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Commit a9031464 authored by Chao Liu's avatar Chao Liu
Browse files

implicit gemm v1r3 nchw_cyxk_nkhw

parent 569ad66e
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driver/device_convolution_implicit_gemm_v1_nchw_cyxk_khwn.hpp
View file @ a9031464
......@@ -128,7 +128,7 @@ void device_convolution_implicit_gemm_v1_nchw_cyxk_khwn(InDesc,
using InBlockReorderSrcClusterLengths_NCHW = Sequence<4, 8, 2, 2>;
using InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW = Sequence<1, 2, 0, 3>;
constexpr index_t InBlockReorderDataPerRead_W = 1; // v1r3 cannot do vector load input for NCHW
constexpr index_t InBlockReorderDataPerWrite_N = 1; // not used yet
constexpr index_t InBlockReorderDataPerWrite_N = 1;
using WeiBlockCopyClusterLengths = Sequence<0, 0>; // not used
constexpr index_t WeiBlockCopyDataPerRead_K = 4;
......@@ -163,7 +163,7 @@ void device_convolution_implicit_gemm_v1_nchw_cyxk_khwn(InDesc,
using InBlockReorderSrcClusterLengths_NCHW = Sequence<1, 8, 1, 16>;
using InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW = Sequence<1, 2, 0, 3>;
constexpr index_t InBlockReorderDataPerRead_W = 1; // v1r3 cannot do vector load input for NCHW
constexpr index_t InBlockReorderDataPerWrite_N = 1; // not used yet
constexpr index_t InBlockReorderDataPerWrite_N = 2;
using WeiBlockCopyClusterLengths = Sequence<0, 0>; // not used
constexpr index_t WeiBlockCopyDataPerRead_K = 4;
......
driver/device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw.hpp 0 → 100644
View file @ a9031464
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "gridwise_convolution_wrapper.hip.hpp"
#include "gridwise_convolution_implicit_gemm_v1r3_nchw_cyxk_nkhw.hip.hpp"
template <class T, class InDesc, class WeiDesc, class OutDesc>
void device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw(InDesc,
const Tensor<T>& in_nchw,
WeiDesc,
const Tensor<T>& wei_kcyx,
OutDesc,
Tensor<T>& out_nkhw,
index_t nrepeat)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto in_nchw_desc = InDesc{};
constexpr auto wei_kcyx_desc = WeiDesc{};
constexpr auto out_nkhw_desc = OutDesc{};
constexpr index_t Hi = in_nchw_desc.GetLength(I2);
constexpr index_t Wi = in_nchw_desc.GetLength(I3);
constexpr index_t N = out_nkhw_desc.GetLength(I0);
constexpr index_t Ho = out_nkhw_desc.GetLength(I2);
constexpr index_t Wo = out_nkhw_desc.GetLength(I3);
constexpr index_t K = wei_kcyx_desc.GetLength(I0);
constexpr index_t C = wei_kcyx_desc.GetLength(I1);
constexpr index_t Y = wei_kcyx_desc.GetLength(I2);
constexpr index_t X = wei_kcyx_desc.GetLength(I3);
// reorder weight
auto wei_cyxk_desc = make_ConstantTensorDescriptor(Sequence<C, Y, X, K>{});
ostream_ConstantTensorDescriptor(wei_cyxk_desc, std::cout << "wei_cyxk_desc: ");
Tensor<T> wei_cyxk(make_TensorDescriptor(wei_cyxk_desc));
auto f_reorder_kcyx2cyxk = [&](auto k, auto c, auto y, auto x) {
wei_cyxk(c, y, x, k) = wei_kcyx(k, c, y, x);
};
make_ParallelTensorFunctor(f_reorder_kcyx2cyxk, K, C, Y, X)(
std::thread::hardware_concurrency());
std::size_t data_sz = sizeof(T);
DeviceMem in_nchw_device_buf(data_sz * in_nchw.mDesc.GetElementSpace());
DeviceMem wei_cyxk_device_buf(data_sz * wei_cyxk.mDesc.GetElementSpace());
DeviceMem out_nkhw_device_buf(data_sz * out_nkhw.mDesc.GetElementSpace());
in_nchw_device_buf.ToDevice(in_nchw.mData.data());
wei_cyxk_device_buf.ToDevice(wei_cyxk.mData.data());
out_nkhw_device_buf.ToDevice(out_nkhw.mData.data());
#if 0
// for 3x3, 28x28, v1r2, Pascal
constexpr index_t BlockSize = 128;
constexpr index_t NPerBlock = 16;
constexpr index_t KPerBlock = 128;
constexpr index_t CPerBlock = 8;
constexpr index_t HoPerBlock = 2;
constexpr index_t WoPerBlock = 2;
constexpr index_t NPerThread = 4;
constexpr index_t KPerThread = 8;
constexpr index_t HoPerThread = 1;
constexpr index_t WoPerThread = 2;
constexpr index_t GemmMPerThreadSubC = 4;
constexpr index_t GemmNPerThreadSubC = 4;
constexpr index_t GemmMLevel0Cluster = 4;
constexpr index_t GemmNLevel0Cluster = 2;
constexpr index_t GemmMLevel1Cluster = 4;
constexpr index_t GemmNLevel1Cluster = 2;
constexpr index_t GemmKPerThreadLoop = 1;
constexpr index_t GemmDataPerReadA = 4;
constexpr index_t GemmDataPerReadB = 4;
using InBlockReorderSrcSubLengths_NCHW = Sequence<4, 1, 1, 2>;
using InBlockReorderSrcClusterLengths_NCHW = Sequence<4, 8, 2, 2>;
using InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW = Sequence<1, 2, 0, 3>;
constexpr index_t InBlockReorderDataPerRead_W = 2;
constexpr index_t InBlockReorderDataPerWrite_N = 4;
using WeiBlockCopyClusterLengths = Sequence<4, 1, 32>;
constexpr index_t WeiBlockCopyDataPerRead_K = 4;
constexpr index_t OutThreadCopyDataPerWrite_W = 2;
#elif 0
// for 3x3, 28x28, v1r3, Pascal, bad
constexpr index_t BlockSize = 128;
constexpr index_t NPerBlock = 16;
constexpr index_t KPerBlock = 128;
constexpr index_t CPerBlock = 8;
constexpr index_t HoPerBlock = 2;
constexpr index_t WoPerBlock = 2;
constexpr index_t NPerThread = 4;
constexpr index_t KPerThread = 8;
constexpr index_t HoPerThread = 1;
constexpr index_t WoPerThread = 2;
constexpr index_t GemmMPerThreadSubC = 4;
constexpr index_t GemmNPerThreadSubC = 4;
constexpr index_t GemmMLevel0Cluster = 4;
constexpr index_t GemmNLevel0Cluster = 2;
constexpr index_t GemmMLevel1Cluster = 4;
constexpr index_t GemmNLevel1Cluster = 2;
constexpr index_t GemmKPerThreadLoop = 1;
constexpr index_t GemmDataPerReadA = 4;
constexpr index_t GemmDataPerReadB = 4;
using InBlockReorderSrcSubLengths_NCHW = Sequence<4, 1, 1, 1>;
using InBlockReorderSrcClusterLengths_NCHW = Sequence<4, 8, 2, 2>;
using InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW = Sequence<1, 2, 0, 3>;
constexpr index_t InBlockReorderDataPerRead_W = 1; // v1r3 cannot do vector load input for NCHW
constexpr index_t InBlockReorderDataPerWrite_N = 1; // not used yet
using WeiBlockCopyClusterLengths = Sequence<0, 0>; // not used
constexpr index_t WeiBlockCopyDataPerRead_K = 4;
constexpr index_t OutThreadCopyDataPerWrite_W = 2;
#elif 1
// for 3x3, 34x34, v1r3, Pascal
constexpr index_t BlockSize = 128;
constexpr index_t NPerBlock = 2;
constexpr index_t KPerBlock = 128;
constexpr index_t CPerBlock = 8;
constexpr index_t HoPerBlock = 2;
constexpr index_t WoPerBlock = 16;
constexpr index_t NPerThread = 2;
constexpr index_t KPerThread = 8;
constexpr index_t HoPerThread = 1;
constexpr index_t WoPerThread = 4;
constexpr index_t GemmMPerThreadSubC = 4;
constexpr index_t GemmNPerThreadSubC = 4;
constexpr index_t GemmMLevel0Cluster = 4;
constexpr index_t GemmNLevel0Cluster = 2;
constexpr index_t GemmMLevel1Cluster = 4;
constexpr index_t GemmNLevel1Cluster = 2;
constexpr index_t GemmKPerThreadLoop = 1;
constexpr index_t GemmDataPerReadA = 4;
constexpr index_t GemmDataPerReadB = 4;
using InBlockReorderSrcSubLengths_NCHW = Sequence<2, 1, 2, 1>;
using InBlockReorderSrcClusterLengths_NCHW = Sequence<1, 8, 1, 16>;
using InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW = Sequence<1, 2, 0, 3>;
constexpr index_t InBlockReorderDataPerRead_W = 1; // v1r3 cannot do vector load input for NCHW
constexpr index_t InBlockReorderDataPerWrite_N = 1; // not used yet
using WeiBlockCopyClusterLengths = Sequence<0, 0>; // not used
constexpr index_t WeiBlockCopyDataPerRead_K = 4;
constexpr index_t OutThreadCopyDataPerWrite_W = 2;
#endif
constexpr index_t GridSize =
((N + NPerBlock - 1) / NPerBlock) * ((K + KPerBlock - 1) / KPerBlock) *
((Ho + HoPerBlock - 1) / HoPerBlock) * ((Wo + WoPerBlock - 1) / WoPerBlock);
printf("%s: BlockSize %u, GridSize %u \n", __func__, BlockSize, GridSize);
for(index_t i = 0; i < nrepeat; ++i)
{
constexpr auto gridwise_conv = GridwiseConvolutionImplicitGemm_v1r3_nchw_cyxk_nkhw<
GridSize,
BlockSize,
T,
decltype(in_nchw_desc),
decltype(wei_cyxk_desc),
decltype(out_nkhw_desc),
NPerBlock,
KPerBlock,
CPerBlock,
HoPerBlock,
WoPerBlock,
NPerThread,
KPerThread,
HoPerThread,
WoPerThread,
GemmMPerThreadSubC,
GemmNPerThreadSubC,
GemmMLevel0Cluster,
GemmNLevel0Cluster,
GemmMLevel1Cluster,
GemmNLevel1Cluster,
GemmKPerThreadLoop,
GemmDataPerReadA,
GemmDataPerReadB,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N,
WeiBlockCopyClusterLengths,
WeiBlockCopyDataPerRead_K,
OutThreadCopyDataPerWrite_W>{};
float time = launch_kernel(run_gridwise_convolution<decltype(gridwise_conv), T>,
dim3(GridSize),
dim3(BlockSize),
0,
static_cast<T*>(in_nchw_device_buf.GetDeviceBuffer()),
static_cast<T*>(wei_cyxk_device_buf.GetDeviceBuffer()),
static_cast<T*>(out_nkhw_device_buf.GetDeviceBuffer()));
printf("Elapsed time : %f ms, %f TFlop/s\n",
time,
(float)calculate_convolution_flops(InDesc{}, WeiDesc{}, OutDesc{}) /
(std::size_t(1000) * 1000 * 1000) / time);
usleep(std::min(time * 1000, float(10000)));
}
out_nkhw_device_buf.FromDevice(out_nkhw.mData.data());
}
driver/driver.hip.cpp
View file @ a9031464
......@@ -12,7 +12,7 @@
//#include "device_direct_convolution_2_vectorized_nchw_kcyx_nkhw.hpp"
#include "device_convolution_implicit_gemm_v1_chwn_cyxk_khwn.hpp"
#include "device_convolution_implicit_gemm_v1_nchw_cyxk_khwn.hpp"
//#include "device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded.hpp"
#include "device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw.hpp"
#include "device_convolution_implicit_gemm_v2_chwn_cyxk_khwn.hpp"
struct GeneratorTensor_1
......@@ -605,8 +605,10 @@ int main(int argc, char* argv[])
device_direct_convolution_2_vectorized_nchw_kcyx_nkhw
#elif 0
device_convolution_implicit_gemm_v1_chwn_cyxk_khwn
#elif 1
#elif 0
device_convolution_implicit_gemm_v1_nchw_cyxk_khwn
#elif 1
device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw
#elif 0
device_convolution_implicit_gemm_v2_chwn_cyxk_khwn
#endif
......
src/include/Array.hip.hpp
View file @ a9031464
......@@ -24,7 +24,7 @@ struct Array
{
Array<TData, NSize + 1> new_array;
static_for<0, NSize, 1>{}([=](auto I) {
static_for<0, NSize, 1>{}([&](auto I) {
constexpr index_t i = I.Get();
new_array[i] = mData[i];
});
......
src/include/ConstantTensorDescriptor.hip.hpp
View file @ a9031464
......@@ -137,11 +137,16 @@ struct ConstantTensorDescriptor
}
template <index_t... Is>
__host__ __device__ static constexpr index_t Get1dIndex(Sequence<Is...> multi_id)
__host__ __device__ static constexpr index_t Get1dIndex(Sequence<Is...> /*multi_id*/)
{
static_assert(sizeof...(Is) == nDim, "wrong! Dimension not consistent");
return Get1dIndex(Is...);
constexpr auto multi_id = Sequence<Is...>{};
constexpr auto seq_tmp =
transform_sequences(mod_conv::multiplies<index_t>{}, multi_id, GetStrides());
return accumulate_on_sequence(seq_tmp, mod_conv::plus<index_t>{}, Number<0>{});
}
__host__ __device__ static Array<index_t, nDim> GetMultiIndex(index_t id)
......
src/include/Sequence.hip.hpp
View file @ a9031464
......@@ -246,7 +246,8 @@ struct accumulate_on_sequence_f
};
template <class Seq, class Reduce, index_t I>
__host__ __device__ constexpr index_t accumulate_on_sequence(Seq, Reduce, Number<I>)
__host__ __device__ constexpr index_t
accumulate_on_sequence(Seq, Reduce, Number<I> /*initial_value*/)
{
constexpr index_t a =
static_const_reduce_n<Seq::mSize>{}(accumulate_on_sequence_f<Seq>{}, Reduce{});
......
src/include/blockwise_2d_tensor_op.hip.hpp
View file @ a9031464
......@@ -471,7 +471,6 @@ struct Blockwise2dTensorCopy3
DstDesc{}.GetStride(I0) % DataPerRead == 0,
"src and dst stride should be multiple of DataPerRead to keep alignment");
constexpr index_t L0 = CopyLengths{}.Get(I0);
constexpr index_t L1 = CopyLengths{}.Get(I1);
constexpr index_t thread_per_d1 = (L1 + DataPerRead - 1) / DataPerRead;
......
src/include/blockwise_4d_tensor_op.hip.hpp
View file @ a9031464
......@@ -761,339 +761,3 @@ struct Blockwise4dTensorCopyReorder1
SrcDesc{}, p_src, DstDesc{}, p_dst, SrcOpLengths{}, MapDst2Src{}, f_copy);
}
};
template <index_t BlockSize,
class Float,
class SrcDesc,
class DstDesc,
class SrcLengths,
class SrcSubLengths,
class SrcClusterLengths,
class MapDst2Src,
class MapThreadCluster2SrcCluster,
index_t SrcDataPerRead,
index_t DstDataPerWrite>
struct Blockwise4dTensorCopyReorder3
{
static constexpr index_t nDim = SrcLengths::GetSize();
index_t mSrcMyThreadOffset;
index_t mDstMyThreadOffset;
__device__ Blockwise4dTensorCopyReorder3()
{
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto src_lengths = SrcLengths{};
constexpr auto map_dst2src = MapDst2Src{};
constexpr auto src_sub_lengths = SrcSubLengths{};
constexpr auto dst_sub_lengths = src_sub_lengths.ReorderGivenNew2Old(map_dst2src);
constexpr auto map_thread_cluster_2_src_cluster = MapThreadCluster2SrcCluster{};
constexpr auto src_cluster_lengths = SrcClusterLengths{};
constexpr auto thread_cluster_lengths =
src_cluster_lengths.ReorderGivenNew2Old(map_thread_cluster_2_src_cluster);
constexpr auto thread_cluster_desc = make_ConstantTensorDescriptor(thread_cluster_lengths);
// sanity check: data type
static_assert(is_same<Float, float>::value, "wrong! only support float for now!\n");
// sanity check: nDim
static_assert(SrcDesc::GetDimension() == nDim && DstDesc::GetDimension() == nDim &&
SrcLengths::GetSize() == nDim && SrcSubLengths::GetSize() == nDim &&
SrcClusterLengths::GetSize() == nDim && MapDst2Src::GetSize() == nDim &&
MapThreadCluster2SrcCluster::GetSize() == nDim,
"wrong! nDim is not consistent\n");
// sanity check: BlockSize
constexpr index_t num_active_thread = thread_cluster_desc.GetElementSize();
static_assert(BlockSize >= num_active_thread,
"wrong! BlockSize is not big enough for ThreadPerDims!");
// sanity check: work division
static_for<0, nDim, 1>{}([](auto IDim) {
constexpr auto I = decltype(IDim){};
constexpr index_t src_len = src_lengths.Get(I);
constexpr index_t src_sub_len = src_sub_lengths.Get(I);
constexpr index_t src_cluster_len = src_cluster_lengths.Get(I);
static_assert(src_len % (src_sub_len * src_cluster_len) == 0,
"wrong! cannot evenly divide Src tensor lengths");
});
// sanity check: src read
static_assert(SrcDataPerRead == 1 || SrcDataPerRead == 2 || SrcDataPerRead == 4,
"wrong! only support SrcDataPerRead == 1, 2 or 4!\n");
static_assert(SrcDataPerRead == 1 || src_desc.GetStride(Number<nDim - 1>{}) == 1,
"wrong! only support src.stride(nDim-1) == 1 if SrcDataPerRead > 1!\n");
static_assert(src_sub_lengths.Get(Number<nDim - 1>{}) % SrcDataPerRead == 0,
"wrong! src_sub_lengths[nDim-1] % SrcDataPerRead != 0\n");
static_assert(src_desc.GetStride(Number<nDim - 2>{}) % SrcDataPerRead == 0,
"wrong! should satisfy src_desc.stride(nDim-2) % SrcDataPerRead == 0, to "
"keep alignment");
// sanity check: dst write
static_assert(DstDataPerWrite == 1 || DstDataPerWrite == 2 || DstDataPerWrite == 4,
"wrong! only support DstDataPerWrite == 1, 2 or 4!\n");
static_assert(DstDataPerWrite == 1 || dst_desc.GetStride(Number<nDim - 1>{}) == 1,
"wrong! only support dst.stride(nDim-1) == 1 if DstDataPerWrite > 1!\n");
static_assert(dst_sub_lengths.Get(Number<nDim - 1>{}) % DstDataPerWrite == 0,
"wrong! dst_sub_lengths[nDim-1] % DstDataPerWrite != 0\n");
static_assert(dst_desc.GetStride(Number<nDim - 2>{}) % DstDataPerWrite == 0,
"wrong! should satisfy dst_desc.stride(nDim-2) % DstDataPerWrite == 0, to "
"keep alignment");
// start dividing work
if(BlockSize > num_active_thread)
{
if(get_thread_local_1d_id() >= num_active_thread)
{
return;
}
}
const auto thread_multi_id = thread_cluster_desc.GetMultiIndex(get_thread_local_1d_id());
// compiler: thread_multi_id, src_data_multi_id, dst_data_multi_id, will use separate
// regsiters, or only one copy???
auto src_data_multi_id =
reorder_array_given_old2new(thread_multi_id, map_thread_cluster_2_src_cluster);
static_for<0, nDim, 1>{}([&](auto IDim) {
constexpr auto I = decltype(IDim){};
constexpr index_t i = I.Get();
// compiler: will it really compute index here, or be associated with Get1dIndex and
// optimized away???
src_data_multi_id[i] *= src_sub_lengths.Get(I);
});
// compiler: will it really compute index here, or be associated with Get1dIndex and
// optimized away???
const auto dst_data_multi_id = reorder_array_given_new2old(src_data_multi_id, map_dst2src);
mSrcMyThreadOffset = src_desc.Get1dIndex(src_data_multi_id);
mDstMyThreadOffset = dst_desc.Get1dIndex(dst_data_multi_id);
#if 0
if(get_block_1d_id() == 0)
{
printf("tid %5u, "
"thread_multi_id %5u %5u %5u %5u, "
"src_data_multi_id %5u %5u %5u %5u, "
"dst_data_multi_id %5u %5u %5u %5u, "
"mSrcMyThreadOffset %u, mDstMyThreadOffset %u\n",
get_thread_local_1d_id(),
thread_multi_id[0],
thread_multi_id[1],
thread_multi_id[2],
thread_multi_id[3],
src_data_multi_id[0],
src_data_multi_id[1],
src_data_multi_id[2],
src_data_multi_id[3],
dst_data_multi_id[0],
dst_data_multi_id[1],
dst_data_multi_id[2],
dst_data_multi_id[3],
mSrcMyThreadOffset,
mDstMyThreadOffset);
}
#endif
}
__device__ static constexpr index_t GetRegisterClipboardSize()
{
constexpr auto thread_sub_tensor_lengths = SrcSubLengths{};
constexpr auto src_data_per_cluster_per_dims = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, SrcClusterLengths{});
constexpr auto cluster_per_dims =
transform_sequences(mod_conv::integer_divide_ceiler<index_t>{},
SrcLengths{},
src_data_per_cluster_per_dims);
constexpr auto thread_tensor_lengths = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, cluster_per_dims);
constexpr auto thread_tensor_desc = make_ConstantTensorDescriptor(thread_tensor_lengths);
return thread_tensor_desc.GetElementSpace();
}
__device__ void RunLoadRegisterClipboard(const Float* __restrict__ p_src,
Float* __restrict__ p_clipboard) const
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto thread_sub_tensor_lengths = SrcSubLengths{};
constexpr auto src_data_per_cluster_per_dims = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, SrcClusterLengths{});
constexpr auto cluster_per_dims =
transform_sequences(mod_conv::integer_divide_ceiler<index_t>{},
SrcLengths{},
src_data_per_cluster_per_dims);
constexpr auto thread_tensor_lengths = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, cluster_per_dims);
constexpr auto thread_tensor_desc = make_ConstantTensorDescriptor(thread_tensor_lengths);
constexpr auto thread_sub_tensor_desc =
make_ConstantTensorDescriptor(SrcClusterLengths{}, thread_tensor_desc.GetStrides());
#if 1
for(index_t icluster_d0 = 0; icluster_d0 < cluster_per_dims.Get(I0); ++icluster_d0)
{
for(index_t icluster_d1 = 0; icluster_d1 < cluster_per_dims.Get(I1); ++icluster_d1)
{
for(index_t icluster_d2 = 0; icluster_d2 < cluster_per_dims.Get(I2); ++icluster_d2)
{
for(index_t icluster_d3 = 0; icluster_d3 < cluster_per_dims.Get(I3);
++icluster_d3)
{
const index_t src_offset = SrcDesc{}.Get1dIndex(
icluster_d0 * src_data_per_cluster_per_dims.Get(I0),
icluster_d1 * src_data_per_cluster_per_dims.Get(I1),
icluster_d2 * src_data_per_cluster_per_dims.Get(I2),
icluster_d3 * src_data_per_cluster_per_dims.Get(I3));
const index_t clipboard_offset = thread_tensor_desc.Get1dIndex(
icluster_d0 * thread_sub_tensor_lengths.Get(I0),
icluster_d1 * thread_sub_tensor_lengths.Get(I1),
icluster_d2 * thread_sub_tensor_lengths.Get(I2),
icluster_d3 * thread_sub_tensor_lengths.Get(I3));
threadwise_nd_tensor_copy(SrcDesc{},
p_src + src_offset + mSrcMyThreadOffset,
thread_tensor_desc,
p_clipboard + clipboard_offset,
thread_sub_tensor_lengths,
Number<SrcDataPerRead>{});
}
}
}
}
#else
static_ford<decltype(cluster_per_dims)>{}([=](auto cluster_ids) {
});
#endif
#if 0
if(get_block_1d_id() == 0)
{
printf("tid %5u, "
"data: %f %f %f %f %f %f %f %f\n",
get_thread_local_1d_id(),
p_clipboard[0],
p_clipboard[1],
p_clipboard[2],
p_clipboard[3],
p_clipboard[4],
p_clipboard[5],
p_clipboard[6],
p_clipboard[7]);
}
#endif
}
__device__ void RunStoreRegisterClipboard(const Float* __restrict__ p_clipboard,
Float* __restrict__ p_dst) const
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto thread_sub_tensor_lengths = SrcSubLengths{};
constexpr auto src_data_per_cluster_per_dims = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, SrcClusterLengths{});
constexpr auto cluster_per_dims =
transform_sequences(mod_conv::integer_divide_ceiler<index_t>{},
SrcLengths{},
src_data_per_cluster_per_dims);
constexpr auto thread_tensor_lengths = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, cluster_per_dims);
constexpr auto thread_tensor_desc = make_ConstantTensorDescriptor(thread_tensor_lengths);
constexpr auto thread_sub_tensor_desc =
make_ConstantTensorDescriptor(SrcClusterLengths{}, thread_tensor_desc.GetStrides());
for(index_t icluster_d0 = 0; icluster_d0 < cluster_per_dims.Get(I0); ++icluster_d0)
{
for(index_t icluster_d1 = 0; icluster_d1 < cluster_per_dims.Get(I1); ++icluster_d1)
{
for(index_t icluster_d2 = 0; icluster_d2 < cluster_per_dims.Get(I2); ++icluster_d2)
{
for(index_t icluster_d3 = 0; icluster_d3 < cluster_per_dims.Get(I3);
++icluster_d3)
{
const index_t clipboard_offset = thread_tensor_desc.Get1dIndex(
icluster_d0 * thread_sub_tensor_lengths.Get(I0),
icluster_d1 * thread_sub_tensor_lengths.Get(I1),
icluster_d2 * thread_sub_tensor_lengths.Get(I2),
icluster_d3 * thread_sub_tensor_lengths.Get(I3));
const auto dst_multi_id = reorder_array_given_new2old(
Array<index_t, nDim>{
icluster_d0 * src_data_per_cluster_per_dims.Get(I0),
icluster_d1 * src_data_per_cluster_per_dims.Get(I1),
icluster_d2 * src_data_per_cluster_per_dims.Get(I2),
icluster_d3 * src_data_per_cluster_per_dims.Get(I3)},
MapDst2Src{});
const index_t dst_offset = DstDesc{}.Get1dIndex(dst_multi_id);
#if 0
if(get_block_1d_id() == 0)
{
printf("tid %5u, "
"clipboard_offsetm %5u, dst_offset %5u\n",
get_thread_local_1d_id(),
clipboard_offset,
dst_offset);
}
#endif
threadwise_4d_tensor_copy_reorder_given_dst2src_v2(
thread_tensor_desc,
p_clipboard + clipboard_offset,
DstDesc{},
p_dst + dst_offset + mDstMyThreadOffset,
thread_sub_tensor_lengths,
MapDst2Src{});
}
}
}
}
}
__device__ void Run(const Float* __restrict__ p_src, Float* __restrict__ p_dst) const
{
Float p_clipboard[GetRegisterClipboardSize()];
RunLoadRegisterClipboard(p_src, p_clipboard);
RunStoreRegisterClipboard(p_clipboard, p_dst);
}
};
src/include/blockwise_batched_gemm.hip.hpp
View file @ a9031464
......@@ -53,7 +53,6 @@ struct BlockwiseBatchGemmBlockABlockBThreadCTransANormalBNormalC_V2
constexpr index_t M = a_block_mtx.NCol(); // A is transposed
constexpr index_t N = b_block_mtx.NCol();
constexpr index_t K = a_block_mtx.NRow();
constexpr index_t MPerThread = c_thread_mtx.NRow();
constexpr index_t NPerThread = c_thread_mtx.NCol();
......@@ -114,8 +113,6 @@ struct BlockwiseBatchGemmBlockABlockBThreadCTransANormalBNormalC_V2
__device__ MatrixIndex GetBeginOfThreadMatrixC(index_t thread_id) const
{
constexpr index_t BatchThreadWork = BatchSize / BatchPerThread;
constexpr index_t ThreadPerLevel1Cluster =
MLevel0Cluster * NLevel0Cluster * MLevel1Cluster * NLevel1Cluster;
......
src/include/blockwise_nd_tensor_op.hip.hpp 0 → 100644
View file @ a9031464
#pragma once
#include "threadwise_nd_tensor_op.hip.hpp"
template <index_t BlockSize,
class Float,
class SrcDesc,
class DstDesc,
class SrcLengths,
class SrcSubLengths,
class SrcClusterLengths,
class MapDst2Src,
class MapThreadCluster2SrcCluster,
index_t SrcDataPerRead,
index_t DstDataPerWrite>
struct BlockwiseNdTensorCopyReorder_v3
{
static constexpr index_t nDim = SrcLengths::GetSize();
index_t mSrcMyThreadOffset;
index_t mDstMyThreadOffset;
__device__ BlockwiseNdTensorCopyReorder_v3()
{
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto src_lengths = SrcLengths{};
constexpr auto map_dst2src = MapDst2Src{};
constexpr auto src_sub_lengths = SrcSubLengths{};
constexpr auto dst_sub_lengths = src_sub_lengths.ReorderGivenNew2Old(map_dst2src);
constexpr auto map_thread_cluster_2_src_cluster = MapThreadCluster2SrcCluster{};
constexpr auto src_cluster_lengths = SrcClusterLengths{};
constexpr auto thread_cluster_lengths =
src_cluster_lengths.ReorderGivenNew2Old(map_thread_cluster_2_src_cluster);
constexpr auto thread_cluster_desc = make_ConstantTensorDescriptor(thread_cluster_lengths);
// sanity check: data type
static_assert(is_same<Float, float>::value, "wrong! only support float for now!\n");
// sanity check: nDim
static_assert(SrcDesc::GetDimension() == nDim && DstDesc::GetDimension() == nDim &&
SrcLengths::GetSize() == nDim && SrcSubLengths::GetSize() == nDim &&
SrcClusterLengths::GetSize() == nDim && MapDst2Src::GetSize() == nDim &&
MapThreadCluster2SrcCluster::GetSize() == nDim,
"wrong! nDim is not consistent\n");
// sanity check: BlockSize
constexpr index_t num_active_thread = thread_cluster_desc.GetElementSize();
static_assert(BlockSize >= num_active_thread,
"wrong! BlockSize is not big enough for ThreadPerDims!");
// sanity check: work division
static_for<0, nDim, 1>{}([](auto IDim) {
constexpr auto I = decltype(IDim){};
constexpr index_t src_len = src_lengths.Get(I);
constexpr index_t src_sub_len = src_sub_lengths.Get(I);
constexpr index_t src_cluster_len = src_cluster_lengths.Get(I);
static_assert(src_len % (src_sub_len * src_cluster_len) == 0,
"wrong! cannot evenly divide Src tensor lengths");
});
// sanity check: src read
static_assert(SrcDataPerRead == 1 || SrcDataPerRead == 2 || SrcDataPerRead == 4,
"wrong! only support SrcDataPerRead == 1, 2 or 4!\n");
static_assert(SrcDataPerRead == 1 || src_desc.GetStride(Number<nDim - 1>{}) == 1,
"wrong! only support src.stride(nDim-1) == 1 if SrcDataPerRead > 1!\n");
static_assert(src_sub_lengths.Get(Number<nDim - 1>{}) % SrcDataPerRead == 0,
"wrong! src_sub_lengths[nDim-1] % SrcDataPerRead != 0\n");
static_assert(src_desc.GetStride(Number<nDim - 2>{}) % SrcDataPerRead == 0,
"wrong! should satisfy src_desc.stride(nDim-2) % SrcDataPerRead == 0, to "
"keep alignment");
// sanity check: dst write
static_assert(DstDataPerWrite == 1 || DstDataPerWrite == 2 || DstDataPerWrite == 4,
"wrong! only support DstDataPerWrite == 1, 2 or 4!\n");
static_assert(DstDataPerWrite == 1 || dst_desc.GetStride(Number<nDim - 1>{}) == 1,
"wrong! only support dst.stride(nDim-1) == 1 if DstDataPerWrite > 1!\n");
static_assert(dst_sub_lengths.Get(Number<nDim - 1>{}) % DstDataPerWrite == 0,
"wrong! dst_sub_lengths[nDim-1] % DstDataPerWrite != 0\n");
static_assert(dst_desc.GetStride(Number<nDim - 2>{}) % DstDataPerWrite == 0,
"wrong! should satisfy dst_desc.stride(nDim-2) % DstDataPerWrite == 0, to "
"keep alignment");
// start dividing work
if(BlockSize > num_active_thread)
{
if(get_thread_local_1d_id() >= num_active_thread)
{
return;
}
}
const auto thread_multi_id = thread_cluster_desc.GetMultiIndex(get_thread_local_1d_id());
// compiler: thread_multi_id, src_data_multi_id, dst_data_multi_id, will use separate
// regsiters, or only one copy???
auto src_data_multi_id =
reorder_array_given_old2new(thread_multi_id, map_thread_cluster_2_src_cluster);
static_for<0, nDim, 1>{}([&](auto IDim) {
constexpr auto I = decltype(IDim){};
constexpr index_t i = I.Get();
// compiler: will it really compute index here, or be associated with Get1dIndex and
// optimized away???
src_data_multi_id[i] *= src_sub_lengths.Get(I);
});
// compiler: will it really compute index here, or be associated with Get1dIndex and
// optimized away???
const auto dst_data_multi_id = reorder_array_given_new2old(src_data_multi_id, map_dst2src);
mSrcMyThreadOffset = src_desc.Get1dIndex(src_data_multi_id);
mDstMyThreadOffset = dst_desc.Get1dIndex(dst_data_multi_id);
}
__device__ static constexpr index_t GetRegisterClipboardSize()
{
constexpr auto thread_sub_tensor_lengths = SrcSubLengths{};
constexpr auto src_data_per_cluster_per_dims = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, SrcClusterLengths{});
constexpr auto repeat_lengths =
transform_sequences(mod_conv::integer_divide_ceiler<index_t>{},
SrcLengths{},
src_data_per_cluster_per_dims);
constexpr auto thread_tensor_lengths = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, repeat_lengths);
constexpr auto thread_tensor_desc = make_ConstantTensorDescriptor(thread_tensor_lengths);
return thread_tensor_desc.GetElementSpace();
}
__device__ void RunLoadRegisterClipboard(const Float* __restrict__ p_src,
Float* __restrict__ p_clipboard) const
{
constexpr auto thread_sub_tensor_lengths = SrcSubLengths{};
constexpr auto src_data_per_cluster_per_dims = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, SrcClusterLengths{});
constexpr auto repeat_lengths =
transform_sequences(mod_conv::integer_divide_ceiler<index_t>{},
SrcLengths{},
src_data_per_cluster_per_dims);
constexpr auto thread_tensor_lengths = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, repeat_lengths);
constexpr auto thread_tensor_desc = make_ConstantTensorDescriptor(thread_tensor_lengths);
static_ford<decltype(repeat_lengths)>{}([&](auto repeat_multi_id_) {
constexpr auto repeat_multi_id = decltype(repeat_multi_id_){};
constexpr auto src_data_multi_id = transform_sequences(
mod_conv::multiplies<index_t>{}, repeat_multi_id, src_data_per_cluster_per_dims);
constexpr auto clipboard_data_multi_id = transform_sequences(
mod_conv::multiplies<index_t>{}, repeat_multi_id, thread_sub_tensor_lengths);
constexpr index_t src_offset = SrcDesc{}.Get1dIndex(src_data_multi_id);
constexpr index_t clipboard_offset =
thread_tensor_desc.Get1dIndex(clipboard_data_multi_id);
threadwise_nd_tensor_copy(SrcDesc{},
p_src + src_offset + mSrcMyThreadOffset,
thread_tensor_desc,
p_clipboard + clipboard_offset,
thread_sub_tensor_lengths,
Number<SrcDataPerRead>{});
});
}
__device__ void RunStoreRegisterClipboard(const Float* __restrict__ p_clipboard,
Float* __restrict__ p_dst) const
{
constexpr auto thread_sub_tensor_lengths = SrcSubLengths{};
constexpr auto src_data_per_cluster_per_dims = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, SrcClusterLengths{});
constexpr auto repeat_lengths =
transform_sequences(mod_conv::integer_divide_ceiler<index_t>{},
SrcLengths{},
src_data_per_cluster_per_dims);
constexpr auto thread_tensor_lengths = transform_sequences(
mod_conv::multiplies<index_t>{}, thread_sub_tensor_lengths, repeat_lengths);
constexpr auto thread_tensor_desc = make_ConstantTensorDescriptor(thread_tensor_lengths);
static_ford<decltype(repeat_lengths)>{}([&](auto repeat_multi_id_) {
constexpr auto repeat_multi_id = decltype(repeat_multi_id_){};
constexpr auto clipboard_data_multi_id = transform_sequences(
mod_conv::multiplies<index_t>{}, repeat_multi_id, thread_sub_tensor_lengths);
constexpr auto src_data_multi_id = transform_sequences(
mod_conv::multiplies<index_t>{}, repeat_multi_id, src_data_per_cluster_per_dims);
// reorder src_data_multi_id to get dst_data_multi_id
constexpr auto dst_data_multi_id = src_data_multi_id.ReorderGivenNew2Old(MapDst2Src{});
constexpr index_t clipboard_offset =
thread_tensor_desc.Get1dIndex(clipboard_data_multi_id);
constexpr index_t dst_offset = DstDesc{}.Get1dIndex(dst_data_multi_id);
// write in the order of dst
#if 1
threadwise_nd_tensor_copy_reorder_given_dst2src_v2(thread_tensor_desc,
p_clipboard + clipboard_offset,
DstDesc{},
p_dst + dst_offset +
mDstMyThreadOffset,
thread_sub_tensor_lengths,
MapDst2Src{});
#else
threadwise_nd_tensor_copy_reorder_given_dst2src_v3(thread_tensor_desc,
p_clipboard + clipboard_offset,
DstDesc{},
p_dst + dst_offset +
mDstMyThreadOffset,
thread_sub_tensor_lengths,
MapDst2Src{},
Number<DstDataPerWrite>{});
#endif
});
}
__device__ void Run(const Float* __restrict__ p_src, Float* __restrict__ p_dst) const
{
Float p_clipboard[GetRegisterClipboardSize()];
RunLoadRegisterClipboard(p_src, p_clipboard);
RunStoreRegisterClipboard(p_clipboard, p_dst);
}
};
src/include/conv_common.hip.hpp
View file @ a9031464
......@@ -73,7 +73,6 @@ __host__ __device__ constexpr auto get_convolution_with_padding_output_default_4
template <class InDesc, class WeiDesc, class OutDesc>
__host__ __device__ constexpr std::size_t calculate_convolution_flops(InDesc, WeiDesc, OutDesc)
{
constexpr auto in_desc = InDesc{};
constexpr auto wei_desc = WeiDesc{};
constexpr auto out_desc = OutDesc{};
......
src/include/data_type.hip.hpp
View file @ a9031464
#pragma once
#include "config.h"
#include "constant_integral.hip.hpp"
template <class T, index_t N>
struct vector_type
......@@ -10,6 +11,13 @@ template <>
struct vector_type<float, 1>
{
typedef float MemoryType;
template <index_t I>
__host__ __device__ static void SetScalar(MemoryType& v, float s, Number<I>)
{
static_assert(I < 1, "wrong");
*(reinterpret_cast<float*>(&v) + I) = s;
}
};
template <>
......@@ -20,21 +28,29 @@ struct vector_type<float, 2>
// instruction
typedef float MemoryType __attribute__((ext_vector_type(2)));
#elif DEVICE_BACKEND_CUDA
// For some reason, CUDA need this definition to, otherwise
// For some reason, CUDA need this definition, otherwise
// compiler won't generate optimal load and store instruction, and
// kernel would produce wrong result, indicating the compiler fail to generate correct
// instruction,
using MemoryType = float2;
#endif
__host__ __device__ static MemoryType Pack(float s0, float s1)
union Data
{
union
{
MemoryType vector;
float scalar[2];
} data;
MemoryType vector;
float scalar[2];
};
template <index_t I>
__host__ __device__ static void SetScalar(MemoryType& v, float s, Number<I>)
{
static_assert(I < 2, "wrong");
*(reinterpret_cast<float*>(&v) + I) = s;
}
__host__ __device__ static MemoryType Pack(float s0, float s1)
{
Data data;
data.scalar[0] = s0;
data.scalar[1] = s1;
return data.vector;
......@@ -49,12 +65,19 @@ struct vector_type<float, 4>
// instruction
typedef float MemoryType __attribute__((ext_vector_type(4)));
#elif DEVICE_BACKEND_CUDA
// For some reason, CUDA need this definition to, otherwise
// For some reason, CUDA need this definition, otherwise
// compiler won't generate optimal load and store instruction, and
// kernel would produce wrong result, indicating the compiler fail to generate correct
// instruction,
using MemoryType = float4;
#endif
template <index_t I>
__host__ __device__ static void SetScalar(MemoryType& v, float s, Number<I>)
{
static_assert(I < 4, "wrong");
*(reinterpret_cast<float*>(&v) + I) = s;
}
};
#if 0
......
src/include/gridwise_convolution_implicit_gemm_v1r2_nchw_cyxk_khwn.hip.hpp
View file @ a9031464
......@@ -4,7 +4,7 @@
#include "ConstantMatrixDescriptor.hip.hpp"
#include "blockwise_2d_tensor_op.hip.hpp"
#include "blockwise_3d_tensor_op.hip.hpp"
#include "blockwise_4d_tensor_op.hip.hpp"
#include "blockwise_nd_tensor_op.hip.hpp"
#include "threadwise_nd_tensor_op.hip.hpp"
#include "threadwise_4d_tensor_op.hip.hpp"
#include "blockwise_batched_gemm.hip.hpp"
......@@ -125,17 +125,17 @@ struct GridwiseConvolutionImplicitGemm_v1r2_nchw_cyxk_khwn
constexpr auto map_chwn2nchw = Sequence<1, 2, 3, 0>{};
const auto blockwise_in_copy_reorder =
Blockwise4dTensorCopyReorder3<BlockSize,
Float,
decltype(in_n_c_h_w_global_desc),
decltype(in_c_h_w_n_block_desc),
Sequence<NPerBlock, CPerBlock, HoPerBlock, WiPerBlock>,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
decltype(map_chwn2nchw),
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N>{};
BlockwiseNdTensorCopyReorder_v3<BlockSize,
Float,
decltype(in_n_c_h_w_global_desc),
decltype(in_c_h_w_n_block_desc),
Sequence<NPerBlock, CPerBlock, HoPerBlock, WiPerBlock>,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
decltype(map_chwn2nchw),
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N>{};
// blockwise wei copy
// format is [CPerBlock, X * KPerBlock]
......
src/include/gridwise_convolution_implicit_gemm_v1r3_lds_double_buffer_nchw_cyxk_khwn.hip.hpp
View file @ a9031464
......@@ -3,7 +3,7 @@
#include "ConstantTensorDescriptor.hip.hpp"
#include "ConstantMatrixDescriptor.hip.hpp"
#include "blockwise_2d_tensor_op.hip.hpp"
#include "blockwise_4d_tensor_op.hip.hpp"
#include "blockwise_nd_tensor_op.hip.hpp"
#include "threadwise_nd_tensor_op.hip.hpp"
#include "threadwise_4d_tensor_op.hip.hpp"
#include "blockwise_batched_gemm.hip.hpp"
......@@ -133,17 +133,17 @@ struct GridwiseConvolutionImplicitGemm_v1r3_lds_double_buffer_nchw_cyxk_khwn
constexpr auto map_chwn2nchw = Sequence<1, 2, 3, 0>{};
const auto blockwise_in_copy_reorder =
Blockwise4dTensorCopyReorder3<BlockSize,
Float,
decltype(in_n_c_h_w_global_desc),
decltype(in_c_h_w_n_block_desc),
Sequence<NPerBlock, CPerBlock, HoPerBlock, WoPerBlock>,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
decltype(map_chwn2nchw),
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N>{};
BlockwiseNdTensorCopyReorder_v3<BlockSize,
Float,
decltype(in_n_c_h_w_global_desc),
decltype(in_c_h_w_n_block_desc),
Sequence<NPerBlock, CPerBlock, HoPerBlock, WoPerBlock>,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
decltype(map_chwn2nchw),
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N>{};
// blockwise wei copy
// format is [CPerBlock, KPerBlock]
......
src/include/gridwise_convolution_implicit_gemm_v1r3_nchw_cyxk_khwn.hip.hpp
View file @ a9031464
......@@ -3,7 +3,7 @@
#include "ConstantTensorDescriptor.hip.hpp"
#include "ConstantMatrixDescriptor.hip.hpp"
#include "blockwise_2d_tensor_op.hip.hpp"
#include "blockwise_4d_tensor_op.hip.hpp"
#include "blockwise_nd_tensor_op.hip.hpp"
#include "threadwise_nd_tensor_op.hip.hpp"
#include "threadwise_4d_tensor_op.hip.hpp"
#include "blockwise_batched_gemm.hip.hpp"
......@@ -130,17 +130,17 @@ struct GridwiseConvolutionImplicitGemm_v1r3_nchw_cyxk_khwn
constexpr auto map_chwn2nchw = Sequence<1, 2, 3, 0>{};
const auto blockwise_in_copy_reorder =
Blockwise4dTensorCopyReorder3<BlockSize,
Float,
decltype(in_n_c_h_w_global_desc),
decltype(in_c_h_w_n_block_desc),
Sequence<NPerBlock, CPerBlock, HoPerBlock, WoPerBlock>,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
decltype(map_chwn2nchw),
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N>{};
BlockwiseNdTensorCopyReorder_v3<BlockSize,
Float,
decltype(in_n_c_h_w_global_desc),
decltype(in_c_h_w_n_block_desc),
Sequence<NPerBlock, CPerBlock, HoPerBlock, WoPerBlock>,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
decltype(map_chwn2nchw),
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N>{};
// blockwise wei copy
// format is [CPerBlock, KPerBlock]
......
src/include/gridwise_convolution_implicit_gemm_v1r3_nchw_cyxk_nkhw.hip.hpp 0 → 100644
View file @ a9031464
#pragma once
#include "common.hip.hpp"
#include "ConstantTensorDescriptor.hip.hpp"
#include "ConstantMatrixDescriptor.hip.hpp"
#include "blockwise_2d_tensor_op.hip.hpp"
#include "blockwise_nd_tensor_op.hip.hpp"
#include "threadwise_nd_tensor_op.hip.hpp"
#include "threadwise_4d_tensor_op.hip.hpp"
#include "blockwise_batched_gemm.hip.hpp"
template <index_t GridSize,
index_t BlockSize,
class Float,
class InGlobalDesc,
class WeiGlobalDesc,
class OutGlobalDesc,
index_t NPerBlock,
index_t KPerBlock,
index_t CPerBlock,
index_t HoPerBlock,
index_t WoPerBlock,
index_t NPerThread,
index_t KPerThread,
index_t HoPerThread,
index_t WoPerThread,
index_t GemmMPerThreadSubC,
index_t GemmNPerThreadSubC,
index_t GemmMLevel0Cluster,
index_t GemmNLevel0Cluster,
index_t GemmMLevel1Cluster,
index_t GemmNLevel1Cluster,
index_t GemmKPerThreadLoop,
index_t GemmDataPerReadA,
index_t GemmDataPerReadB,
class InBlockReorderSrcSubLengths_NCHW,
class InBlockReorderSrcClusterLengths_NCHW,
class InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
index_t InBlockReorderDataPerRead_W,
index_t InBlockReorderDataPerWrite_N,
class WeiBlockCopyClusterLengths_CK, // not used
index_t WeiBlockCopyDataPerRead_K,
index_t OutThreadCopyDataPerWrite_W>
struct GridwiseConvolutionImplicitGemm_v1r3_nchw_cyxk_nkhw
{
__device__ void Run(const Float* const __restrict__ p_in_global,
const Float* const __restrict__ p_wei_global,
Float* const __restrict__ p_out_global) const
{
// be careful of this assertion
static_assert(NPerBlock % NPerThread == 0 && (GemmNPerThreadSubC <= NPerBlock &&
NPerBlock % GemmNPerThreadSubC == 0) ||
(GemmNPerThreadSubC >= NPerBlock && NPerThread == NPerBlock &&
GemmNPerThreadSubC % NPerThread == 0),
"wrong!");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto in_n_c_h_w_global_desc = InGlobalDesc{};
constexpr auto wei_c_y_x_k_global_desc = WeiGlobalDesc{};
constexpr auto out_n_k_h_w_global_desc = OutGlobalDesc{};
constexpr index_t C = in_n_c_h_w_global_desc.GetLength(I1);
constexpr index_t N = out_n_k_h_w_global_desc.GetLength(I0);
constexpr index_t K = out_n_k_h_w_global_desc.GetLength(I1);
constexpr index_t Ho = out_n_k_h_w_global_desc.GetLength(I2);
constexpr index_t Wo = out_n_k_h_w_global_desc.GetLength(I3);
constexpr index_t Y = wei_c_y_x_k_global_desc.GetLength(I1);
constexpr index_t X = wei_c_y_x_k_global_desc.GetLength(I2);
// divide block work: [K, Ho, Wo, N]
static_assert(N % NPerBlock == 0 && K % KPerBlock == 0 && C % CPerBlock == 0 &&
Ho % HoPerBlock == 0 && Wo % WoPerBlock == 0,
"wrong! cannot evenly divide work for workgroup ");
// constexpr index_t KBlockWork = (K + KPerBlock - 1) / KPerBlock;
constexpr index_t HBlockWork = (Ho + HoPerBlock - 1) / HoPerBlock;
constexpr index_t WBlockWork = (Wo + WoPerBlock - 1) / WoPerBlock;
constexpr index_t NBlockWork = (N + NPerBlock - 1) / NPerBlock;
const index_t k_block_work_id = get_block_1d_id() / (HBlockWork * WBlockWork * NBlockWork);
index_t itmp = get_block_1d_id() - k_block_work_id * (HBlockWork * WBlockWork * NBlockWork);
const index_t h_block_work_id = itmp / (WBlockWork * NBlockWork);
itmp -= h_block_work_id * (WBlockWork * NBlockWork);
const index_t w_block_work_id = itmp / NBlockWork;
const index_t n_block_work_id = itmp - w_block_work_id * NBlockWork;
const index_t k_block_data_begin = k_block_work_id * KPerBlock;
const index_t ho_block_data_begin = h_block_work_id * HoPerBlock;
const index_t wo_block_data_begin = w_block_work_id * WoPerBlock;
const index_t n_block_data_begin = n_block_work_id * NPerBlock;
const index_t hi_block_data_begin = ho_block_data_begin;
const index_t wi_block_data_begin = wo_block_data_begin;
// global tensor view
constexpr auto wei_c_k_global_desc =
make_ConstantTensorDescriptor(Sequence<C, K>{}, Sequence<Y * X * K, 1>{});
// LDS tensor view
// be careful of alignment
constexpr index_t max_align = mod_conv::max(InBlockReorderDataPerWrite_N,
WeiBlockCopyDataPerRead_K,
GemmDataPerReadA,
GemmDataPerReadB);
constexpr auto in_c_h_w_n_block_desc = make_ConstantTensorDescriptor_aligned(
Sequence<CPerBlock, HoPerBlock, WoPerBlock, NPerBlock>{},
Number<InBlockReorderDataPerWrite_N>{});
// this check is ad-hoc
// TODO: need to properly implement tensor descriptor with alignment
static_assert(in_c_h_w_n_block_desc.GetStride(I1) % GemmDataPerReadB == 0,
"GemmDataPerReadB alignment requirement is not meet");
constexpr auto wei_c_k_block_desc = make_ConstantTensorDescriptor_aligned(
Sequence<CPerBlock, KPerBlock>{},
Number<mod_conv::max(WeiBlockCopyDataPerRead_K, GemmDataPerReadA)>{});
// tensor view of threadwise output in register
constexpr auto out_k_h_w_n_thread_desc = make_ConstantTensorDescriptor(
Sequence<KPerThread, HoPerThread, WoPerThread, NPerThread>{});
// blockwise copy
// input: format is [N, C, Hi, Wi] to [C, Hi, Wi, N]
constexpr auto map_chwn2nchw = Sequence<1, 2, 3, 0>{};
const auto blockwise_in_copy_reorder =
BlockwiseNdTensorCopyReorder_v3<BlockSize,
Float,
decltype(in_n_c_h_w_global_desc),
decltype(in_c_h_w_n_block_desc),
Sequence<NPerBlock, CPerBlock, HoPerBlock, WoPerBlock>,
InBlockReorderSrcSubLengths_NCHW,
InBlockReorderSrcClusterLengths_NCHW,
decltype(map_chwn2nchw),
InBlockReorderMapThreadCluster2SrcCluster_CHNW2NCHW,
InBlockReorderDataPerRead_W,
InBlockReorderDataPerWrite_N>{};
// blockwise wei copy
// format is [CPerBlock, KPerBlock]
const auto blockwise_wei_copy =
Blockwise2dTensorCopy3<BlockSize,
Float,
decltype(wei_c_k_global_desc),
decltype(wei_c_k_block_desc),
decltype(wei_c_k_block_desc.GetLengths()),
WeiBlockCopyDataPerRead_K>{};
// a series of blockwise batched GEMM
// C_matrix += transpose(A_matrix) * B_matrix
// A_matrix and B_matrix saved in LDS, C_matrix saved in register
// A_matrix[C,K] is a sub-matrix of wei_block[C,K]
// B_matrix[C,Wo*N] is a sub-matrix of in_block[C,Hi,Wi,N]
// C_matrix[K,Wo*N] is a sub-matrix of out_block[K,Ho,Wo,N]
constexpr auto a_c_k_block_mtx_desc = make_ConstantMatrixDescriptor(
Number<CPerBlock>{}, Number<KPerBlock>{}, Number<wei_c_k_block_desc.GetStride(I0)>{});
constexpr auto b_c_wn_block_mtx_desc =
make_ConstantMatrixDescriptor(Number<CPerBlock>{},
Number<WoPerBlock * NPerBlock>{},
Number<in_c_h_w_n_block_desc.GetStride(I0)>{});
constexpr auto c_k_wn_thread_mtx_desc =
make_ConstantMatrixDescriptor(Number<KPerThread>{},
Number<WoPerThread * NPerThread>{},
Number<out_k_h_w_n_thread_desc.GetStride(I0)>{});
const auto blockwise_batch_gemm =
BlockwiseBatchGemmBlockABlockBThreadCTransANormalBNormalC_V2<
BlockSize,
decltype(a_c_k_block_mtx_desc),
decltype(b_c_wn_block_mtx_desc),
decltype(c_k_wn_thread_mtx_desc),
0,
in_c_h_w_n_block_desc.GetStride(I1),
out_k_h_w_n_thread_desc.GetStride(I1),
HoPerBlock,
GemmMPerThreadSubC,
GemmNPerThreadSubC,
GemmMLevel0Cluster,
GemmNLevel0Cluster,
GemmMLevel1Cluster,
GemmNLevel1Cluster,
GemmKPerThreadLoop,
HoPerThread,
GemmDataPerReadA,
GemmDataPerReadB>{};
// LDS: be careful of alignment
constexpr index_t in_block_space =
in_c_h_w_n_block_desc.GetElementSpace(Number<max_align>{});
constexpr index_t wei_block_space = wei_c_k_block_desc.GetElementSpace(Number<max_align>{});
__shared__ Float p_in_block[in_block_space];
__shared__ Float p_wei_block[wei_block_space];
// register
// C++ lambda doesn't capture array, use pointer instead
Float p_out_thread_data[out_k_h_w_n_thread_desc.GetElementSpace()];
Float* const p_out_thread = p_out_thread_data;
#if 0
if(get_thread_local_1d_id() == 0 && get_block_1d_id() == 0)
{
print_ConstantTensorDescriptor(in_c_h_w_n_global_desc, "in_c_h_w_n_global_desc");
print_ConstantTensorDescriptor(wei_c_y_x_k_global_desc, "wei_c_y_x_k_global_desc");
print_ConstantTensorDescriptor(in_c_h_w_n_block_desc, "in_c_h_w_n_block_desc");
print_ConstantTensorDescriptor(wei_c_k_block_desc, "wei_c_k_block_desc");
printf("in_block_space %u, wei_block_space %u\n", in_block_space, wei_block_space);
}
#endif
// set threadwise output tensor to 0
threadwise_4d_tensor_set_zero(out_k_h_w_n_thread_desc, p_out_thread);
#if 1
const Float* p_in_global_block_offset =
p_in_global + in_n_c_h_w_global_desc.Get1dIndex(
n_block_data_begin, 0, hi_block_data_begin, wi_block_data_begin);
const Float* p_wei_global_block_offset =
p_wei_global + wei_c_y_x_k_global_desc.Get1dIndex(0, 0, 0, k_block_data_begin);
for(index_t c_block_data_begin = 0; c_block_data_begin < C; c_block_data_begin += CPerBlock,
p_in_global_block_offset += CPerBlock * in_n_c_h_w_global_desc.GetStride(I1),
p_wei_global_block_offset += CPerBlock * wei_c_y_x_k_global_desc.GetStride(I0))
{
for(index_t y = 0; y < Y; ++y)
{
for(index_t x = 0; x < X; ++x)
{
#if 1
blockwise_in_copy_reorder.Run(p_in_global_block_offset +
in_n_c_h_w_global_desc.Get1dIndex(0, 0, y, x),
p_in_block);
blockwise_wei_copy.Run(p_wei_global_block_offset +
wei_c_y_x_k_global_desc.Get1dIndex(0, y, x, 0),
p_wei_block);
#else
Float p_in_clipboard[blockwise_in_copy_reorder.GetRegisterClipboardSize()];
Float p_wei_clipboard[blockwise_wei_copy.GetRegisterClipboardSize()];
blockwise_in_copy_reorder.RunLoadRegisterClipboard(
p_in_global_block_offset + in_n_c_h_w_global_desc.Get1dIndex(0, 0, y, x),
p_in_clipboard);
blockwise_wei_copy.RunLoadRegisterClipboard(
p_wei_global_block_offset + wei_c_y_x_k_global_desc.Get1dIndex(0, y, x, 0),
p_wei_clipboard);
blockwise_wei_copy.RunStoreRegisterClipboard(p_wei_clipboard, p_wei_block);
blockwise_in_copy_reorder.RunStoreRegisterClipboard(p_in_clipboard, p_in_block);
#endif
__syncthreads();
blockwise_batch_gemm.Run(p_wei_block, p_in_block, p_out_thread);
__syncthreads();
}
}
}
#else
for(index_t y = 0; y < Y; ++y)
{
for(index_t x = 0; x < X; ++x)
{
const Float* p_in_global_block_offset =
p_in_global +
in_n_c_h_w_global_desc.Get1dIndex(
n_block_data_begin, 0, hi_block_data_begin + y, wi_block_data_begin + x);
const Float* p_wei_global_block_offset =
p_wei_global + wei_c_y_x_k_global_desc.Get1dIndex(0, y, x, k_block_data_begin);
for(index_t c_block_data_begin = 0; c_block_data_begin < C;
c_block_data_begin += CPerBlock,
p_in_global_block_offset +=
CPerBlock * in_n_c_h_w_global_desc.GetStride(I1),
p_wei_global_block_offset +=
CPerBlock * wei_c_y_x_k_global_desc.GetStride(I0))
{
#if 0
blockwise_in_copy_reorder.Run(p_in_global_block_offset,
p_in_block);
blockwise_wei_copy.Run(p_wei_global_block_offset,
p_wei_block);
#else
Float p_in_clipboard[blockwise_in_copy_reorder.GetRegisterClipboardSize()];
Float p_wei_clipboard[blockwise_wei_copy.GetRegisterClipboardSize()];
blockwise_in_copy_reorder.RunLoadRegisterClipboard(p_in_global_block_offset,
p_in_clipboard);
blockwise_wei_copy.RunLoadRegisterClipboard(p_wei_global_block_offset,
p_wei_clipboard);
blockwise_wei_copy.RunStoreRegisterClipboard(p_wei_clipboard, p_wei_block);
blockwise_in_copy_reorder.RunStoreRegisterClipboard(p_in_clipboard, p_in_block);
#endif
__syncthreads();
blockwise_batch_gemm.Run(p_wei_block, p_in_block, p_out_thread);
__syncthreads();
}
}
}
#endif
// output: register to global mem,
const auto c_thread_mtx_begin =
blockwise_batch_gemm.GetBeginOfThreadMatrixC(get_thread_local_1d_id());
const index_t k_thread_data_begin = c_thread_mtx_begin.row;
const index_t ho_thread_data_begin = c_thread_mtx_begin.batch;
const index_t wo_thread_data_begin = c_thread_mtx_begin.col / NPerBlock;
const index_t n_thread_data_begin = c_thread_mtx_begin.col % NPerBlock;
static_if<GemmNPerThreadSubC <= NPerBlock>{}(
[&](auto f_dummy) { // f_dummy do nothing but perfect forwarding. Using this trick to
// make this lambda a generic lambda, so it won't be compiled until
// instantiated
static_assert((f_dummy(GemmNPerThreadSubC) <= NPerBlock &&
NPerBlock % GemmNPerThreadSubC == 0),
"wrong!");
// output is a 10d tensor
constexpr index_t N2 = GemmNPerThreadSubC;
constexpr index_t N1 = NPerBlock / N2;
constexpr index_t W2 = (GemmNLevel0Cluster * GemmNLevel1Cluster) /
f_dummy(NPerBlock / GemmNPerThreadSubC);
constexpr index_t W1 = WoPerBlock / W2;
constexpr index_t K2 = GemmMPerThreadSubC;
constexpr index_t K1 = KPerBlock / KPerThread;
#if 0
constexpr auto out_10d_global_desc =
make_ConstantTensorDescriptor(Sequence<K / (K1 * K2),
K1,
K2,
Ho,
Wo / (W1 * W2),
W1,
W2,
N / f_dummy(N1 * N2),
N1,
N2>{});
#else
constexpr auto out_10d_global_desc =
make_ConstantTensorDescriptor(Sequence<N / f_dummy(N1 * N2),
N1,
N2,
K / (K1 * K2),
K1,
K2,
Ho,
Wo / (W1 * W2),
W1,
W2>{});
#endif
constexpr auto out_10d_thread_desc = make_ConstantTensorDescriptor(
Sequence<KPerThread / K2, 1, K2, HoPerThread, 1, W1, 1, 1, 1, N2>{});
#if 0
if(get_thread_local_1d_id() == 0 && get_block_1d_id() == 0)
{
print_ConstantTensorDescriptor(out_k_h_w_n_thread_desc,
"out_k_h_w_n_thread_desc");
print_ConstantTensorDescriptor(out_10d_thread_desc, "out_10d_thread_desc");
print_ConstantTensorDescriptor(out_k_h_w_n_global_desc,
"out_k_h_w_n_global_desc");
print_ConstantTensorDescriptor(out_10d_global_desc, "out_10d_global_desc");
}
#endif
#if 0
threadwise_nd_tensor_copy(out_10d_thread_desc,
p_out_thread,
out_10d_global_desc,
p_out_global +
out_k_h_w_n_global_desc.Get1dIndex(
k_block_data_begin + k_thread_data_begin,
ho_block_data_begin + ho_thread_data_begin,
wo_block_data_begin + wo_thread_data_begin,
n_block_data_begin + n_thread_data_begin),
out_10d_thread_desc.GetLengths(),
Number<OutThreadCopyDataPerWrite_N>{});
#else
constexpr auto map_out_global2thread = Sequence<7, 8, 9, 0, 1, 2, 6, 3, 4, 5>{};
threadwise_nd_tensor_copy_reorder_given_dst2src_v2(
out_10d_thread_desc,
p_out_thread,
out_10d_global_desc,
p_out_global + out_n_k_h_w_global_desc.Get1dIndex(
n_block_data_begin + n_thread_data_begin,
k_block_data_begin + k_thread_data_begin,
ho_block_data_begin + ho_thread_data_begin,
wo_block_data_begin + wo_thread_data_begin),
out_10d_thread_desc.GetLengths(),
map_out_global2thread);
// Number<OutThreadCopyDataPerWrite_W>{});
#endif
})
.else_([&](auto f_dummy) {
static_assert(f_dummy(GemmNPerThreadSubC) >= NPerBlock && NPerThread == NPerBlock &&
GemmNPerThreadSubC % NPerThread == 0,
"wrong!");
// output is a 10d tensor
constexpr index_t N1 = NPerBlock;
constexpr index_t W3 = GemmNPerThreadSubC / NPerBlock;
constexpr index_t W2 = GemmNLevel0Cluster * GemmNLevel1Cluster;
constexpr index_t W1 = WoPerBlock / f_dummy(W2 * W3);
constexpr index_t K2 = GemmMPerThreadSubC;
constexpr index_t K1 = KPerBlock / KPerThread;
#if 0
constexpr auto out_10d_global_desc =
make_ConstantTensorDescriptor(Sequence<K / (K1 * K2),
K1,
K2,
Ho,
Wo / (W1 * W2 * W3),
W1,
W2,
W3,
N / N1,
N1>{});
#else
constexpr auto out_10d_global_desc =
make_ConstantTensorDescriptor(Sequence<N / N1,
N1,
K / (K1 * K2),
K1,
K2,
Ho,
Wo / (W1 * W2 * W3),
W1,
W2,
W3>{});
#endif
constexpr auto out_10d_thread_desc = make_ConstantTensorDescriptor(
Sequence<KPerThread / K2, 1, K2, HoPerThread, 1, W1, 1, W3, 1, N1>{});
#if 0
if(get_thread_local_1d_id() == 0 && get_block_1d_id() == 0)
{
print_ConstantTensorDescriptor(out_k_h_w_n_thread_desc,
"out_k_h_w_n_thread_desc");
print_ConstantTensorDescriptor(out_10d_thread_desc, "out_10d_thread_desc");
print_ConstantTensorDescriptor(out_k_h_w_n_global_desc,
"out_k_h_w_n_global_desc");
print_ConstantTensorDescriptor(out_10d_global_desc, "out_10d_global_desc");
for(index_t i = 0; i < 64; ++i)
{
printf("out %f, ", p_out_thread[i]);
}
}
#endif
#if 0
threadwise_nd_tensor_copy(out_10d_thread_desc,
p_out_thread,
out_10d_global_desc,
p_out_global +
out_k_h_w_n_global_desc.Get1dIndex(
k_block_data_begin + k_thread_data_begin,
ho_block_data_begin + ho_thread_data_begin,
wo_block_data_begin + wo_thread_data_begin,
n_block_data_begin + n_thread_data_begin),
out_10d_thread_desc.GetLengths(),
Number<OutThreadCopyDataPerWrite_N>{});
#else
constexpr auto map_out_global2thread = Sequence<8, 9, 0, 1, 2, 3, 4, 5, 6, 7>{};
threadwise_nd_tensor_copy_reorder_given_dst2src_v2(
out_10d_thread_desc,
p_out_thread,
out_10d_global_desc,
p_out_global + out_n_k_h_w_global_desc.Get1dIndex(
n_block_data_begin + n_thread_data_begin,
k_block_data_begin + k_thread_data_begin,
ho_block_data_begin + ho_thread_data_begin,
wo_block_data_begin + wo_thread_data_begin),
out_10d_thread_desc.GetLengths(),
map_out_global2thread);
// Number<OutThreadCopyDataPerWrite_W>{});
#endif
});
}
};
src/include/threadwise_4d_tensor_op.hip.hpp
View file @ a9031464
......@@ -139,135 +139,6 @@ __device__ void threadwise_4d_tensor_copy_reorder_given_dst2src(SrcDesc,
SrcDesc{}, p_src, DstDesc{}, p_dst, SrcOpLengths{}, MapDst2Src{}, f_copy);
}
#if 0 // replaced threadwise_nd_tensor_copy
template <class SrcData, class DstData, class SrcDesc, class DstDesc, class SrcOpLengths>
__device__ void threadwise_4d_tensor_copy(
SrcDesc, const SrcData* __restrict__ p_src, DstDesc, DstData* __restrict__ p_dst, SrcOpLengths)
{
auto dst_from_src_reorder = Sequence<0, 1, 2, 3>{};
threadwise_4d_tensor_copy_reorder_given_dst2src(
SrcDesc{}, p_src, DstDesc{}, p_dst, SrcOpLengths{}, dst_from_src_reorder);
}
// need to assume src and dst is aligned
template <class Float, class SrcDesc, class DstDesc, class SrcOpLengths, index_t DataPerRead>
__device__ void threadwise_4d_tensor_copy_v2(SrcDesc,
const Float* __restrict__ p_src,
DstDesc,
Float* __restrict__ p_dst,
SrcOpLengths,
Number<DataPerRead>)
{
static_assert(SrcDesc{}.GetDimension() == 4 && DstDesc{}.GetDimension() == 4 &&
SrcOpLengths::GetSize() == 4,
"wrong! should be 4 dimension");
using vector_t = typename vector_type<Float, DataPerRead>::MemoryType;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto ref_desc = make_ConstantTensorDescriptor(SrcOpLengths{});
static_assert(SrcDesc{}.GetStride(I3) == 1 && DstDesc{}.GetStride(I3) == 1,
"wrong! only support stride3 == 1!\n");
static_assert(DataPerRead == 1 || DataPerRead == 2 || DataPerRead == 4,
"wrong! only support DataPerRead == 1, 2 or 4!\n");
static_assert(SrcDesc{}.GetStride(I2) % DataPerRead == 0 &&
DstDesc{}.GetStride(I2) % DataPerRead == 0,
"wrong! src and dst stride should be multiple of DataPerRead to keep alignment");
constexpr index_t L3 = SrcOpLengths{}.Get(I3);
static_assert(L3 % DataPerRead == 0, "wrong! L3 should be evenly divided by DataPerRead");
constexpr index_t nloop_d3 = L3 / DataPerRead;
for(index_t did0 = 0; did0 < ref_desc.GetLength(I0); ++did0)
{
for(index_t did1 = 0; did1 < ref_desc.GetLength(I1); ++did1)
{
for(index_t did2 = 0; did2 < ref_desc.GetLength(I2); ++did2)
{
for(index_t iloop_d3 = 0; iloop_d3 < nloop_d3; ++iloop_d3)
{
const index_t src_index =
src_desc.Get1dIndex(did0, did1, did2, iloop_d3 * DataPerRead);
const index_t dst_index =
dst_desc.Get1dIndex(did0, did1, did2, iloop_d3 * DataPerRead);
*(reinterpret_cast<vector_t*>(&p_dst[dst_index])) =
*(reinterpret_cast<const vector_t*>(&p_src[src_index]));
}
}
}
}
}
#endif
template <class SrcData,
class DstData,
class SrcDesc,
class DstDesc,
class SrcOpLengths,
class MapDst2Src>
__device__ void
threadwise_4d_tensor_copy_reorder_given_dst2src_v2(SrcDesc,
const SrcData* __restrict__ p_src,
DstDesc,
DstData* __restrict__ p_dst,
SrcOpLengths,
MapDst2Src)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr index_t IR0 = MapDst2Src{}.Get(I0);
constexpr index_t IR1 = MapDst2Src{}.Get(I1);
constexpr index_t IR2 = MapDst2Src{}.Get(I2);
constexpr index_t IR3 = MapDst2Src{}.Get(I3);
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
// ref_desc has dst_desc's ordering
constexpr auto ref_desc =
make_ConstantTensorDescriptor(SrcOpLengths{}.ReorderGivenNew2Old(MapDst2Src{}));
for(index_t did0 = 0; did0 < ref_desc.GetLength(I0); ++did0)
{
for(index_t did1 = 0; did1 < ref_desc.GetLength(I1); ++did1)
{
for(index_t did2 = 0; did2 < ref_desc.GetLength(I2); ++did2)
{
for(index_t did3 = 0; did3 < ref_desc.GetLength(I3); ++did3)
{
const auto dst_multi_id = Array<index_t, 4>{did0, did1, did2, did3};
const auto src_multi_id =
reorder_array_given_old2new(dst_multi_id, MapDst2Src{});
const index_t dst_index = dst_desc.Get1dIndex(dst_multi_id);
const index_t src_index = src_desc.Get1dIndex(src_multi_id);
p_dst[dst_index] = p_src[src_index];
}
}
}
}
}
template <class Float, class Desc, class IDim, class NShift>
__device__ void threadwise_4d_tensor_shift_down(Desc, Float* __restrict__ p, IDim, NShift)
{
......
src/include/threadwise_nd_tensor_op.hip.hpp
View file @ a9031464
......@@ -50,7 +50,7 @@ __device__ void threadwise_nd_tensor_copy(SrcDesc,
constexpr index_t nRead = L_Back / DataPerRead;
static_ford<decltype(ref_desc.GetLengths().PopBack())>{}([=](auto Ids) {
static_for<0, nRead, 1>{}([=](auto IRead) {
static_for<0, nRead, 1>{}([&](auto IRead) {
constexpr auto multi_id = decltype(Ids){}.PushBack(Number<IRead.Get() * DataPerRead>{});
const index_t src_index = src_desc.Get1dIndex(multi_id);
......@@ -62,3 +62,131 @@ __device__ void threadwise_nd_tensor_copy(SrcDesc,
});
});
}
// write in order of src
template <class SrcData,
class DstData,
class SrcDesc,
class DstDesc,
class SrcOpLengths,
class MapDst2Src>
__device__ void
threadwise_nd_tensor_copy_reorder_given_dst2src_v1(SrcDesc,
const SrcData* __restrict__ p_src,
DstDesc,
DstData* __restrict__ p_dst,
SrcOpLengths,
MapDst2Src)
{
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
ford<SrcOpLengths>{}([&](auto src_multi_id) {
const auto dst_multi_id = reorder_array_given_new2old(src_multi_id, MapDst2Src{});
const index_t dst_index = dst_desc.Get1dIndex(dst_multi_id);
const index_t src_index = src_desc.Get1dIndex(src_multi_id);
p_dst[dst_index] = p_src[src_index];
});
}
// write in order of dst
template <class SrcData,
class DstData,
class SrcDesc,
class DstDesc,
class SrcOpLengths,
class MapDst2Src>
__device__ void
threadwise_nd_tensor_copy_reorder_given_dst2src_v2(SrcDesc,
const SrcData* __restrict__ p_src,
DstDesc,
DstData* __restrict__ p_dst,
SrcOpLengths,
MapDst2Src)
{
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto dst_op_lengths = SrcOpLengths{}.ReorderGivenNew2Old(MapDst2Src{});
ford<decltype(dst_op_lengths)>{}([&](auto dst_multi_id) {
const auto src_multi_id = reorder_array_given_old2new(dst_multi_id, MapDst2Src{});
const index_t dst_index = dst_desc.Get1dIndex(dst_multi_id);
const index_t src_index = src_desc.Get1dIndex(src_multi_id);
p_dst[dst_index] = p_src[src_index];
});
}
// write in order of dst
template <class Float,
class SrcDesc,
class DstDesc,
class SrcOpLengths,
class MapDst2Src,
index_t DstDataPerWrite>
__device__ void threadwise_nd_tensor_copy_reorder_given_dst2src_v3(SrcDesc,
const Float* __restrict__ p_src,
DstDesc,
Float* __restrict__ p_dst,
SrcOpLengths,
MapDst2Src,
Number<DstDataPerWrite>)
{
using vector_t = typename vector_type<Float, DstDataPerWrite>::MemoryType;
constexpr index_t nDim = SrcOpLengths::GetSize();
static_assert(DstDataPerWrite == 1 || DstDesc{}.GetStride(Number<nDim - 1>{}) == 1,
"wrong! only support dst.stride[nDim-1] == 1, if DstDataPerWrite != 1");
static_assert(DstDataPerWrite == 1 || DstDataPerWrite == 2 || DstDataPerWrite == 4,
"wrong! only support DstDataPerWrite == 1, 2 or 4");
static_assert(
DstDesc{}.GetStride(Number<nDim - 2>{}) % DstDataPerWrite == 0,
"wrong! dst.stride[nDim-2] should be multiple of DstDataPerWrite to keep alignment");
constexpr auto src_desc = SrcDesc{};
constexpr auto dst_desc = DstDesc{};
constexpr auto dst_op_lengths = SrcOpLengths{}.ReorderGivenNew2Old(MapDst2Src{});
constexpr index_t L_Dst_Back = dst_op_lengths.Back();
static_assert(L_Dst_Back % DstDataPerWrite == 0,
"wrong! dst.lengths[nDim-1] should be evenly divided by DstDataPerWrite");
constexpr index_t nWrite = L_Dst_Back / DstDataPerWrite;
ford<decltype(dst_op_lengths.PopBack())>{}([&](auto ids) {
static_for<0, nWrite, 1>{}([&](auto IWrite) {
vector_t dst_vec_data;
// pack data
static_for<0, DstDataPerWrite, 1>{}([&](auto IDstData) {
const auto dst_multi_id =
ids.PushBack(IWrite.Get() * DstDataPerWrite + IDstData.Get());
const auto src_multi_id = reorder_array_given_old2new(dst_multi_id, MapDst2Src{});
const index_t src_index = src_desc.Get1dIndex(src_multi_id);
vector_type<Float, DstDataPerWrite>::SetScalar(
dst_vec_data, p_src[src_index], IDstData);
});
// write data
const auto dst_multi_id = ids.PushBack(IWrite.Get() * DstDataPerWrite);
const index_t dst_index = dst_desc.Get1dIndex(dst_multi_id);
*(reinterpret_cast<vector_t*>(&p_dst[dst_index])) = dst_vec_data;
});
});
}
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