uint32_t mant_mask = 0x7FFFFF;
......@@ -1109,6 +1092,7 @@ struct NumericUtils<half_t>
{
static constexpr int exp = 5;
static constexpr int mant = 10;
static constexpr int bias = 15;
static constexpr uint16_t nan_mask = 0x7C00;
static constexpr uint16_t head_mask = 0xFC00;
static constexpr uint16_t mant_mask = 0x3FF;
......@@ -1120,22 +1104,21 @@ struct NumericUtils<half_t>
using bitwise_type = uint16_t;
};
#if defined CK_ENABLE_FP8
template <>
struct NumericUtils<f8_t>
{
static constexpr int exp = 4;
static constexpr int mant = 3;
static constexpr int bias = 8; // negative zero nan mode
// static constexpr int bias = 7; // ieee mode
};
#endif
#if defined CK_ENABLE_BF8
template <>
struct NumericUtils<bf8_t>
{
static constexpr int exp = 5;
static constexpr int mant = 2;
static constexpr int bias = 16; // negative zero nan mode
// static constexpr int bias = 15; // ieee mode
};
#endif
} // namespace ck
include/ck/utility/f8_utils.hpp
View file @ f30e5975
......@@ -5,9 +5,6 @@
#include "ck/utility/data_type.hpp"
// these conversions are disabled if native conversions available
#if !defined(__gfx940__) && !defined(__gfx941__) && !defined(__gfx942__)
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
namespace ck {
// fp8 rounding modes
......@@ -19,6 +16,9 @@ enum class f8_rounding_mode
stochastic
};
__host__ inline int clz(uint32_t x) { return __builtin_clz(x); }
__device__ inline int clz(uint32_t x) { return __clz(x); }
} // namespace ck
namespace ck::utils {
......@@ -36,7 +36,7 @@ __host__ __device__ Y run_cast_to_f8(X x, uint32_t rng)
constexpr int in_exp = NumericUtils<X>::exp;
constexpr int in_mant = NumericUtils<X>::mant;
int exponent;
int exponent, bias;
uint32_t head, mantissa, sign;
// nan code is same for float and half
constexpr Y nan_code = 0x80;
......@@ -51,12 +51,11 @@ __host__ __device__ Y run_cast_to_f8(X x, uint32_t rng)
mantissa = x_bitwise & NumericUtils<X>::mant_mask;
exponent = (head >> in_mant) & NumericUtils<X>::exp_mask;
sign = head >> (in_exp + in_mant);
bias = NumericUtils<X>::bias;
uint32_t signed_inf = (sign << (in_exp + in_mant)) + (((1 << in_exp) - 1) << in_mant);
uint32_t drop_mask = (1 << (in_mant - out_mant)) - 1;
constexpr int max_exp = (1 << out_exp) - (negative_zero_nan ? 1 : 2);
constexpr int exp_low_cutoff =
(1 << (in_exp - 1)) - (1 << (out_exp - 1)) + 1 - (negative_zero_nan ? 1 : 0);
if constexpr(negative_zero_nan)
{
......@@ -69,56 +68,107 @@ __host__ __device__ Y run_cast_to_f8(X x, uint32_t rng)
return signed_inf + (mantissa != 0 ? 1 : 0);
}
// if input is half and output is bf8
if((NumericUtils<X>::mant == 10) && (NumericUtils<Y>::mant == 2) && negative_zero_nan &&
exponent == 0)
{
exponent += 1;
while(mantissa < (1 << in_mant))
{
mantissa <<= 1;
exponent -= 1;
}
mantissa &= ~(1 << in_mant);
}
// check if x is 0.0
if(x_bitwise == 0)
return 0;
exponent -= exp_low_cutoff - 1;
if(exponent <= 0)
drop_mask = (1 << (in_mant - out_mant + 1 - exponent)) - 1;
mantissa += 1 << in_mant;
// apply random number if needed
mantissa += (stoch ? rng : mantissa) & drop_mask;
if(mantissa >= (2 << in_mant))
{
mantissa >>= 1;
exponent++;
// First need to check if it is normal or denorm as there is a difference of implict 1
// Then need to adjust the exponent to align with the F8 exponent, in the meanwhile, shift
// The mantissa. Then for stochastic rounding, add rng to mantissa and truncate. And for
// RNE, no need to add rng. Then probably need to check whether there is carry and adjust
// exponent and mantissa again3
// For IEEE bias mode, the bias is 2^(k-1)-1 where k is the width of exponent bits
const int out_bias = (1 << (out_exp - 1)) - 1 + (negative_zero_nan ? 1 : 0);
const int out_denormal_act_exponent = 1 - out_bias; // actual exponent of f8 denormal
// act_exponent is the actual exponent of fp32/fp16 (after subtracting bias)
// out_exponent is the converted f8 exponent with bias encoding
// exponent_diff is the diff between fp32/fp16 exponent and f8 exponent,
// the difference needs to be adjusted and mantissa shifted
int act_exponent, out_exponent, exponent_diff;
if(exponent == 0)
{ // fp32/fp16 is in denormal.
/* fp32 denormal is below 2^-127 so it is usually not a concern here, we mostly concern fp16
here. In this case, f8 is usually in denormal. But there could be exceptions. fp16 denormal has
exponent bias 15 while bf8 with NANOO has exponent bias 16. It means that there are some numbers in
fp16 denormal but they are bf8 (NANOO) normals - smallest bf8 (NANOO) normal is 2^-15. fp16 numbers
where exponent==0 (actual exponent -14) and highest bit of mantissa is 1 are bf8 (NANOO) normal.
In this case, the fp16 mantissa should be shift left by 1 */
act_exponent = exponent - bias + 1;
exponent_diff = out_denormal_act_exponent -
act_exponent; // actual exponent is exponent-bias+1 as it is denormal
}
else
{ // fp32/fp16 is normal with implicit 1
act_exponent = exponent - bias;
if(act_exponent <= out_denormal_act_exponent)
{
/* This is the case where fp32/fp16 is normal but it is in f8 denormal range.
For example fp8 nanoo mode, denormal exponent is -7, but if the fp32/fp16
actual exponent is -7, it is actually larger due to the implict 1,
Therefore it needs to be adjust to -6 and mantissa shift right by 1.
So for fp32/fp16, exponent -8 is the cut point to convert to fp8 nanoo */
exponent_diff = out_denormal_act_exponent - act_exponent;
}
else
{ // both fp32/fp16 and f8 are in normal range
exponent_diff =
0; // exponent_diff=0 does not mean there is no difference for this case,
// act_exponent could be larger. Just that it does not need shift mantissa
}
mantissa += (1 << in_mant); // Add the implicit 1 into mantissa
}
mantissa >>= (in_mant - out_mant);
// check negative exponent
if(exponent <= 0)
bool midpoint = (mantissa & ((1 << (in_mant - out_mant + exponent_diff)) - 1)) ==
(1 << (in_mant - out_mant + exponent_diff - 1));
/* This part is a bit tricky. The judgment of whether it is a tie needs to be done before we
shift right as shift right could rip off some residual part and make something not midpoint look
like midpoint. For example, the fp16 number 0x1002 (0 00100 0000000010), it is larger than
midpoint, but after shift right by 4 bits, it would look like midpoint. */
if(exponent_diff > 0)
mantissa >>= exponent_diff;
else if(exponent_diff == -1)
mantissa <<= -exponent_diff;
bool implicit_one = mantissa & (1 << in_mant);
// if there is no implict 1, it means the f8 is denormal and need to adjust to denorm exponent
out_exponent =
(act_exponent + exponent_diff) /*actual f8 exponent*/ + out_bias - (implicit_one ? 0 : 1);
// Now we have the exponent and mantissa adjusted
bool odd =
mantissa &
(1 << (in_mant - out_mant)); // if the least significant bit that is not truncated is 1
mantissa += (stoch ? rng : (midpoint ? (odd ? mantissa : mantissa - 1) : mantissa)) & drop_mask;
// Now we deal with overflow
if(out_exponent == 0)
{
if(x_bitwise == 0)
return 0;
else
if((1 << in_mant) & mantissa)
{
// subnormal range; represented by a subnormal float8 (exponent 0)
// and involves loss of accuracy
mantissa >>= 1 - exponent;
exponent = 0;
out_exponent = 1; // denormal overflow to become normal, promote exponent
// No need to make 1 implicit now as it will be addressed later
}
}
// above range: quantize to maximum possible float of the same sign
else if(exponent > max_exp)
else
{
if((1 << (in_mant + 1)) & mantissa)
{
mantissa >>= 1;
out_exponent++;
// No need to make 1 implicit now as it will be addressed later
}
}
mantissa >>= (in_mant - out_mant);
if(out_exponent > max_exp)
{
if(clip)
{
mantissa = (1 << out_mant) - 1;
exponent = max_exp;
mantissa = (1 << out_mant) - 1;
out_exponent = max_exp;
}
else
{
......@@ -127,10 +177,10 @@ __host__ __device__ Y run_cast_to_f8(X x, uint32_t rng)
}
// check if x is 0.0 or -0.0
if(exponent == 0 && mantissa == 0)
if(out_exponent == 0 && mantissa == 0)
return negative_zero_nan ? 0 : (sign << (out_exp + out_mant));
mantissa &= (1 << out_mant) - 1;
return (sign << (out_exp + out_mant)) | (exponent << out_mant) | mantissa;
return (sign << (out_exp + out_mant)) | (out_exponent << out_mant) | mantissa;
}
template <typename X, typename Y, bool negative_zero_nan>
......@@ -196,12 +246,9 @@ __host__ __device__ Y run_cast_from_f8(X x)
if(exponent == 0)
{
// guaranteed mantissa!=0 since cases 0x0 and 0x80 are handled above
exponent++;
while(mantissa < (1 << in_mant))
{
mantissa <<= 1;
exponent--;
}
int sh = 1 + clz(mantissa) - (32 - in_mant);
mantissa <<= sh;
exponent += 1 - sh;
mantissa &= ((1 << in_mant) - 1);
}
exponent += exp_low_cutoff - 1;
......@@ -244,5 +291,3 @@ __host__ __device__ Y cast_from_f8(X x)
}
} // namespace ck::utils
#endif // #if defined CK_ENABLE_FP8 || defined CK_ENABLE_BF8
#endif // #if !defined(__gfx940__) && !defined(__gfx941__) && !defined(__gfx942__)
include/ck/utility/inner_product.hpp
View file @ f30e5975
......@@ -192,6 +192,8 @@ inner_product<int8x4_t, int8x4_t, int32_t>(const int8x4_t& a, const int8x4_t& b,
#else
c = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b), c, false);
#endif
#elif defined(CK_USE_AMD_V_DOT4_I32_I8_GFX11)
c = __builtin_amdgcn_sudot4(true, bit_cast<int32_t>(a), true, bit_cast<int32_t>(b), c, false);
#else
const vector_type<int8_t, 4> a_vector{a};
const vector_type<int8_t, 4> b_vector{b};
......
include/ck/utility/is_detected.hpp
View file @ f30e5975
......@@ -31,4 +31,13 @@ struct nonesuch
template <template <class...> class Op, class... Args>
using is_detected = typename detail::detector<nonesuch, void, Op, Args...>::value_t;
template <typename T>
using is_pack2_invocable_t = decltype(std::declval<T&>().is_pack2_invocable);
template <typename T>
using is_pack4_invocable_t = decltype(std::declval<T&>().is_pack4_invocable);
template <typename T>
using is_pack8_invocable_t = decltype(std::declval<T&>().is_pack8_invocable);
} // namespace ck
include/ck/utility/math.hpp
View file @ f30e5975
......@@ -150,28 +150,6 @@ __host__ __device__ constexpr T clamp(const T& x, const T& lowerbound, const T&
return min(max(x, lowerbound), upperbound);
}
// disallow implicit type casting
template <typename T>
__device__ T exp(T x);
// TODO: add f16 support using v_exp_f16
template <>
__device__ float exp<float>(float x)
{
return __expf(x);
}
template <>
__device__ double exp<double>(double x)
{
return exp(x);
}
static inline __host__ float exp(float x) { return std::expf(x); }
static inline __host__ double exp(double x) { return std::exp(x); }
// greatest common divisor, aka highest common factor
__host__ __device__ constexpr index_t gcd(index_t x, index_t y)
{
......
include/ck/utility/math_v2.hpp
View file @ f30e5975
......@@ -9,6 +9,7 @@
#include "ck/utility/data_type.hpp"
#include "ck/utility/type.hpp"
#include "ck/utility/type_convert.hpp"
namespace ck {
namespace math {
......@@ -92,14 +93,96 @@ static inline __host__ float sqrt(float x) { return std::sqrt(x); };
static inline __host__ double sqrt(double x) { return std::sqrt(x); };
static inline __host__ half_t tanh(half_t x)
template <typename T>
inline __host__ T tanh(T x)
{
return static_cast<half_t>(std::tanh(static_cast<float>(x)));
return ck::type_convert<T>(std::tanhf(ck::type_convert<float>(x)));
};
static inline __host__ float tanh(float x) { return std::tanh(x); };
template <>
inline __host__ float tanh<float>(float x)
{
return std::tanhf(x);
};
template <>
inline __host__ double tanh<double>(double x)
{
return std::tanh(x);
};
template <typename T>
inline __host__ T exp(T x)
{
return ck::type_convert<T>(std::expf(ck::type_convert<float>(x)));
}
template <>
inline __host__ float exp<float>(float x)
{
return std::expf(x);
}
static inline __host__ double tanh(double x) { return std::tanh(x); };
template <>
inline __host__ double exp<double>(double x)
{
return std::exp(x);
}
template <typename T>
inline __host__ T log(T x)
{
return ck::type_convert<T>(std::logf(ck::type_convert<float>(x)));
}
template <>
inline __host__ float log<float>(float x)
{
return std::logf(x);
}
template <>
inline __host__ double log<double>(double x)
{
return std::log(x);
}
template <typename T>
inline __host__ T pow(T x, T gamma)
{
return ck::type_convert<T>(
std::powf(ck::type_convert<float>(x), ck::type_convert<float>(gamma)));
}
template <>
inline __host__ float pow<float>(float x, float gamma)
{
return std::powf(x, gamma);
}
template <>
inline __host__ double pow<double>(double x, double gamma)
{
return std::pow(x, gamma);
}
template <typename T>
inline __host__ T expm1(T x)
{
return ck::type_convert<T>(std::expm1f(ck::type_convert<float>(x)));
}
template <>
inline __host__ float expm1<float>(float x)
{
return std::expm1f(x);
}
template <>
inline __host__ double expm1<double>(double x)
{
return std::expm1(x);
}
// math functions for the HIP kernel, some are implemented by calling hip builtin functions
......@@ -181,14 +264,107 @@ static inline __device__ float sqrt(float x) { return __builtin_amdgcn_sqrtf(x);
static inline __device__ double sqrt(double x) { return __builtin_amdgcn_sqrt(x); };
static inline __device__ half_t tanh(half_t x)
template <typename T>
inline __device__ T tanh(T x)
{
return ck::type_convert<T>(::tanhf(ck::type_convert<float>(x)));
};
template <>
inline __device__ float tanh<float>(float x)
{
return static_cast<half_t>(::tanhf(static_cast<float>(x)));
return ::tanhf(x);
};
static inline __device__ float tanh(float x) { return ::tanhf(x); };
template <>
inline __device__ double tanh<double>(double x)
{
return ::tanh(x);
};
template <typename T>
inline __device__ T exp(T x)
{
return ck::type_convert<T>(__expf(ck::type_convert<float>(x)));
};
template <>
inline __device__ half_t exp<half_t>(half_t x)
{
return hexp(x);
};
template <>
inline __device__ float exp<float>(float x)
{
return __expf(x);
};
static inline __device__ double tanh(double x) { return ::tanh(x); };
template <>
inline __device__ double exp<double>(double x)
{
return exp(x);
};
template <typename T>
inline __device__ T log(T x)
{
return ck::type_convert<T>(__logf(ck::type_convert<float>(x)));
};
template <>
inline __device__ half_t log<half_t>(half_t x)
{
return hlog(x);
};
template <>
inline __device__ float log<float>(float x)
{
return __logf(x);
};
template <>
inline __device__ double log<double>(double x)
{
return log(x);
};
template <typename T>
inline __device__ T pow(T x, T gamma)
{
return ck::type_convert<T>(powf(ck::type_convert<float>(x), ck::type_convert<float>(gamma)));
};
template <>
inline __device__ float pow<float>(float x, float gamma)
{
return powf(x, gamma);
};
template <>
inline __device__ double pow<double>(double x, double gamma)
{
return pow(x, gamma);
};
template <typename T>
inline __device__ T expm1(T x)
{
return ck::type_convert<T>(expm1f(ck::type_convert<float>(x)));
};
template <>
inline __device__ float expm1<float>(float x)
{
return expm1f(x);
};
template <>
inline __device__ double expm1<double>(double x)
{
return expm1(x);
};
} // namespace math
} // namespace ck
include/ck/utility/statically_indexed_array_multi_index.hpp
View file @ f30e5975
......@@ -5,6 +5,7 @@
#define CK_STATICALLY_INDEXED_ARRAY_MULTI_INDEX_HPP
#include "common_header.hpp"
#include "ck/utility/math_v2.hpp"
namespace ck {
......
include/ck/utility/type_convert.hpp
View file @ f30e5975
......@@ -95,7 +95,6 @@ inline __host__ __device__ constexpr bhalf_t type_convert<bhalf_t, int8_t>(int8_
return type_convert<bhalf_t>(x_fp32);
}
#if defined CK_ENABLE_FP8
// convert fp32 to fp8
template <>
inline __host__ __device__ f8_t type_convert<f8_t, float>(float x)
......@@ -169,9 +168,7 @@ inline __host__ __device__ half_t type_convert<half_t, f8_t>(f8_t x)
return utils::cast_from_f8<f8_t, half_t, negative_zero_nan>(x);
#endif
}
#endif
#if defined CK_ENABLE_BF8
// convert fp32 to bf8
template <>
inline __host__ __device__ bf8_t type_convert<bf8_t, float>(float x)
......@@ -245,7 +242,6 @@ inline __host__ __device__ half_t type_convert<half_t, bf8_t>(bf8_t x)
return utils::cast_from_f8<bf8_t, half_t, negative_zero_nan>(x);
#endif
}
#endif
// Declare a template function for bf16 conversion using RTN
template <typename Y, typename X>
......@@ -308,7 +304,6 @@ inline __host__ __device__ constexpr bhalf_t bf16_convert_rtn<bhalf_t, half_t>(h
template <typename Y, typename X>
__host__ __device__ constexpr Y f8_convert_sr(X x);
#if defined CK_ENABLE_FP8
// convert fp32 to fp8 with stochastic rounding
template <>
inline __host__ __device__ f8_t f8_convert_sr<f8_t, float>(float x)
......@@ -344,7 +339,7 @@ inline __host__ __device__ f8_t f8_convert_sr<f8_t, half_t>(half_t x)
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
// convert to float and use native converion
return f8_convert_sr<f8_t>(type_convert<float>(x));
#elif 0
#else
constexpr bool negative_zero_nan = true;
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::stochastic;
......@@ -353,13 +348,9 @@ inline __host__ __device__ f8_t f8_convert_sr<f8_t, half_t>(half_t x)
return utils::
cast_to_f8<half_t, f8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
#else
return type_convert<f8_t>(type_convert<float>(x));
#endif
}
#endif
#if defined CK_ENABLE_BF8
// convert fp32 to bf8 with stochastic rounding
template <>
inline __host__ __device__ bf8_t f8_convert_sr<bf8_t, float>(float x)
......@@ -395,7 +386,7 @@ inline __host__ __device__ bf8_t f8_convert_sr<bf8_t, half_t>(half_t x)
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
// convert to float and use native converion
return f8_convert_sr<f8_t>(type_convert<float>(x));
#elif 0
#else
constexpr bool negative_zero_nan = true;
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::stochastic;
......@@ -405,10 +396,7 @@ inline __host__ __device__ bf8_t f8_convert_sr<bf8_t, half_t>(half_t x)
return utils::
cast_to_f8<half_t, bf8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
#else
return type_convert<bf8_t>(type_convert<float>(x));
#endif
}
#endif
} // namespace ck
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View file @ f30e5975
......@@ -128,11 +128,9 @@ struct ReferenceConvFwd : public device::BaseOperator
}
}
float v_out;
arg.out_element_op_(v_out, v_acc);
arg.output_(g, n, k, wo) = ck::type_convert<OutDataType>(v_out);
OutDataType v_out;
arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc));
arg.output_(g, n, k, wo) = v_out;
};
make_ParallelTensorFunctor(func,
......@@ -184,11 +182,9 @@ struct ReferenceConvFwd : public device::BaseOperator
}
}
float v_out;
arg.out_element_op_(v_out, v_acc);
arg.output_(g, n, k, ho, wo) = ck::type_convert<OutDataType>(v_out);
OutDataType v_out;
arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc));
arg.output_(g, n, k, ho, wo) = v_out;
};
make_ParallelTensorFunctor(func,
......@@ -253,11 +249,9 @@ struct ReferenceConvFwd : public device::BaseOperator
}
}
float v_out;
arg.out_element_op_(v_out, v_acc);
arg.output_(g, n, k, d_o, ho, wo) = ck::type_convert<OutDataType>(v_out);
OutDataType v_out;
arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc));
arg.output_(g, n, k, d_o, ho, wo) = v_out;
};
make_ParallelTensorFunctor(func,
......
library/include/ck/library/reference_tensor_operation/cpu/reference_groupnorm.hpp
View file @ f30e5975
......@@ -20,8 +20,9 @@ template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename AccDataType,
typename AccElementwiseOperation>
typename SaveMeanInvStdDataType,
typename ComputeDataType,
typename YElementwiseOperation>
struct ReferenceGroupnorm : public device::BaseOperator
{
// x = [N, H, W, G, C]
......@@ -35,14 +36,18 @@ struct ReferenceGroupnorm : public device::BaseOperator
const Tensor<GammaDataType>& gamma,
const Tensor<BetaDataType>& beta,
Tensor<YDataType>& y,
AccElementwiseOperation acc_elementwise_op,
Tensor<SaveMeanInvStdDataType>& save_mean,
Tensor<SaveMeanInvStdDataType>& save_inv_std,
YElementwiseOperation y_elementwise_op,
const std::vector<index_t> lengths,
AccDataType epsilon)
ComputeDataType epsilon)
: x_(x),
gamma_(gamma),
beta_(beta),
y_(y),
acc_elementwise_op_(acc_elementwise_op),
save_mean_(save_mean),
save_inv_std_(save_inv_std),
y_elementwise_op_(y_elementwise_op),
lengths_(lengths),
epsilon_(epsilon)
{
......@@ -52,9 +57,11 @@ struct ReferenceGroupnorm : public device::BaseOperator
const Tensor<XDataType> gamma_;
const Tensor<XDataType> beta_;
Tensor<YDataType>& y_;
AccElementwiseOperation acc_elementwise_op_;
Tensor<SaveMeanInvStdDataType>& save_mean_;
Tensor<SaveMeanInvStdDataType>& save_inv_std_;
YElementwiseOperation y_elementwise_op_;
std::vector<index_t> lengths_;
AccDataType epsilon_;
ComputeDataType epsilon_;
};
// Invoker
......@@ -68,8 +75,8 @@ struct ReferenceGroupnorm : public device::BaseOperator
int G = arg.lengths_[3];
int C = arg.lengths_[4];
Tensor<AccDataType> mean({N, G});
Tensor<AccDataType> var({N, G});
Tensor<ComputeDataType> mean({N, G});
Tensor<ComputeDataType> var({N, G});
// Compute mean & var in [H, W, C] by Welford Algorithm
// TODO - parallel for each HWC
......@@ -78,9 +85,9 @@ struct ReferenceGroupnorm : public device::BaseOperator
{
for(int g = 0; g < G; ++g)
{
AccDataType mean_val = type_convert<AccDataType>(0.0f);
AccDataType var_val = type_convert<AccDataType>(0.0f);
int32_t curr_count = 0;
ComputeDataType mean_val = type_convert<ComputeDataType>(0.0f);
ComputeDataType var_val = type_convert<ComputeDataType>(0.0f);
int32_t curr_count = 0;
for(int h = 0; h < H; ++h)
{
......@@ -89,10 +96,11 @@ struct ReferenceGroupnorm : public device::BaseOperator
for(int c = 0; c < C; ++c)
{
curr_count++;
AccDataType x = type_convert<AccDataType>(arg.x_(n, h, w, g, c));
AccDataType delta = x - mean_val;
ComputeDataType x =
type_convert<ComputeDataType>(arg.x_(n, h, w, g, c));
ComputeDataType delta = x - mean_val;
mean_val += delta / curr_count;
AccDataType delta2 = x - mean_val;
ComputeDataType delta2 = x - mean_val;
var_val += delta * delta2;
}
}
......@@ -100,6 +108,12 @@ struct ReferenceGroupnorm : public device::BaseOperator
mean(n, g) = mean_val;
var(n, g) = var_val / curr_count;
arg.save_mean_(n, g) = ck::type_convert<SaveMeanInvStdDataType>(mean(n, g));
ComputeDataType divisor =
static_cast<ComputeDataType>(1) / ck::math::sqrt(var(n, g) + arg.epsilon_);
arg.save_inv_std_(n, g) = ck::type_convert<SaveMeanInvStdDataType>(divisor);
}
}
......@@ -114,15 +128,19 @@ struct ReferenceGroupnorm : public device::BaseOperator
{
for(int c = 0; c < C; ++c)
{
AccDataType x = type_convert<AccDataType>(arg.x_(n, h, w, g, c));
AccDataType gamma = type_convert<AccDataType>(arg.gamma_(g, c));
AccDataType beta = type_convert<AccDataType>(arg.beta_(g, c));
AccDataType mean_val = type_convert<AccDataType>(mean(n, g));
AccDataType var_val = type_convert<AccDataType>(var(n, g));
AccDataType y = gamma * (x - mean_val) /
ck::math::sqrt(arg.epsilon_ + var_val) +
beta;
arg.acc_elementwise_op_(y, y);
ComputeDataType x =
type_convert<ComputeDataType>(arg.x_(n, h, w, g, c));
ComputeDataType gamma =
type_convert<ComputeDataType>(arg.gamma_(g, c));
ComputeDataType beta =
type_convert<ComputeDataType>(arg.beta_(g, c));
ComputeDataType mean_val =
type_convert<ComputeDataType>(mean(n, g));
ComputeDataType var_val = type_convert<ComputeDataType>(var(n, g));
ComputeDataType y = gamma * (x - mean_val) /
ck::math::sqrt(arg.epsilon_ + var_val) +
beta;
arg.y_elementwise_op_(y, y);
arg.y_(n, h, w, g, c) = type_convert<YDataType>(y);
}
}
......@@ -159,11 +177,14 @@ struct ReferenceGroupnorm : public device::BaseOperator
const Tensor<GammaDataType>& gamma,
const Tensor<BetaDataType>& beta,
Tensor<YDataType>& y,
AccElementwiseOperation acc_elementwise_op,
Tensor<SaveMeanInvStdDataType>& save_mean,
Tensor<SaveMeanInvStdDataType>& save_inv_std,
YElementwiseOperation y_elementwise_op,
const std::vector<index_t> lengths,
AccDataType epsilon)
ComputeDataType epsilon)
{
return Argument{x, gamma, beta, y, acc_elementwise_op, lengths, epsilon};
return Argument{
x, gamma, beta, y, save_mean, save_inv_std, y_elementwise_op, lengths, epsilon};
}
static auto MakeInvoker() { return Invoker{}; }
......
library/include/ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp
View file @ f30e5975
......@@ -20,8 +20,9 @@ template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename AccDataType,
typename AccElementwiseOperation,
typename SaveMeanInvStdDataType,
typename ComputeDataType,
typename YElementwiseOperation,
index_t Rank,
index_t NumReduceDim>
struct ReferenceLayernorm : public device::BaseOperator
......@@ -36,15 +37,19 @@ struct ReferenceLayernorm : public device::BaseOperator
const Tensor<GammaDataType>& gamma_n,
const Tensor<BetaDataType>& beta_n,
Tensor<YDataType>& y_m_n,
AccElementwiseOperation acc_elementwise_op,
Tensor<SaveMeanInvStdDataType>& save_mean_m,
Tensor<SaveMeanInvStdDataType>& save_inv_std_m,
YElementwiseOperation y_elementwise_op,
const std::vector<index_t> lengths,
const std::vector<index_t> reduceDims,
AccDataType epsilon)
ComputeDataType epsilon)
: x_m_n_(x_m_n),
gamma_n_(gamma_n),
beta_n_(beta_n),
y_m_n_(y_m_n),
acc_elementwise_op_(acc_elementwise_op),
save_mean_m_(save_mean_m),
save_inv_std_m_(save_inv_std_m),
y_elementwise_op_(y_elementwise_op),
lengths_(lengths),
reduceDims_(reduceDims),
epsilon_(epsilon)
......@@ -55,10 +60,12 @@ struct ReferenceLayernorm : public device::BaseOperator
const Tensor<XDataType> gamma_n_;
const Tensor<XDataType> beta_n_;
Tensor<YDataType>& y_m_n_;
AccElementwiseOperation acc_elementwise_op_;
Tensor<SaveMeanInvStdDataType>& save_mean_m_;
Tensor<SaveMeanInvStdDataType>& save_inv_std_m_;
YElementwiseOperation y_elementwise_op_;
std::vector<index_t> lengths_;
std::vector<index_t> reduceDims_;
AccDataType epsilon_;
ComputeDataType epsilon_;
};
// Invoker
......@@ -69,8 +76,8 @@ struct ReferenceLayernorm : public device::BaseOperator
int M = arg.lengths_[0];
int N = arg.lengths_[1];
Tensor<AccDataType> mean({M});
Tensor<AccDataType> var({M});
Tensor<ComputeDataType> mean({M});
Tensor<ComputeDataType> var({M});
for(int m = 0; m < M; ++m)
{
......@@ -79,7 +86,7 @@ struct ReferenceLayernorm : public device::BaseOperator
for(int n = 0; n < N; ++n)
{
auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n));
auto x_val = ck::type_convert<ComputeDataType>(arg.x_m_n_(m, n));
mean(m) += x_val;
var(m) += x_val * x_val;
}
......@@ -90,17 +97,21 @@ struct ReferenceLayernorm : public device::BaseOperator
for(int m = 0; m < M; ++m)
{
AccDataType divisor =
static_cast<AccDataType>(1) / ck::math::sqrt(var(m) + arg.epsilon_);
ComputeDataType divisor =
static_cast<ComputeDataType>(1) / ck::math::sqrt(var(m) + arg.epsilon_);
for(int n = 0; n < N; ++n)
{
auto x_val = ck::type_convert<AccDataType>(arg.x_m_n_(m, n));
auto y_val = (x_val - mean(m)) * divisor;
y_val = (y_val * arg.gamma_n_(n)) + arg.beta_n_(n);
arg.acc_elementwise_op_(y_val, y_val);
auto x_val = ck::type_convert<ComputeDataType>(arg.x_m_n_(m, n));
auto gamma_val = ck::type_convert<ComputeDataType>(arg.gamma_n_(n));
auto beta_val = ck::type_convert<ComputeDataType>(arg.beta_n_(n));
auto y_val = (x_val - mean(m)) * divisor;
y_val = (y_val * gamma_val) + beta_val;
arg.y_elementwise_op_(y_val, y_val);
arg.y_m_n_(m, n) = ck::type_convert<YDataType>(y_val);
}
arg.save_mean_m_(m) = ck::type_convert<SaveMeanInvStdDataType>(mean(m));
arg.save_inv_std_m_(m) = ck::type_convert<SaveMeanInvStdDataType>(divisor);
}
return 0;
......@@ -140,13 +151,23 @@ struct ReferenceLayernorm : public device::BaseOperator
const Tensor<GammaDataType>& gamma_n,
const Tensor<BetaDataType>& beta_n,
Tensor<YDataType>& y_m_n,
AccElementwiseOperation acc_elementwise_op,
Tensor<SaveMeanInvStdDataType>& save_mean_m,
Tensor<SaveMeanInvStdDataType>& save_inv_std_m,
YElementwiseOperation y_elementwise_op,
const std::vector<index_t> lengths,
const std::vector<index_t> reduceDims,
AccDataType epsilon)
ComputeDataType epsilon)
{
return Argument{
x_m_n, gamma_n, beta_n, y_m_n, acc_elementwise_op, lengths, reduceDims, epsilon};
return Argument{x_m_n,
gamma_n,
beta_n,
y_m_n,
save_mean_m,
save_inv_std_m,
y_elementwise_op,
lengths,
reduceDims,
epsilon};
}
static auto MakeInvoker() { return Invoker{}; }
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ f30e5975
......@@ -20,12 +20,8 @@ using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using I32 = int32_t;
#if defined CK_ENABLE_FP8
using F8 = ck::f8_t;
#endif
#if defined CK_ENABLE_BF8
using BF8 = ck::bf8_t;
#endif
using F8 = ck::f8_t;
using BF8 = ck::bf8_t;
using Empty_Tuple = ck::Tuple<>;
......
library/include/ck/library/tensor_operation_instance/gpu/convolution_backward_data.hpp
View file @ f30e5975
......@@ -240,11 +240,13 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
if constexpr(NumDimSpatial == 1 && is_same_v<InLayout, NWC> && is_same_v<WeiLayout, KXC> &&
is_same_v<OutLayout, NWK>)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
......@@ -267,17 +269,23 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
}
#endif
}
else if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWC> &&
is_same_v<WeiLayout, KYXC> && is_same_v<OutLayout, NHWK>)
if constexpr(NumDimSpatial == 2 && is_same_v<InLayout, NHWC> &&
is_same_v<WeiLayout, KYXC> && is_same_v<OutLayout, NHWK>)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
}
#endif
#if defined(DL_KERNELS) && defined(CK_ENABLE_FP32)
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_conv2d_bwd_data_dl_nhwc_kyxc_nhwk_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
......@@ -306,14 +314,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceConvBw
}
#endif
}
else if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, NDHWC> &&
is_same_v<WeiLayout, KZYXC> && is_same_v<OutLayout, NDHWK>)
if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, NDHWC> &&
is_same_v<WeiLayout, KZYXC> && is_same_v<OutLayout, NDHWK>)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t>)
......
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