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Commit 7e7640ce authored by carlushuang's avatar carlushuang
Browse files

Merge remote-tracking branch 'origin/develop' into cpu_avx2

parents cc8df39e f015c776
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Showing with 1280 additions and 71 deletions
example/01_gemm/gemm_xdl_bf16.cpp
View file @ 7e7640ce
...@@ -5,11 +5,9 @@ ...@@ -5,11 +5,9 @@
#include <stdlib.h> #include <stdlib.h>
#include <half.hpp> #include <half.hpp>
#include "config.hpp" #include "config.hpp"
#include "print.hpp"
#include "device.hpp" #include "device.hpp"
#include "host_tensor.hpp" #include "host_tensor.hpp"
#include "host_tensor_generator.hpp" #include "host_tensor_generator.hpp"
#include "host_gemm.hpp"
#include "device_tensor.hpp" #include "device_tensor.hpp"
#include "device_gemm_xdl.hpp" #include "device_gemm_xdl.hpp"
#include "device_gemm_xdl_c_shuffle.hpp" #include "device_gemm_xdl_c_shuffle.hpp"
......
example/01_gemm/gemm_xdl_fp16.cpp
View file @ 7e7640ce
...@@ -5,11 +5,9 @@ ...@@ -5,11 +5,9 @@
#include <stdlib.h> #include <stdlib.h>
#include <half.hpp> #include <half.hpp>
#include "config.hpp" #include "config.hpp"
#include "print.hpp"
#include "device.hpp" #include "device.hpp"
#include "host_tensor.hpp" #include "host_tensor.hpp"
#include "host_tensor_generator.hpp" #include "host_tensor_generator.hpp"
#include "host_gemm.hpp"
#include "device_tensor.hpp" #include "device_tensor.hpp"
#include "device_gemm_xdl.hpp" #include "device_gemm_xdl.hpp"
#include "device_gemm_xdl_c_shuffle.hpp" #include "device_gemm_xdl_c_shuffle.hpp"
......
example/01_gemm/gemm_xdl_int8.cpp
View file @ 7e7640ce
...@@ -5,11 +5,9 @@ ...@@ -5,11 +5,9 @@
#include <stdlib.h> #include <stdlib.h>
#include <half.hpp> #include <half.hpp>
#include "config.hpp" #include "config.hpp"
#include "print.hpp"
#include "device.hpp" #include "device.hpp"
#include "host_tensor.hpp" #include "host_tensor.hpp"
#include "host_tensor_generator.hpp" #include "host_tensor_generator.hpp"
#include "host_gemm.hpp"
#include "device_tensor.hpp" #include "device_tensor.hpp"
#include "device_gemm_xdl.hpp" #include "device_gemm_xdl.hpp"
#include "device_gemm_xdl_c_shuffle.hpp" #include "device_gemm_xdl_c_shuffle.hpp"
......
example/12_reduce/reduce_blockwise.cpp
View file @ 7e7640ce
...@@ -261,7 +261,7 @@ int main(int argc, char* argv[]) ...@@ -261,7 +261,7 @@ int main(int argc, char* argv[])
float alpha = args.scales[0]; float alpha = args.scales[0];
float beta = args.scales[1]; float beta = args.scales[1];
std::size_t num_thread = std::thread::hardware_concurrency(); std::size_t num_thread = 1;
if(args.do_verification) if(args.do_verification)
{ {
......
example/16_gemm_reduce/gemm_reduce_xdl_fp16.cpp
View file @ 7e7640ce
...@@ -5,13 +5,10 @@ ...@@ -5,13 +5,10 @@
#include <stdlib.h> #include <stdlib.h>
#include <half.hpp> #include <half.hpp>
#include "config.hpp" #include "config.hpp"
#include "print.hpp"
#include "device.hpp" #include "device.hpp"
#include "host_tensor.hpp" #include "host_tensor.hpp"
#include "host_tensor_generator.hpp" #include "host_tensor_generator.hpp"
#include "host_gemm.hpp"
#include "device_tensor.hpp" #include "device_tensor.hpp"
#include "device_gemm_xdl.hpp"
#include "device_gemm_reduce_xdl_cshuffle.hpp" #include "device_gemm_reduce_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp" #include "element_wise_operation.hpp"
#include "reference_gemm.hpp" #include "reference_gemm.hpp"
......
example/18_batched_gemm_reduce/CMakeLists.txt 0 → 100644
View file @ 7e7640ce
add_example_executable(example_batched_gemm_reduce_xdl_fp16 batched_gemm_reduce_xdl_fp16.cpp)
example/18_batched_gemm_reduce/batched_gemm_reduce_xdl_fp16.cpp 0 → 100644
View file @ 7e7640ce
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "device_batched_gemm_reduce_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp"
#include "reference_batched_gemm.hpp"
#include "gemm_specialization.hpp"
#include "element_wise_reduce_operation.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using ADataType = F16;
using BDataType = F16;
using CDataType = F16;
using DDataType = F32;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
using D0ReduceOp = ck::tensor_operation::element_wise::ReduceSum;
using D1ReduceOp = ck::tensor_operation::element_wise::ReduceSquareSum;
static constexpr auto GemmSpecialization =
ck::tensor_operation::device::GemmSpecialization_t::Default;
// clang-format off
using DeviceBatchedGemmReduceInstance = ck::tensor_operation::device::DeviceBatchedGemmReduce_Xdl_CShuffle
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| D0| D1| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
//######| | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< Row, Col, Row, F16, F16, F16, F32, F32, F32, F32, AElementOp, BElementOp, CElementOp, D0ReduceOp, D1ReduceOp, GemmSpecialization, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>;
// clang-format on
using ReferenceBatchedGemmInstance = ck::tensor_operation::host::
ReferenceBatchedGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
int main(int argc, char* argv[])
{
bool do_verification = 1;
int init_method = 1;
int nrepeat = 5;
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096;
ck::index_t BatchCount = 4;
if(argc == 1)
{
// do nothing
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
}
else if(argc == 11)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]);
BatchCount = std::stoi(argv[9]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC, BatchCount\n");
exit(0);
}
auto f_host_tensor_descriptor = [](std::size_t batch_count,
std::size_t row,
std::size_t col,
std::size_t stride,
auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({row * stride, stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({col * stride, 1, stride}));
}
};
Tensor<ADataType> a_g_m_k(f_host_tensor_descriptor(BatchCount, M, K, StrideA, ALayout{}));
Tensor<BDataType> b_g_k_n(f_host_tensor_descriptor(BatchCount, K, N, StrideB, BLayout{}));
Tensor<CDataType> c_g_m_n_host_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<DDataType> d0_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<DDataType> d1_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<CDataType> c_g_m_n_device_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<DDataType> d0_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<DDataType> d1_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
std::cout << "c_g_m_n: " << c_g_m_n_host_result.mDesc << std::endl;
std::cout << "d0_g_m: " << d0_g_m_host_result.mDesc << std::endl;
std::cout << "d1_g_m: " << d1_g_m_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
default:
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
}
DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpace());
DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpace());
DeviceMem c_device_buf(sizeof(CDataType) * c_g_m_n_device_result.mDesc.GetElementSpace());
DeviceMem d0_device_buf(sizeof(DDataType) * d0_g_m_device_result.mDesc.GetElementSpace());
DeviceMem d1_device_buf(sizeof(DDataType) * d1_g_m_device_result.mDesc.GetElementSpace());
a_device_buf.ToDevice(a_g_m_k.mData.data());
b_device_buf.ToDevice(b_g_k_n.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
auto d0_reduce_op = D0ReduceOp{};
auto d1_reduce_op = D1ReduceOp{};
// do GEMM
auto batched_gemm = DeviceBatchedGemmReduceInstance{};
auto invoker = batched_gemm.MakeInvoker();
auto argument =
batched_gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
static_cast<DDataType*>(d0_device_buf.GetDeviceBuffer()),
static_cast<DDataType*>(d1_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op,
d0_reduce_op,
d1_reduce_op,
BatchCount);
if(!batched_gemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
// warm up
invoker.Run(argument);
// timing
float total_time = 0;
for(int i = 0; i < nrepeat; ++i)
{
// init DO, D1 to 0
d0_device_buf.SetZero();
d1_device_buf.SetZero();
KernelTimer timer;
timer.Start();
invoker.Run(argument);
timer.End();
total_time += timer.GetElapsedTime();
}
float ave_time = total_time / nrepeat;
std::size_t flop = std::size_t(2) * BatchCount * M * N * K;
std::size_t num_btype = sizeof(ADataType) * BatchCount * M * K +
sizeof(BDataType) * BatchCount * K * N +
sizeof(CDataType) * BatchCount * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< batched_gemm.GetTypeString() << std::endl;
if(do_verification)
{
c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
d0_device_buf.FromDevice(d0_g_m_device_result.mData.data());
d1_device_buf.FromDevice(d1_g_m_device_result.mData.data());
auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
auto ref_invoker = ref_batched_gemm.MakeInvoker();
auto ref_argument = ref_batched_gemm.MakeArgument(
a_g_m_k, b_g_k_n, c_g_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
for(int batch = 0; batch < BatchCount; ++batch)
{
for(int m = 0; m < M; ++m)
{
float d0_acc = d0_reduce_op.GetReduceZeroValue();
float d1_acc = d1_reduce_op.GetReduceZeroValue();
for(int n = 0; n < N; ++n)
{
d0_reduce_op.Reduce(d0_acc, c_g_m_n_host_result(batch, m, n));
d1_reduce_op.Reduce(d1_acc, c_g_m_n_host_result(batch, m, n));
}
d0_g_m_host_result(batch, m) = d0_acc;
d1_g_m_host_result(batch, m) = d1_acc;
}
}
check_error(c_g_m_n_host_result, c_g_m_n_device_result);
check_error(d0_g_m_host_result, d0_g_m_device_result);
check_error(d1_g_m_host_result, d1_g_m_device_result);
}
return 0;
}
example/CMakeLists.txt
View file @ 7e7640ce
...@@ -42,3 +42,4 @@ add_subdirectory(14_gemm_xdl_requant_relu_requant) ...@@ -42,3 +42,4 @@ add_subdirectory(14_gemm_xdl_requant_relu_requant)
add_subdirectory(17_convnd_bwd_data_xdl) add_subdirectory(17_convnd_bwd_data_xdl)
add_subdirectory(15_grouped_gemm) add_subdirectory(15_grouped_gemm)
add_subdirectory(16_gemm_reduce) add_subdirectory(16_gemm_reduce)
add_subdirectory(18_batched_gemm_reduce)
include/ck/tensor_operation/gpu/device/device_batched_gemm_xdl.hpp
View file @ 7e7640ce
...@@ -36,7 +36,7 @@ __global__ void ...@@ -36,7 +36,7 @@ __global__ void
const FloatAB* __restrict__ p_a_grid, const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid, const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid, FloatC* __restrict__ p_c_grid,
const index_t num_batches, const index_t batch_count,
const AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1, const AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1,
const BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1, const BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1,
const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2, const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
...@@ -47,7 +47,7 @@ __global__ void ...@@ -47,7 +47,7 @@ __global__ void
const Block2CTileMap block_2_ctile_map) const Block2CTileMap block_2_ctile_map)
{ {
const index_t num_blocks_per_batch = const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / num_batches); __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch); const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane( const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
...@@ -203,49 +203,43 @@ struct DeviceBatchedGemmXdl ...@@ -203,49 +203,43 @@ struct DeviceBatchedGemmXdl
using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1)); using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1));
using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1)); using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
struct Block2CTileMapMaker static constexpr auto MakeBlock2CTileMap(index_t batch_count,
const CGridDesc_M_N& c_grid_desc_m_n,
index_t M01,
index_t N01)
{ {
Block2CTileMapMaker(index_t num_batches) : num_batches_(num_batches) {} const auto M = c_grid_desc_m_n.GetLength(I0);
const auto N = c_grid_desc_m_n.GetLength(I1);
__host__ __device__ constexpr auto constexpr auto M1 = Number<MPerBlock>{};
MakeBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n, index_t M01, index_t N01) constexpr auto N1 = Number<NPerBlock>{};
{
const auto M = c_grid_desc_m_n.GetLength(I0);
const auto N = c_grid_desc_m_n.GetLength(I1);
constexpr auto M1 = Number<MPerBlock>{};
constexpr auto N1 = Number<NPerBlock>{};
const auto M0 = M / M1; const auto M0 = M / M1;
const auto N0 = N / N1; const auto N0 = N / N1;
const auto M00 = M0 / M01; const auto M00 = M0 / M01;
const auto N00 = N0 / N01; const auto N00 = N0 / N01;
const auto g_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor = const auto g_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
make_single_stage_tensor_adaptor( make_single_stage_tensor_adaptor(
make_tuple(make_insert_transform(num_batches_), make_tuple(make_insert_transform(batch_count),
make_unmerge_transform(make_tuple(M00, M01)), make_unmerge_transform(make_tuple(M00, M01)),
make_unmerge_transform(make_tuple(N00, N01))), make_unmerge_transform(make_tuple(N00, N01))),
make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<1>{}), make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{})); make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
const auto globalblockid_to_m00_m01_n00_n01_block_cluster_adaptor = const auto globalblockid_to_m00_m01_n00_n01_block_cluster_adaptor =
make_single_stage_tensor_adaptor( make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(num_batches_, M00, N00, M01, N01))), make_tuple(make_merge_transform(make_tuple(batch_count, M00, N00, M01, N01))),
make_tuple(Sequence<0, 1, 2, 3, 4>{}), make_tuple(Sequence<0, 1, 2, 3, 4>{}),
make_tuple(Sequence<0>{})); make_tuple(Sequence<0>{}));
const auto globalblockid_to_m0_n0_block_cluster_adaptor = const auto globalblockid_to_m0_n0_block_cluster_adaptor =
chain_tensor_adaptors(g_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor, chain_tensor_adaptors(g_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
globalblockid_to_m00_m01_n00_n01_block_cluster_adaptor); globalblockid_to_m00_m01_n00_n01_block_cluster_adaptor);
return globalblockid_to_m0_n0_block_cluster_adaptor;
}
private: return globalblockid_to_m0_n0_block_cluster_adaptor;
index_t num_batches_; }
};
struct ComputeBasePtrOfStridedBatch struct ComputeBasePtrOfStridedBatch
{ {
...@@ -320,8 +314,7 @@ struct DeviceBatchedGemmXdl ...@@ -320,8 +314,7 @@ struct DeviceBatchedGemmXdl
using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 = using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 =
decltype(GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_M_N{})); decltype(GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_M_N{}));
using Block2CTileMap = using Block2CTileMap = decltype(MakeBlock2CTileMap(1, CGridDesc_M_N{}, 1, 1));
decltype(Block2CTileMapMaker{1}.MakeBlock2CTileMap(CGridDesc_M_N{}, 1, 1));
// Argument // Argument
struct Argument : public BaseArgument struct Argument : public BaseArgument
...@@ -367,8 +360,7 @@ struct DeviceBatchedGemmXdl ...@@ -367,8 +360,7 @@ struct DeviceBatchedGemmXdl
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ = c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ =
GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_); GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_);
block_2_ctile_map_ = block_2_ctile_map_ = MakeBlock2CTileMap(BatchCount, c_grid_desc_m_n_, M01, N01);
Block2CTileMapMaker{BatchCount}.MakeBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
} }
} }
......
include/ck/tensor_operation/gpu/device/device_gemm.hpp
View file @ 7e7640ce
#pragma once #pragma once
#include <iostream> #include <iostream>
#include <vector>
#include "device_base.hpp" #include "device_base.hpp"
namespace ck { namespace ck {
......
include/ck/tensor_operation/gpu/device/device_gemm_reduce.hpp
View file @ 7e7640ce
...@@ -28,7 +28,8 @@ struct DeviceGemmReduce : public BaseOperator ...@@ -28,7 +28,8 @@ struct DeviceGemmReduce : public BaseOperator
BElementwiseOperation b_element_op, BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op, CElementwiseOperation c_element_op,
D0ReduceOperation d0_reduce_op, D0ReduceOperation d0_reduce_op,
D1ReduceOperation d1_reduce_op) = 0; D1ReduceOperation d1_reduce_op,
ck::index_t BatchCount = 1) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0; virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
}; };
......
include/ck/tensor_operation/gpu/device/device_gemm_reduce_xdl_cshuffle.hpp
View file @ 7e7640ce
...@@ -694,7 +694,8 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwiseOpera ...@@ -694,7 +694,8 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwiseOpera
BElementwiseOperation b_element_op, BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op, CElementwiseOperation c_element_op,
D0ReduceOperation d0_reduce_op, D0ReduceOperation d0_reduce_op,
D1ReduceOperation d1_reduce_op) override D1ReduceOperation d1_reduce_op,
index_t /* KBatch */ = 1) override
{ {
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),
......
include/ck/tensor_operation/gpu/device/tensor_layout.hpp
View file @ 7e7640ce
#ifndef TENSOR_LAYOUT_HPP #pragma once
#define TENSOR_LAYOUT_HPP
namespace ck { namespace ck {
namespace tensor_layout { namespace tensor_layout {
...@@ -128,4 +127,3 @@ std::ostream& operator<<(std::ostream& os, const Layout&) ...@@ -128,4 +127,3 @@ std::ostream& operator<<(std::ostream& os, const Layout&)
} // namespace tensor_layout } // namespace tensor_layout
} // namespace ck } // namespace ck
#endif
library/include/ck/library/host_tensor/host_reduction.hpp
View file @ 7e7640ce
...@@ -277,7 +277,7 @@ struct ReductionHost ...@@ -277,7 +277,7 @@ struct ReductionHost
out_indices[dst_offset] = accuIndex; out_indices[dst_offset] = accuIndex;
}; };
std::size_t num_thread = std::thread::hardware_concurrency(); std::size_t num_thread = 1;
std::size_t work_per_thread = std::size_t work_per_thread =
(invariant_dim_indexes.size() + num_thread - 1) / num_thread; (invariant_dim_indexes.size() + num_thread - 1) / num_thread;
...@@ -374,7 +374,7 @@ struct ReductionHost ...@@ -374,7 +374,7 @@ struct ReductionHost
out_data[dst_offset] = type_convert<OutDataType>(accuVal); out_data[dst_offset] = type_convert<OutDataType>(accuVal);
}; };
std::size_t num_thread = std::thread::hardware_concurrency(); std::size_t num_thread = 1;
std::size_t work_per_thread = std::size_t work_per_thread =
(invariant_dim_indexes.size() + num_thread - 1) / num_thread; (invariant_dim_indexes.size() + num_thread - 1) / num_thread;
......
library/include/ck/library/host_tensor/host_tensor.hpp
View file @ 7e7640ce
...@@ -73,10 +73,10 @@ struct HostTensorDescriptor ...@@ -73,10 +73,10 @@ struct HostTensorDescriptor
HostTensorDescriptor() = delete; HostTensorDescriptor() = delete;
template <typename X> template <typename X>
HostTensorDescriptor(std::vector<X> lens); HostTensorDescriptor(const std::vector<X>& lens);
template <typename X, typename Y> template <typename X, typename Y>
HostTensorDescriptor(std::vector<X> lens, std::vector<Y> strides); HostTensorDescriptor(const std::vector<X>& lens, const std::vector<Y>& strides);
void CalculateStrides(); void CalculateStrides();
...@@ -163,7 +163,7 @@ struct ParallelTensorFunctor ...@@ -163,7 +163,7 @@ struct ParallelTensorFunctor
return indices; return indices;
} }
void operator()(std::size_t num_thread = std::thread::hardware_concurrency()) const void operator()(std::size_t num_thread = 1) const
{ {
std::size_t work_per_thread = (mN1d + num_thread - 1) / num_thread; std::size_t work_per_thread = (mN1d + num_thread - 1) / num_thread;
...@@ -213,7 +213,7 @@ struct Tensor ...@@ -213,7 +213,7 @@ struct Tensor
Tensor(const HostTensorDescriptor& desc) : mDesc(desc), mData(mDesc.GetElementSpace()) {} Tensor(const HostTensorDescriptor& desc) : mDesc(desc), mData(mDesc.GetElementSpace()) {}
template <typename G> template <typename G>
void GenerateTensorValue(G g, std::size_t num_thread = std::thread::hardware_concurrency()) void GenerateTensorValue(G g, std::size_t num_thread = 1)
{ {
switch(mDesc.GetNumOfDimension()) switch(mDesc.GetNumOfDimension())
{ {
...@@ -285,13 +285,14 @@ struct Tensor ...@@ -285,13 +285,14 @@ struct Tensor
}; };
template <typename X> template <typename X>
HostTensorDescriptor::HostTensorDescriptor(std::vector<X> lens) : mLens(lens) HostTensorDescriptor::HostTensorDescriptor(const std::vector<X>& lens) : mLens(lens)
{ {
this->CalculateStrides(); this->CalculateStrides();
} }
template <typename X, typename Y> template <typename X, typename Y>
HostTensorDescriptor::HostTensorDescriptor(std::vector<X> lens, std::vector<Y> strides) HostTensorDescriptor::HostTensorDescriptor(const std::vector<X>& lens,
const std::vector<Y>& strides)
: mLens(lens), mStrides(strides) : mLens(lens), mStrides(strides)
{ {
} }
......
library/include/ck/library/host_tensor/host_tensor_generator.hpp
View file @ 7e7640ce
#ifndef HOST_TENSOR_GENERATOR_HPP #pragma once
#define HOST_TENSOR_GENERATOR_HPP
#include <cmath> #include <cmath>
#include <numeric>
#include "config.hpp" #include "config.hpp"
template <typename T> template <typename T>
...@@ -147,5 +148,3 @@ struct GeneratorTensor_Sequential ...@@ -147,5 +148,3 @@ struct GeneratorTensor_Sequential
return dims[Dim]; return dims[Dim];
} }
}; };
#endif
library/src/obselete_driver_offline/conv_add_fwd_driver_offline_nchwc.cpp
View file @ 7e7640ce
...@@ -302,7 +302,7 @@ int main(int argc, char* argv[]) ...@@ -302,7 +302,7 @@ int main(int argc, char* argv[])
print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w)); print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w)); print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
std::size_t num_thread = std::thread::hardware_concurrency(); std::size_t num_thread = 1;
switch(init_method) switch(init_method)
{ {
......
library/src/obselete_driver_offline/conv_bwd_driver_offline.cpp
View file @ 7e7640ce
...@@ -317,7 +317,7 @@ int main(int argc, char* argv[]) ...@@ -317,7 +317,7 @@ int main(int argc, char* argv[])
print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w)); print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w)); print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
std::size_t num_thread = std::thread::hardware_concurrency(); std::size_t num_thread = 1;
switch(init_method) switch(init_method)
{ {
......
library/src/obselete_driver_offline/conv_fwd_driver_offline.cpp
View file @ 7e7640ce
...@@ -319,7 +319,7 @@ int main(int argc, char* argv[]) ...@@ -319,7 +319,7 @@ int main(int argc, char* argv[])
print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w)); print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w)); print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
std::size_t num_thread = std::thread::hardware_concurrency(); std::size_t num_thread = 1;
switch(init_method) switch(init_method)
{ {
......
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