Skip to content

GitLab

Commit 8053bca3 authored by aska-0096's avatar aska-0096
Browse files

separate array base and vector base attention tensor transformation

parent b3770638
Hide whitespace changes
Inline Side-by-side
Showing with 461 additions and 251 deletions
include/ck/tensor_operation/gpu/device/device_grouped_gemm_softmax_gemm_permute_xdl_cshuffle.hpp
View file @ 8053bca3
...@@ -44,7 +44,7 @@ __global__ void ...@@ -44,7 +44,7 @@ __global__ void
const CElementwiseOperation c_element_op) const CElementwiseOperation c_element_op)
{ {
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \ #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__)) defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()]; __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t block_id = get_block_1d_id(); const index_t block_id = get_block_1d_id();
...@@ -682,7 +682,8 @@ struct DeviceGroupedGemmSoftmaxGemmPermute_Xdl_CShuffle ...@@ -682,7 +682,8 @@ struct DeviceGroupedGemmSoftmaxGemmPermute_Xdl_CShuffle
static bool IsSupportedArgument(const Argument& arg) static bool IsSupportedArgument(const Argument& arg)
{ {
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a" || if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a" ||
ck::get_device_name() == "gfx940")) ck::get_device_name() == "gfx940" || ck::get_device_name() == "gfx941" ||
ck::get_device_name() == "gfx942"))
{ {
return false; return false;
} }
......
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_wmma_cshuffle.hpp
View file @ 8053bca3
...@@ -18,7 +18,7 @@ ...@@ -18,7 +18,7 @@
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp" #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_wmma_cshuffle.hpp" #include "ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_wmma_cshuffle.hpp"
#include "ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp" #include "ck/tensor_operation/operator_transform/transform_contraction_to_gemm_arraybase.hpp"
#include "ck/host_utility/device_prop.hpp" #include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp" #include "ck/host_utility/kernel_launch.hpp"
...@@ -572,7 +572,7 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle ...@@ -572,7 +572,7 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
static constexpr auto B0EnableLds = B0EnableLds_auto || B0EnableLds_manu || (NumPrefetch > 1); static constexpr auto B0EnableLds = B0EnableLds_auto || B0EnableLds_manu || (NumPrefetch > 1);
static constexpr auto B1EnableLds = B1EnableLds_auto || B1EnableLds_manu || (NumPrefetch > 1); static constexpr auto B1EnableLds = B1EnableLds_auto || B1EnableLds_manu || (NumPrefetch > 1);
using Transform = TransformBatchedContractionContractionToBatchedGemmGemm< using Transform = TransformBatchedContractionContractionToBatchedGemmGemm_Wmma<
Sequence<NumDimG, NumDimM, NumDimL, NumDimK, NumDimN>, Sequence<NumDimG, NumDimM, NumDimL, NumDimK, NumDimN>,
Sequence<MPerBlock, LPerBlock, KPerBlock, NPerBlock>, Sequence<MPerBlock, LPerBlock, KPerBlock, NPerBlock>,
GemmSpec, GemmSpec,
......
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_xdl_cshuffle.hpp
View file @ 8053bca3
...@@ -68,7 +68,7 @@ __global__ void ...@@ -68,7 +68,7 @@ __global__ void
const C0MatrixMask c0_matrix_mask) const C0MatrixMask c0_matrix_mask)
{ {
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \ #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__)) defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()]; __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t num_blocks_per_batch = const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count); __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
...@@ -252,27 +252,25 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle ...@@ -252,27 +252,25 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle
B1Spec, B1Spec,
CSpec>; CSpec>;
static auto MakeAGridDescriptor_AK0_M_AK1( static auto MakeAGridDescriptor_AK0_M_AK1(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec, const std::vector<index_t>& a_gs_ms_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
{ {
return Transform::MakeAGridDescriptor_AK0_M_AK1( return Transform::MakeAGridDescriptor_AK0_M_AK1(
Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec), Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec),
Number<AK1>{}); Number<AK1>{});
} }
static auto MakeBGridDescriptor_BK0_N_BK1( static auto MakeBGridDescriptor_BK0_N_BK1(const std::vector<index_t>& b_gs_ns_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b_gs_ns_ks_lengths_vec, const std::vector<index_t>& b_gs_ns_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b_gs_ns_ks_strides_vec)
{ {
return Transform::MakeB0GridDescriptor_BK0_N_BK1( return Transform::MakeB0GridDescriptor_BK0_N_BK1(
Transform::MakeB0GridDescriptor_N_K(b_gs_ns_ks_lengths_vec, b_gs_ns_ks_strides_vec), Transform::MakeB0GridDescriptor_N_K(b_gs_ns_ks_lengths_vec, b_gs_ns_ks_strides_vec),
Number<BK1>{}); Number<BK1>{});
} }
static auto MakeB1GridDescriptor_BK0_N_BK1( static auto
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_gemm1ns_gemm1ks_lengths_vec, MakeB1GridDescriptor_BK0_N_BK1(const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_gemm1ns_gemm1ks_strides_vec) const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides_vec)
{ {
return Transform::MakeB1GridDescriptor_BK0_N_BK1( return Transform::MakeB1GridDescriptor_BK0_N_BK1(
Transform::MakeB1GridDescriptor_N_K(b1_gs_gemm1ns_gemm1ks_lengths_vec, Transform::MakeB1GridDescriptor_N_K(b1_gs_gemm1ns_gemm1ks_lengths_vec,
...@@ -455,18 +453,14 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle ...@@ -455,18 +453,14 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle
CDataType* p_c_grid, CDataType* p_c_grid,
const std::array<void*, NumD0Tensor> p_acc0_biases, const std::array<void*, NumD0Tensor> p_acc0_biases,
const std::array<void*, NumD1Tensor> p_acc1_biases, const std::array<void*, NumD1Tensor> p_acc1_biases,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths, const std::vector<index_t>& a_gs_ms_ks_lengths,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides, const std::vector<index_t>& a_gs_ms_ks_strides,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b_gs_ns_ks_lengths, const std::vector<index_t>& b_gs_ns_ks_lengths,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b_gs_ns_ks_strides, const std::vector<index_t>& b_gs_ns_ks_strides,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
const std::array<index_t, NumDimG + NumDimM + NumDimN>& const std::vector<index_t>& c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides const std::vector<index_t>& c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::array<index_t, NumDimG + NumDimM + NumDimN>&
c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
const std::array<index_t, NumDimG + NumDimM + NumDimN>&
c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
const std::array<std::vector<ck::index_t>, NumD0Tensor>& acc0_biases_gs_ms_ns_lengths, const std::array<std::vector<ck::index_t>, NumD0Tensor>& acc0_biases_gs_ms_ns_lengths,
const std::array<std::vector<ck::index_t>, NumD0Tensor>& acc0_biases_gs_ms_ns_strides, const std::array<std::vector<ck::index_t>, NumD0Tensor>& acc0_biases_gs_ms_ns_strides,
const std::array<std::vector<ck::index_t>, NumD1Tensor>& const std::array<std::vector<ck::index_t>, NumD1Tensor>&
...@@ -730,7 +724,8 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle ...@@ -730,7 +724,8 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle
#endif #endif
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a" || if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a" ||
ck::get_device_name() == "gfx940")) ck::get_device_name() == "gfx940" || ck::get_device_name() == "gfx941" ||
ck::get_device_name() == "gfx942"))
{ {
return false; return false;
} }
...@@ -791,12 +786,10 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle ...@@ -791,12 +786,10 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle
if(arg.d0s_nl_ns_lengths_strides_[i][1] == 1 && if(arg.d0s_nl_ns_lengths_strides_[i][1] == 1 &&
arg.d0s_nl_ns_lengths_strides_[i][0] % D0sTransferSrcScalarPerVector != 0) arg.d0s_nl_ns_lengths_strides_[i][0] % D0sTransferSrcScalarPerVector != 0)
{ {
std::cout << "first" << std::endl;
return false; return false;
} }
if(arg.d0s_nl_ns_lengths_strides_[i][1] != 1 && D0sTransferSrcScalarPerVector != 1) if(arg.d0s_nl_ns_lengths_strides_[i][1] != 1 && D0sTransferSrcScalarPerVector != 1)
{ {
std::cout << "second" << std::endl;
return false; return false;
} }
} }
...@@ -841,56 +834,20 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle ...@@ -841,56 +834,20 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle
B1ElementwiseOperation b1_element_op, B1ElementwiseOperation b1_element_op,
C1DEElementwiseOperation c1de_element_op) C1DEElementwiseOperation c1de_element_op)
{ {
constexpr auto dimension = NumDimG + NumDimM + NumDimN;
std::array<index_t, dimension> a_gs_ms_ks_lengths_{};
std::array<index_t, dimension> a_gs_ms_ks_strides_{};
std::array<index_t, dimension> b_gs_ns_ks_lengths_{};
std::array<index_t, dimension> b_gs_ns_ks_strides_{};
std::array<index_t, dimension> b1_gs_gemm1ns_gemm1ks_lengths_{}; // b1_gs_os_ns_lengths
std::array<index_t, dimension> b1_gs_gemm1ns_gemm1ks_strides_{}; // b1_gs_os_ns_strides
std::array<index_t, dimension> c_gs_ms_gemm1ns_lengths_{}; // c_gs_ms_os_lengths
std::array<index_t, dimension> c_gs_ms_gemm1ns_strides_{}; // c_gs_ms_os_strides
std::copy(a_gs_ms_ks_lengths.begin(),
a_gs_ms_ks_lengths.begin() + dimension,
a_gs_ms_ks_lengths_.begin());
std::copy(a_gs_ms_ks_strides.begin(),
a_gs_ms_ks_strides.begin() + dimension,
a_gs_ms_ks_strides_.begin());
std::copy(b_gs_ns_ks_lengths.begin(),
b_gs_ns_ks_lengths.begin() + dimension,
b_gs_ns_ks_lengths_.begin());
std::copy(b_gs_ns_ks_strides.begin(),
b_gs_ns_ks_strides.begin() + dimension,
b_gs_ns_ks_strides_.begin());
std::copy(b1_gs_gemm1ns_gemm1ks_lengths.begin(),
b1_gs_gemm1ns_gemm1ks_lengths.begin() + dimension,
b1_gs_gemm1ns_gemm1ks_lengths_.begin()); // b1_gs_os_ns_lengths
std::copy(b1_gs_gemm1ns_gemm1ks_strides.begin(),
b1_gs_gemm1ns_gemm1ks_strides.begin() + dimension,
b1_gs_gemm1ns_gemm1ks_strides_.begin()); // b1_gs_os_ns_strides
std::copy(c_gs_ms_gemm1ns_lengths.begin(),
c_gs_ms_gemm1ns_lengths.begin() + dimension,
c_gs_ms_gemm1ns_lengths_.begin()); // c_gs_ms_os_lengths
std::copy(c_gs_ms_gemm1ns_strides.begin(),
c_gs_ms_gemm1ns_strides.begin() + dimension,
c_gs_ms_gemm1ns_strides_.begin()); // c_gs_ms_os_strides
return Argument{p_a, return Argument{p_a,
p_b, p_b,
p_b1, p_b1,
p_c, p_c,
p_acc0_biases, p_acc0_biases,
p_acc1_biases, p_acc1_biases,
a_gs_ms_ks_lengths_, a_gs_ms_ks_lengths,
a_gs_ms_ks_strides_, a_gs_ms_ks_strides,
b_gs_ns_ks_lengths_, b_gs_ns_ks_lengths,
b_gs_ns_ks_strides_, b_gs_ns_ks_strides,
b1_gs_gemm1ns_gemm1ks_lengths_, // b1_gs_os_ns_lengths b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
b1_gs_gemm1ns_gemm1ks_strides_, // b1_gs_os_ns_strides b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
c_gs_ms_gemm1ns_lengths_, // c_gs_ms_os_lengths c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides_, // c_gs_ms_os_strides c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
acc0_biases_gs_ms_ns_lengths, acc0_biases_gs_ms_ns_lengths,
acc0_biases_gs_ms_ns_strides, acc0_biases_gs_ms_ns_strides,
acc1_biases_gs_ms_gemm1ns_lengths, // acc1_biases_gs_ms_os_lengths acc1_biases_gs_ms_gemm1ns_lengths, // acc1_biases_gs_ms_os_lengths
...@@ -933,56 +890,20 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle ...@@ -933,56 +890,20 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle
B1ElementwiseOperation b1_element_op, B1ElementwiseOperation b1_element_op,
C1DEElementwiseOperation c1de_element_op) override C1DEElementwiseOperation c1de_element_op) override
{ {
constexpr auto dimension = NumDimG + NumDimM + NumDimN;
std::array<index_t, dimension> a_gs_ms_ks_lengths_{};
std::array<index_t, dimension> a_gs_ms_ks_strides_{};
std::array<index_t, dimension> b_gs_ns_ks_lengths_{};
std::array<index_t, dimension> b_gs_ns_ks_strides_{};
std::array<index_t, dimension> b1_gs_gemm1ns_gemm1ks_lengths_{}; // b1_gs_os_ns_lengths
std::array<index_t, dimension> b1_gs_gemm1ns_gemm1ks_strides_{}; // b1_gs_os_ns_strides
std::array<index_t, dimension> c_gs_ms_gemm1ns_lengths_{}; // c_gs_ms_os_lengths
std::array<index_t, dimension> c_gs_ms_gemm1ns_strides_{}; // c_gs_ms_os_strides
std::copy(a_gs_ms_ks_lengths.begin(),
a_gs_ms_ks_lengths.begin() + dimension,
a_gs_ms_ks_lengths_.begin());
std::copy(a_gs_ms_ks_strides.begin(),
a_gs_ms_ks_strides.begin() + dimension,
a_gs_ms_ks_strides_.begin());
std::copy(b_gs_ns_ks_lengths.begin(),
b_gs_ns_ks_lengths.begin() + dimension,
b_gs_ns_ks_lengths_.begin());
std::copy(b_gs_ns_ks_strides.begin(),
b_gs_ns_ks_strides.begin() + dimension,
b_gs_ns_ks_strides_.begin());
std::copy(b1_gs_gemm1ns_gemm1ks_lengths.begin(),
b1_gs_gemm1ns_gemm1ks_lengths.begin() + dimension,
b1_gs_gemm1ns_gemm1ks_lengths_.begin()); // b1_gs_os_ns_lengths
std::copy(b1_gs_gemm1ns_gemm1ks_strides.begin(),
b1_gs_gemm1ns_gemm1ks_strides.begin() + dimension,
b1_gs_gemm1ns_gemm1ks_strides_.begin()); // b1_gs_os_ns_strides
std::copy(c_gs_ms_gemm1ns_lengths.begin(),
c_gs_ms_gemm1ns_lengths.begin() + dimension,
c_gs_ms_gemm1ns_lengths_.begin()); // c_gs_ms_os_lengths
std::copy(c_gs_ms_gemm1ns_strides.begin(),
c_gs_ms_gemm1ns_strides.begin() + dimension,
c_gs_ms_gemm1ns_strides_.begin()); // c_gs_ms_os_strides
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a), return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
static_cast<const BDataType*>(p_b), static_cast<const BDataType*>(p_b),
static_cast<const B1DataType*>(p_b1), static_cast<const B1DataType*>(p_b1),
static_cast<CDataType*>(p_c), static_cast<CDataType*>(p_c),
p_acc0_biases, // cast in struct Argument p_acc0_biases, // cast in struct Argument
p_acc1_biases, // cast in struct Argument p_acc1_biases, // cast in struct Argument
a_gs_ms_ks_lengths_, a_gs_ms_ks_lengths,
a_gs_ms_ks_strides_, a_gs_ms_ks_strides,
b_gs_ns_ks_lengths_, b_gs_ns_ks_lengths,
b_gs_ns_ks_strides_, b_gs_ns_ks_strides,
b1_gs_gemm1ns_gemm1ks_lengths_, // b1_gs_os_ns_lengths b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
b1_gs_gemm1ns_gemm1ks_strides_, // b1_gs_os_ns_strides b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
c_gs_ms_gemm1ns_lengths_, // c_gs_ms_os_lengths c_gs_ms_gemm1ns_lengths, // c_gs_ms_os_lengths
c_gs_ms_gemm1ns_strides_, // c_gs_ms_os_strides c_gs_ms_gemm1ns_strides, // c_gs_ms_os_strides
acc0_biases_gs_ms_ns_lengths, acc0_biases_gs_ms_ns_lengths,
acc0_biases_gs_ms_ns_strides, acc0_biases_gs_ms_ns_strides,
acc1_biases_gs_ms_gemm1ns_lengths, acc1_biases_gs_ms_gemm1ns_lengths,
......
include/ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp
View file @ 8053bca3
...@@ -16,15 +16,14 @@ template <index_t NumDimG, ...@@ -16,15 +16,14 @@ template <index_t NumDimG,
index_t NumDimM, index_t NumDimM,
index_t NumDimN, index_t NumDimN,
device::TensorSpecialization TensorSpec> device::TensorSpecialization TensorSpec>
__host__ __device__ static auto static auto MakeGridDescriptorPair(const std::vector<index_t>& gs_ms_ns_lengths_vec,
MakeGridDescriptorPair(const std::array<index_t, NumDimG + NumDimM + NumDimN>& gs_ms_ns_lengths_vec, const std::vector<index_t>& gs_ms_ns_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& gs_ms_ns_strides_vec)
{ {
// if(!(gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN && if(!(gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
// gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN)) gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN))
// { {
// throw std::runtime_error("wrong! dimension must match input lengths"); throw std::runtime_error("wrong! dimension must match input lengths");
// } }
const auto to_tuple = [&](auto& vec, auto start, auto end) { const auto to_tuple = [&](auto& vec, auto start, auto end) {
return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{}); return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
...@@ -144,24 +143,21 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm ...@@ -144,24 +143,21 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
// //
// A // A
// //
__host__ __device__ static auto MakeAGridDescriptorPair( static auto MakeAGridDescriptorPair(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec, const std::vector<index_t>& a_gs_ms_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
{ {
return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimK, ASpec>(a_gs_ms_ks_lengths_vec, return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimK, ASpec>(a_gs_ms_ks_lengths_vec,
a_gs_ms_ks_strides_vec); a_gs_ms_ks_strides_vec);
} }
// TODO: rename to G_MRaw_KRaw // TODO: rename to G_MRaw_KRaw
__host__ __device__ static auto MakeAGridDescriptor_G_M_K( static auto MakeAGridDescriptor_G_M_K(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec, const std::vector<index_t>& a_gs_ms_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
{ {
return MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).first; return MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).first;
} }
__host__ __device__ static auto MakeAGridDescriptor_M_K( static auto MakeAGridDescriptor_M_K(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec, const std::vector<index_t>& a_gs_ms_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
{ {
return matrix_padder.PadADescriptor_M_K( return matrix_padder.PadADescriptor_M_K(
MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).second); MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).second);
...@@ -183,57 +179,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm ...@@ -183,57 +179,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
make_tuple(Sequence<0, 2>{}, Sequence<1>{})); make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
} }
template <typename AGridDesc_M_K,
typename WmmaK,
typename MRepeat,
typename MWaves,
typename MPerWmma,
typename AK1>
__host__ __device__ static constexpr auto
MakeAGridDescriptor_AKWmma_MBlockRepeat_MWaves_AK0PerWmma_AKRow_MPerWmma_AK1(
const AGridDesc_M_K& a_grid_desc_m_k,
const WmmaK&,
const MRepeat&,
const MWaves&,
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 = 2;
constexpr auto AK0PerWmma = WmmaK{} / AKRow / AK1{};
return transform_tensor_descriptor(
a_grid_desc_m_k,
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, 4, 6>{}, Sequence<1, 2, 5>{}));
}
// //
// B (alias of B0) // B (alias of B0)
// //
__host__ __device__ static auto MakeB0GridDescriptorPair( static auto MakeB0GridDescriptorPair(const std::vector<index_t>& b0_gs_ns_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec, const std::vector<index_t>& b0_gs_ns_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
{ {
return MakeGridDescriptorPair<NumDimG, NumDimN, NumDimK, B0Spec>(b0_gs_ns_ks_lengths_vec, return MakeGridDescriptorPair<NumDimG, NumDimN, NumDimK, B0Spec>(b0_gs_ns_ks_lengths_vec,
b0_gs_ns_ks_strides_vec); b0_gs_ns_ks_strides_vec);
} }
// TODO: rename to G_MRaw_NRaw // TODO: rename to G_MRaw_NRaw
__host__ __device__ static auto MakeB0GridDescriptor_G_N_K( static auto MakeB0GridDescriptor_G_N_K(const std::vector<index_t>& b0_gs_ns_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec, const std::vector<index_t>& b0_gs_ns_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
{ {
return MakeB0GridDescriptorPair(b0_gs_ns_ks_lengths_vec, b0_gs_ns_ks_strides_vec).first; return MakeB0GridDescriptorPair(b0_gs_ns_ks_lengths_vec, b0_gs_ns_ks_strides_vec).first;
} }
__host__ __device__ static auto MakeB0GridDescriptor_N_K( static auto MakeB0GridDescriptor_N_K(const std::vector<index_t>& b0_gs_ns_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec, const std::vector<index_t>& b0_gs_ns_ks_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
{ {
// alias of matrix_padder.PadB0Descriptor_N_K // alias of matrix_padder.PadB0Descriptor_N_K
return matrix_padder.PadBDescriptor_N_K( return matrix_padder.PadBDescriptor_N_K(
...@@ -256,57 +219,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm ...@@ -256,57 +219,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
make_tuple(Sequence<0, 2>{}, Sequence<1>{})); make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
} }
template <typename BGridDesc_L_K,
typename WmmaK,
typename LRepeat,
typename LWaves,
typename LPerWmma,
typename BK1>
__host__ __device__ static constexpr auto
MakeB0GridDescriptor_BKWmma_LBlockRepeat_LWaves_BK0PerWmma_BKRow_LPerWmma_BK1(
const BGridDesc_L_K& b_grid_desc_l_k,
const WmmaK&,
const LRepeat&,
const LWaves&,
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 = 2;
constexpr auto BK0PerWmma = WmmaK{} / BKRow / BK1{};
return transform_tensor_descriptor(
b_grid_desc_l_k,
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, 4, 6>{}, Sequence<1, 2, 5>{}));
}
// //
// B1 // B1
// //
__host__ __device__ static auto MakeB1GridDescriptorPair( static auto MakeB1GridDescriptorPair(const std::vector<index_t>& b1_gs_os_ns_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec, const std::vector<index_t>& b1_gs_os_ns_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
{ {
return MakeGridDescriptorPair<NumDimG, NumDimO, NumDimN, B1Spec>(b1_gs_os_ns_lengths_vec, return MakeGridDescriptorPair<NumDimG, NumDimO, NumDimN, B1Spec>(b1_gs_os_ns_lengths_vec,
b1_gs_os_ns_strides_vec); b1_gs_os_ns_strides_vec);
} }
// TODO: rename to G_NRaw_KRaw // TODO: rename to G_NRaw_KRaw
__host__ __device__ static auto MakeB1GridDescriptor_G_N_K( static auto MakeB1GridDescriptor_G_N_K(const std::vector<index_t>& b1_gs_os_ns_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec, const std::vector<index_t>& b1_gs_os_ns_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
{ {
return MakeB1GridDescriptorPair(b1_gs_os_ns_lengths_vec, b1_gs_os_ns_strides_vec).first; return MakeB1GridDescriptorPair(b1_gs_os_ns_lengths_vec, b1_gs_os_ns_strides_vec).first;
} }
__host__ __device__ static auto MakeB1GridDescriptor_N_K( static auto MakeB1GridDescriptor_N_K(const std::vector<index_t>& b1_gs_os_ns_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec, const std::vector<index_t>& b1_gs_os_ns_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
{ {
// alias of matrix_padder.PadB1Descriptor_O_N // alias of matrix_padder.PadB1Descriptor_O_N
return matrix_padder.PadB1Descriptor_N_K( return matrix_padder.PadB1Descriptor_N_K(
...@@ -330,57 +260,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm ...@@ -330,57 +260,24 @@ struct TransformBatchedContractionContractionToBatchedGemmGemm
make_tuple(Sequence<0, 2>{}, Sequence<1>{})); make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
} }
template <typename BGridDesc_N_L,
typename WmmaL,
typename NRepeat,
typename NWaves,
typename NPerWmma,
typename BL1>
__host__ __device__ static constexpr auto
MakeB1GridDescriptor_BLWmma_NBlockRepeat_NWaves__BL0PerWmma_BLRow_NPerWmma_BL1(
const BGridDesc_N_L& b_grid_desc_n_l,
const WmmaL&,
const NRepeat&,
const NWaves&,
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 = 2;
constexpr auto BL0PerWmma = WmmaL{} / BLRow / BL1{};
return transform_tensor_descriptor(
b_grid_desc_n_l,
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, 4, 6>{}, Sequence<1, 2, 5>{}));
}
// //
// C // C
// //
__host__ __device__ static auto MakeCGridDescriptorPair( static auto MakeCGridDescriptorPair(const std::vector<index_t>& c_gs_ms_os_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec, const std::vector<index_t>& c_gs_ms_os_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
{ {
return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimO, CSpec>(c_gs_ms_os_lengths_vec, return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimO, CSpec>(c_gs_ms_os_lengths_vec,
c_gs_ms_os_strides_vec); c_gs_ms_os_strides_vec);
} }
// TODO: rename to G_MRaw_NRaw // TODO: rename to G_MRaw_NRaw
__host__ __device__ static auto MakeCGridDescriptor_G_M_N( static auto MakeCGridDescriptor_G_M_N(const std::vector<index_t>& c_gs_ms_os_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec, const std::vector<index_t>& c_gs_ms_os_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
{ {
return MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).first; return MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).first;
} }
__host__ __device__ static auto MakeCGridDescriptor_M_N( static auto MakeCGridDescriptor_M_N(const std::vector<index_t>& c_gs_ms_os_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec, const std::vector<index_t>& c_gs_ms_os_strides_vec)
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
{ {
return matrix_padder.PadCDescriptor_M_N( return matrix_padder.PadCDescriptor_M_N(
MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).second); MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).second);
......
include/ck/tensor_operation/operator_transform/transform_contraction_to_gemm_arraybase.hpp 0 → 100644
View file @ 8053bca3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/common_header.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
namespace ck {
namespace tensor_operation {
// assume C[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
template <index_t NumDimG,
index_t NumDimM,
index_t NumDimN,
device::TensorSpecialization TensorSpec>
__host__ __device__ static auto
MakeGridDescriptorPair(const std::array<index_t, NumDimG + NumDimM + NumDimN>& gs_ms_ns_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& gs_ms_ns_strides_vec)
{
// if(!(gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
// gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN))
// {
// throw std::runtime_error("wrong! dimension must match input lengths");
// }
const auto to_tuple = [&](auto& vec, auto start, auto end) {
return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
};
const auto gs_ms_ns_lengths =
to_tuple(gs_ms_ns_lengths_vec, Number<0>{}, Number<NumDimG + NumDimM + NumDimN>{});
const auto gs_ms_ns_strides =
to_tuple(gs_ms_ns_strides_vec, Number<0>{}, Number<NumDimG + NumDimM + NumDimN>{});
// dimension Ids for G0, G1, ...
constexpr auto gDimIds = typename arithmetic_sequence_gen<0, NumDimG, 1>::type{};
// dimension Ids for M0, M1, ...
constexpr auto mDimIds =
typename arithmetic_sequence_gen<NumDimG, NumDimG + NumDimM, 1>::type{};
// dimension Ids for N0, N1, ...
constexpr auto nDimIds =
typename arithmetic_sequence_gen<NumDimG + NumDimM, NumDimG + NumDimM + NumDimN, 1>::type{};
// lengths for G0, G1, ...
const auto gLengths = get_container_subset(gs_ms_ns_lengths, gDimIds);
// lengths for M0, M1, ...
const auto mLengths = get_container_subset(gs_ms_ns_lengths, mDimIds);
// lengths for N0, N1, ...
const auto nLengths = get_container_subset(gs_ms_ns_lengths, nDimIds);
if constexpr(TensorSpec == device::TensorSpecialization::Packed)
{
auto G = container_reduce(gLengths, math::multiplies{}, Number<1>{});
auto M = container_reduce(mLengths, math::multiplies{}, Number<1>{});
auto N = container_reduce(nLengths, math::multiplies{}, Number<1>{});
const auto grid_desc_g_mraw_nraw = make_naive_tensor_descriptor(
make_tuple(G, M, N),
make_tuple(gs_ms_ns_strides[Number<NumDimG - 1>{}],
gs_ms_ns_strides[Number<NumDimG + NumDimM - 1>{}],
gs_ms_ns_strides[Number<NumDimG + NumDimM + NumDimN - 1>{}]));
const auto grid_desc_mraw_nraw = make_naive_tensor_descriptor(
make_tuple(M, N),
make_tuple(gs_ms_ns_strides[Number<NumDimG + NumDimM - 1>{}],
gs_ms_ns_strides[Number<NumDimG + NumDimM + NumDimN - 1>{}]));
return std::make_pair(grid_desc_g_mraw_nraw, grid_desc_mraw_nraw);
}
else
{
// naive tensor C[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
const auto grid_desc_gs_ms_ns =
make_naive_tensor_descriptor(gs_ms_ns_lengths, gs_ms_ns_strides);
// transformed tensor C[G = G0 * G1 * ..., MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 *
// N2 * ...]
// Note: This does not require padding as it only provides G offset calculation. Technically
// descriptor for only G is needed. Here we opt for backward compatibility purpose to return
// G_M_N
const auto grid_desc_g_mraw_nraw =
transform_tensor_descriptor(grid_desc_gs_ms_ns,
make_tuple(make_merge_transform(gLengths),
make_merge_transform(mLengths),
make_merge_transform(nLengths)),
make_tuple(gDimIds, mDimIds, nDimIds),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto c_ms_ns_lengths = to_tuple(
gs_ms_ns_lengths_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimN>{});
const auto c_ms_ns_strides = to_tuple(
gs_ms_ns_strides_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimN>{});
// transformed tensor C[MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 *
// N2 * ...]
const auto grid_desc_ms_ns = make_naive_tensor_descriptor(c_ms_ns_lengths, c_ms_ns_strides);
const auto grid_desc_mraw_nraw = transform_tensor_descriptor(
grid_desc_ms_ns,
make_tuple(make_merge_transform(mLengths), make_merge_transform(nLengths)),
make_tuple(mDimIds - Number<NumDimG>{}, nDimIds - Number<NumDimG>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return std::make_pair(grid_desc_g_mraw_nraw, grid_desc_mraw_nraw);
}
}
template <typename NumDims_G_M_N_K_O, // Sequence<>
typename PerBlock_M_N_K_O, // Sequence<>
device::GemmSpecialization GemmSpec,
device::TensorSpecialization ASpec,
device::TensorSpecialization B0Spec,
device::TensorSpecialization B1Spec,
device::TensorSpecialization CSpec>
struct TransformBatchedContractionContractionToBatchedGemmGemm_Wmma
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
static constexpr auto I4 = Number<4>{};
static constexpr index_t NumDimG = NumDims_G_M_N_K_O::At(I0);
static constexpr index_t NumDimM = NumDims_G_M_N_K_O::At(I1);
static constexpr index_t NumDimN = NumDims_G_M_N_K_O::At(I2);
static constexpr index_t NumDimK = NumDims_G_M_N_K_O::At(I3);
static constexpr index_t NumDimO = NumDims_G_M_N_K_O::At(I4);
static constexpr index_t MPerBlock = PerBlock_M_N_K_O::At(I0);
static constexpr index_t NPerBlock = PerBlock_M_N_K_O::At(I1);
static constexpr index_t KPerBlock = PerBlock_M_N_K_O::At(I2);
static constexpr index_t OPerBlock = PerBlock_M_N_K_O::At(I3);
static constexpr auto matrix_padder =
device::GemmGemmPadder<GemmSpec, index_t, index_t, index_t, index_t>{
MPerBlock, NPerBlock, KPerBlock, OPerBlock};
//
// A
//
__host__ __device__ static auto MakeAGridDescriptorPair(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
{
return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimK, ASpec>(a_gs_ms_ks_lengths_vec,
a_gs_ms_ks_strides_vec);
}
// TODO: rename to G_MRaw_KRaw
__host__ __device__ static auto MakeAGridDescriptor_G_M_K(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
{
return MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).first;
}
__host__ __device__ static auto MakeAGridDescriptor_M_K(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& a_gs_ms_ks_strides_vec)
{
return matrix_padder.PadADescriptor_M_K(
MakeAGridDescriptorPair(a_gs_ms_ks_lengths_vec, a_gs_ms_ks_strides_vec).second);
}
template <typename AGridDesc_M_K, typename Number>
__host__ __device__ static constexpr auto
MakeAGridDescriptor_AK0_M_AK1(const AGridDesc_M_K& a_grid_desc_m_k, const Number& AK1)
{
const auto M = a_grid_desc_m_k.GetLength(I0);
const auto K = a_grid_desc_m_k.GetLength(I1);
const auto AK0 = K / AK1;
return transform_tensor_descriptor(a_grid_desc_m_k,
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
make_pass_through_transform(M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
template <typename AGridDesc_M_K,
typename WmmaK,
typename MRepeat,
typename MWaves,
typename MPerWmma,
typename AK1>
__host__ __device__ static constexpr auto
MakeAGridDescriptor_AKWmma_MBlockRepeat_MWaves_AK0PerWmma_AKRow_MPerWmma_AK1(
const AGridDesc_M_K& a_grid_desc_m_k,
const WmmaK&,
const MRepeat&,
const MWaves&,
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 = 2;
constexpr auto AK0PerWmma = WmmaK{} / AKRow / AK1{};
return transform_tensor_descriptor(
a_grid_desc_m_k,
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, 4, 6>{}, Sequence<1, 2, 5>{}));
}
//
// B (alias of B0)
//
__host__ __device__ static auto MakeB0GridDescriptorPair(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
{
return MakeGridDescriptorPair<NumDimG, NumDimN, NumDimK, B0Spec>(b0_gs_ns_ks_lengths_vec,
b0_gs_ns_ks_strides_vec);
}
// TODO: rename to G_MRaw_NRaw
__host__ __device__ static auto MakeB0GridDescriptor_G_N_K(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
{
return MakeB0GridDescriptorPair(b0_gs_ns_ks_lengths_vec, b0_gs_ns_ks_strides_vec).first;
}
__host__ __device__ static auto MakeB0GridDescriptor_N_K(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b0_gs_ns_ks_strides_vec)
{
// alias of matrix_padder.PadB0Descriptor_N_K
return matrix_padder.PadBDescriptor_N_K(
MakeB0GridDescriptorPair(b0_gs_ns_ks_lengths_vec, b0_gs_ns_ks_strides_vec).second);
}
template <typename BGridDesc_N_K, typename Number>
__host__ __device__ static constexpr auto
MakeB0GridDescriptor_BK0_N_BK1(const BGridDesc_N_K& b_grid_desc_n_k, const Number& BK1)
{
const auto N = b_grid_desc_n_k.GetLength(I0);
const auto K = b_grid_desc_n_k.GetLength(I1);
const auto BK0 = K / BK1;
return transform_tensor_descriptor(b_grid_desc_n_k,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_pass_through_transform(N)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
template <typename BGridDesc_L_K,
typename WmmaK,
typename LRepeat,
typename LWaves,
typename LPerWmma,
typename BK1>
__host__ __device__ static constexpr auto
MakeB0GridDescriptor_BKWmma_LBlockRepeat_LWaves_BK0PerWmma_BKRow_LPerWmma_BK1(
const BGridDesc_L_K& b_grid_desc_l_k,
const WmmaK&,
const LRepeat&,
const LWaves&,
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 = 2;
constexpr auto BK0PerWmma = WmmaK{} / BKRow / BK1{};
return transform_tensor_descriptor(
b_grid_desc_l_k,
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, 4, 6>{}, Sequence<1, 2, 5>{}));
}
//
// B1
//
__host__ __device__ static auto MakeB1GridDescriptorPair(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
{
return MakeGridDescriptorPair<NumDimG, NumDimO, NumDimN, B1Spec>(b1_gs_os_ns_lengths_vec,
b1_gs_os_ns_strides_vec);
}
// TODO: rename to G_NRaw_KRaw
__host__ __device__ static auto MakeB1GridDescriptor_G_N_K(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
{
return MakeB1GridDescriptorPair(b1_gs_os_ns_lengths_vec, b1_gs_os_ns_strides_vec).first;
}
__host__ __device__ static auto MakeB1GridDescriptor_N_K(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& b1_gs_os_ns_strides_vec)
{
// alias of matrix_padder.PadB1Descriptor_O_N
return matrix_padder.PadB1Descriptor_N_K(
MakeB1GridDescriptorPair(b1_gs_os_ns_lengths_vec, b1_gs_os_ns_strides_vec).second);
}
template <typename B1GridDesc_N_K, typename Number>
__host__ __device__ static constexpr auto
MakeB1GridDescriptor_BK0_N_BK1(const B1GridDesc_N_K& b1_grid_desc_n_k, const Number& B1K1)
{
const auto N = b1_grid_desc_n_k.GetLength(I0);
const auto K = b1_grid_desc_n_k.GetLength(I1);
const auto B1K0 = K / B1K1;
return transform_tensor_descriptor(
b1_grid_desc_n_k,
make_tuple(make_unmerge_transform(make_tuple(B1K0, B1K1)),
make_pass_through_transform(N)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
template <typename BGridDesc_N_L,
typename WmmaL,
typename NRepeat,
typename NWaves,
typename NPerWmma,
typename BL1>
__host__ __device__ static constexpr auto
MakeB1GridDescriptor_BLWmma_NBlockRepeat_NWaves__BL0PerWmma_BLRow_NPerWmma_BL1(
const BGridDesc_N_L& b_grid_desc_n_l,
const WmmaL&,
const NRepeat&,
const NWaves&,
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 = 2;
constexpr auto BL0PerWmma = WmmaL{} / BLRow / BL1{};
return transform_tensor_descriptor(
b_grid_desc_n_l,
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, 4, 6>{}, Sequence<1, 2, 5>{}));
}
//
// C
//
__host__ __device__ static auto MakeCGridDescriptorPair(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
{
return MakeGridDescriptorPair<NumDimG, NumDimM, NumDimO, CSpec>(c_gs_ms_os_lengths_vec,
c_gs_ms_os_strides_vec);
}
// TODO: rename to G_MRaw_NRaw
__host__ __device__ static auto MakeCGridDescriptor_G_M_N(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
{
return MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).first;
}
__host__ __device__ static auto MakeCGridDescriptor_M_N(
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_lengths_vec,
const std::array<index_t, NumDimG + NumDimM + NumDimN>& c_gs_ms_os_strides_vec)
{
return matrix_padder.PadCDescriptor_M_N(
MakeCGridDescriptorPair(c_gs_ms_os_lengths_vec, c_gs_ms_os_strides_vec).second);
}
};
} // namespace tensor_operation
} // namespace ck
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment