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Commit e87aa851 authored by Chao Liu's avatar Chao Liu
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adding implcit GEMM v4r2

parent ce4ec7dc
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Showing with 198 additions and 1257 deletions
composable_kernel/include/kernel_algorithm/gridwise_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw_lds_double_buffer.hpp
View file @ e87aa851
......@@ -208,13 +208,12 @@ struct GridwiseConvolutionImplicitGemm_v4r1_nchw_kcyx_nkhw_lds_double_buffer
// b_mtx[EPerBlocl, N1 * BPerBlock * N2] is in LDS
// c_mtx[KPerBlock, N1 * BPerBlock * N2] is distributed among threads, and saved in
// register
constexpr auto a_e_k_block_mtx_desc = make_ConstantMatrixDescriptor(
Number<EPerBlock>{}, Number<KPerBlock>{}, Number<wei_e_k_block_desc.GetStride(I0)>{});
constexpr auto a_e_k_block_mtx_desc =
make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(wei_e_k_block_desc);
constexpr auto b_e_n1bn2_block_mtx_desc =
make_ConstantMatrixDescriptor(Number<EPerBlock>{},
Number<N1 * BPerBlock * N2>{},
Number<in_e_n1_b_n2_block_desc.GetStride(I0)>{});
make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(
in_e_n1_b_n2_block_desc.Unfold(I1, I3));
// sanity check
static_assert(KPerBlock % (GemmMPerThreadSubC * GemmMLevel0Cluster * GemmMLevel1Cluster) ==
......@@ -226,7 +225,7 @@ struct GridwiseConvolutionImplicitGemm_v4r1_nchw_kcyx_nkhw_lds_double_buffer
// c_thread_mtx definition: this is a mess
// TODO:: more elegent way of defining c_thread_mtx
constexpr auto c_k0k2_n1n2_thread_mtx_desc = make_ConstantMatrixDescriptor(
constexpr auto c_k0k2_n1n2_thread_mtx_desc = make_ConstantMatrixDescriptor_packed(
Number<GemmMRepeat * GemmMPerThreadSubC>{}, Number<N1 * N2>{});
const auto blockwise_gemm = BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2<
......
composable_kernel/include/kernel_algorithm/gridwise_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer.hpp
View file @ e87aa851
......@@ -20,15 +20,18 @@ template <index_t GridSize,
class OutGlobalDesc,
class ConvStrides,
class ConvDilations,
index_t BPerBlock,
index_t KPerBlock,
index_t EPerBlock,
index_t N0,
index_t N1,
index_t N2,
index_t Ho0,
index_t Ho1,
index_t Ho2,
index_t Wo0,
index_t Wo1,
index_t Wo2,
index_t BPerBlock,
index_t KPerBlock,
index_t EPerBlock,
index_t N0PerBlock,
index_t Ho0PerBlock,
index_t Wo0PerBlock,
index_t GemmMPerThreadSubC,
index_t GemmNPerThreadSubC,
index_t GemmMLevel0Cluster,
......@@ -38,13 +41,12 @@ template <index_t GridSize,
index_t GemmKPerThreadLoop,
index_t GemmDataPerReadA,
index_t GemmDataPerReadB,
class InBlockCopySubLengths_E_N1_B_N2,
class InBlockCopyClusterLengths_E_N1_B_N2,
class InBlockCopySubLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2,
class InBlockCopyClusterLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2,
class InBlockCopyThreadClusterArrangeOrder,
class InBlockCopySrcAccessOrder,
class InBlockCopyDstAccessOrder,
index_t InBlockCopySrcDataPerRead_B,
index_t InBlockCopyDstDataPerWrite_N2,
index_t InBlockCopyDataPerAccess_W2,
class WeiBlockCopySubLengths_E_K,
class WeiBlockCopyClusterLengths_E_K,
class WeiBlockCopyThreadClusterArrangeOrder,
......@@ -60,8 +62,8 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
{
// this is a mess
// TODO: find more elegent way of specifying (or calculating) performance parameters
static_assert(N2 == GemmNPerThreadSubC, "wrong!");
static_assert((N1 * N2 * BPerBlock) %
static_assert(N2 * Ho2 * Wo2 == GemmNPerThreadSubC, "wrong!");
static_assert((N1 * Ho1 * Wo1 * BPerBlock * N2 * Ho2 * Wo2) %
(GemmNPerThreadSubC * GemmNLevel0Cluster * GemmNLevel1Cluster) ==
0,
"wrong!");
......@@ -71,6 +73,7 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I5 = Number<5>{};
constexpr auto I7 = Number<7>{};
constexpr auto True = integral_constant<bool, true>{};
......@@ -96,13 +99,16 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
constexpr index_t E = C * Y * X;
constexpr index_t N1 = N / (N0 * N2);
constexpr index_t Ho1 = Ho / (Ho0 * Ho2);
constexpr index_t Wo1 = Wo / (Wo0 * Wo2);
constexpr index_t B = N1 * Ho1 * Wo1;
constexpr index_t B1 = N1 * Ho1 * Wo1;
static_assert(N % (N1 * N2) == 0 && Ho % (Ho1 * Ho2) == 0 && Wo % (Wo1 * Wo2) == 0,
"wrong!");
constexpr index_t N0 = N / (N1 * N2);
constexpr index_t Ho0 = Ho / (Ho1 * Ho2);
constexpr index_t Wo0 = Wo / (Wo1 * Wo2);
static_assert((X == 1 || ConvDilationW % InBlockCopySrcDataPerRead_B == 0),
static_assert((X == 1 || ConvDilationW % InBlockCopyDataPerAccess_W2 == 0),
"wrong! aligment requirement for vectorized global load of input tensor will "
"be violated");
......@@ -110,17 +116,17 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
static_assert(K % KPerBlock == 0 && B % BPerBlock == 0 && E % (2 * EPerBlock) == 0,
"wrong! cannot divide work evenly among block");
constexpr index_t KBlockWork = K / KPerBlock;
constexpr index_t B1BlockWork = B1 / B1PerBlock;
constexpr index_t KBlockWork = K / KPerBlock;
constexpr index_t BBlockWork = B / BPerBlock;
constexpr auto block_work_desc =
make_ConstantTensorDescriptor_packed(Sequence<KBlockWork, B1BlockWork>{});
make_ConstantTensorDescriptor_packed(Sequence<KBlockWork, BBlockWork>{});
const auto block_work_multi_id =
block_work_desc.GetMultiIndexFrom1dIndex(get_block_1d_id());
const index_t k_block_data_on_global = block_work_multi_id[0] * KPerBlock;
const index_t b1_block_data_on_global = block_work_multi_id[1] * B1PerBlock;
const index_t k_block_data_on_global = block_work_multi_id[0] * KPerBlock;
const index_t b_block_data_on_global = block_work_multi_id[1] * BPerBlock;
// input tensor
// tensor descriptor in device memory [N0, N1, N2, Ho0, Ho1, Ho2, Wo0, Wo1, Wo2]
......@@ -143,7 +149,7 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
.Extract(Sequence<1, 2, 3>{});
// merged tensor descriptor in device memory [E, N1, B, N2], src of blockwise copy
constexpr auto in_e_n0_ho0_wo0_b1_n2_ho2_wo2_global_merged_desc =
constexpr auto in_e_n0_ho0_wo0_b_n2_ho2_wo2_global_merged_desc =
make_ConstantMergedTensorDescriptor(
in_c_y_x_global_desc.Embed(in_n0_ho0_wo0_n1_ho1_wo1_n2_ho2_wo2_global_desc),
Sequence<0, 1, 2>{},
......@@ -157,8 +163,15 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
// memory layout descriptor in LDS [E, N1, B, N2], dst of blockwise copy
// be careful of LDS alignment
constexpr auto in_e_n0_ho0_wo0_b1_n2_ho2_wo2_block_desc =
in_e_n0_ho0_wo0_b1_n2_ho2_wo2_global_merged_desc.Pack();
constexpr auto in_e_n0_ho0_wo0_b_n2_ho2_wo2_block_desc =
make_ConstantTensorDescriptor_packed(Sequence<EPerBlock,
N0PerBlock,
Ho0PerBlock,
Wo0PerBlock,
BPerBlock,
N2,
Ho2,
Wo2>{});
// input blockwise copy
// slice a merged tensor, reorder and copy to a normal tensor
......@@ -166,17 +179,17 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
auto blockwise_in_copy = BlockwiseGenericTensorSliceCopy_v1<
BlockSize,
Float,
decltype(in_e_n0_ho0_wo0_b1_n2_ho2_wo2_global_merged_desc),
decltype(in_e_n0_ho0_wo0_b1_n2_ho2_wo2_block_desc),
decltype(in_e_n0_ho0_wo0_b1_n2_ho2_wo2_block_desc.GetLengths()),
InBlockCopySubLengths_E_N0_Ho0_Wo0_B1_N2_Ho2_Wo2,
InBlockCopyClusterLengths_E_N0_Ho0_Wo0_B1_N2_Ho2_Wo2,
decltype(in_e_n0_ho0_wo0_b_n2_ho2_wo2_global_merged_desc),
decltype(in_e_n0_ho0_wo0_b_n2_ho2_wo2_block_desc),
decltype(in_e_n0_ho0_wo0_b_n2_ho2_wo2_block_desc.GetLengths()),
InBlockCopySubLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2,
InBlockCopyClusterLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2,
InBlockCopyThreadClusterArrangeOrder,
InBlockCopySrcAccessOrder,
InBlockCopyDstAccessOrder,
InBlockCopyDataPerAccess_Wo2,
InBlockCopyDataPerAccess_Wo2>({0, 0, 0, 0, b1_block_data_on_global, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0});
InBlockCopyDataPerAccess_W2,
InBlockCopyDataPerAccess_W2>({0, 0, 0, 0, b_block_data_on_global, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0});
// weight tensor
// tensor descriptor in device memory, src of blockwise copy
......@@ -219,13 +232,13 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
// this check is ad-hoc
// TODO: need to properly implement tensor descriptor with multiple alignment
// requirements
static_assert(in_e_n0_ho0_wo0_b1_n2_ho2_wo2_block_desc.GetStrides()[3] % GemmDataPerReadB ==
static_assert(in_e_n0_ho0_wo0_b_n2_ho2_wo2_block_desc.GetStrides()[3] % GemmDataPerReadB ==
0,
"GemmDataPerReadB alignment requirement is not satisfied");
constexpr auto b_e_n0ho0wo0b1n2ho2wo2_block_mtx_desc =
constexpr auto b_e_n0ho0wo0bn2ho2wo2_block_mtx_desc =
make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(
in_e_n0_ho0_wo0_b1_n2_ho2_wo2_block_desc.Unfold(I1, I7));
in_e_n0_ho0_wo0_b_n2_ho2_wo2_block_desc.Unfold(I1, I7));
// sanity check
static_assert(KPerBlock % (GemmMPerThreadSubC * GemmMLevel0Cluster * GemmMLevel1Cluster) ==
......@@ -237,13 +250,15 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
// c_thread_mtx definition: this is a mess
// TODO:: more elegent way of defining c_thread_mtx
constexpr auto c_k0k2_n0ho0wo0n2ho2wo2_thread_mtx_desc = make_ConstantMatrixDescriptor(
Number<GemmMRepeat * GemmMPerThreadSubC>{}, Number<N0 * Ho0 * Wo0 * N2 * Ho2 * Wo2>{});
constexpr auto c_k0k2_n0ho0wo0n2ho2wo2_thread_mtx_desc =
make_ConstantMatrixDescriptor_packed(
Number<GemmMRepeat * GemmMPerThreadSubC>{},
Number<N0PerBlock * Ho0PerBlock * Wo0PerBlock * N2 * Ho2 * Wo2>{});
const auto blockwise_gemm = BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2<
BlockSize,
decltype(a_e_k_block_mtx_desc),
decltype(b_e_n0ho0wo0b1n2ho2wo2_block_mtx_desc),
decltype(b_e_n0ho0wo0bn2ho2wo2_block_mtx_desc),
decltype(c_k0k2_n0ho0wo0n2ho2wo2_thread_mtx_desc),
GemmMPerThreadSubC,
GemmNPerThreadSubC,
......@@ -256,13 +271,13 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
GemmDataPerReadB>{};
// LDS allocation for input and weight: be careful of alignment
constexpr index_t max_align = math::lcm(InBlockCopyDstDataPerWrite_N2,
constexpr index_t max_align = math::lcm(InBlockCopyDataPerAccess_W2,
WeiBlockCopyDstDataPerWrite_K,
GemmDataPerReadA,
GemmDataPerReadB);
constexpr index_t in_block_space = math::integer_least_multiple(
in_e_n0_ho0_wo0_b1_n2_ho2_wo2_block_desc.GetElementSpace(), max_align);
in_e_n0_ho0_wo0_b_n2_ho2_wo2_block_desc.GetElementSpace(), max_align);
constexpr index_t wei_block_space =
math::integer_least_multiple(wei_e_k_block_desc.GetElementSpace(), max_align);
......@@ -369,8 +384,18 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
// define tensor descriptor for threadwise copy
// output memory layout descriptor in register
constexpr auto out_k0_k1_k2_n0_ho0_wo0_n1_ho1_wo1_n2_ho2_wo2_thread_mem_desc =
make_ConstantTensorDescriptor_packed(
Sequence<KPerBlock / (K1 * K2), 1, K2, N0, ho0, wo0, 1, 1, 1, N2, Ho2, Wo2>{});
make_ConstantTensorDescriptor_packed(Sequence<KPerBlock / (K1 * K2),
1,
K2,
N0PerBlock,
Ho0PerBlock,
Wo0PerBlock,
1,
1,
1,
N2,
Ho2,
Wo2>{});
// output tensor descriptor in register, src of threadwise copy
constexpr auto out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_thread_desc =
......@@ -378,7 +403,7 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
Sequence<3, 6, 9, 0, 1, 2, 4, 7, 10, 5, 8, 11>{});
// output memory layout descriptor in device memory, dst of threadwise copy
constexpr auto out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_global_desc =
constexpr auto out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_global_mem_desc =
out_n_k_h_w_global_desc.Fold(I3, Sequence<Wo1, Wo2>{})
.Fold(I2, Sequence<Ho1, Ho2>{})
.Fold(I1, Sequence<K1, K2>{})
......@@ -393,33 +418,55 @@ struct GridwiseConvolutionImplicitGemm_v4r2_nchw_kcyx_nkhw_lds_double_buffer
k_block_data_on_global + c_thread_mtx_on_block.row;
const index_t b_thread_data_on_global =
b_block_data_on_global + c_thread_mtx_on_block.col / N2;
b_block_data_on_global + c_thread_mtx_on_block.col / (N2 * Ho2 * Wo2);
// output merged global tensor descriptor, for calculating origin of thread tensor
// in global memory
constexpr auto out_k_n1_b_n2_global_merged_desc = make_ConstantMergedTensorDescriptor(
out_n0_n1_n2_k0_k1_k2_h_w_global_mem_desc.Unfold(I3, I5),
Sequence<3>{},
Sequence<1>{},
Sequence<0, 4, 5>{},
Sequence<2>{});
constexpr auto out_k_n0_ho0_wo0_b_n2_ho2_wo2_global_merged_desc =
make_ConstantMergedTensorDescriptor(
out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_global_mem_desc.Unfold(I3, I5),
Sequence<3>{},
Sequence<0>{},
Sequence<4>{},
Sequence<7>{},
Sequence<1, 5, 8>{},
Sequence<2>{},
Sequence<6>{},
Sequence<9>{});
// origin of dst in device memory
Float* p_out_thread_on_global =
p_out_global +
out_k_n1_b_n2_global_merged_desc.GetOffsetFromMultiIndex(
k_thread_data_on_global, 0, b_thread_data_on_global, 0);
out_k_n0_ho0_wo0_b_n2_ho2_wo2_global_merged_desc.GetOffsetFromMultiIndex(
k_thread_data_on_global, 0, 0, 0, b_thread_data_on_global, 0, 0, 0);
#if 1
threadwise_generic_tensor_slice_copy_v1(
out_n0_n1_n2_k0_k1_k2_h_w_thread_desc,
out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_thread_desc,
p_out_thread,
{0, 0, 0, 0, 0, 0, 0, 0},
out_n0_n1_n2_k0_k1_k2_h_w_global_mem_desc,
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_global_mem_desc,
p_out_thread_on_global,
{0, 0, 0, 0, 0, 0, 0, 0},
out_n0_n1_n2_k0_k1_k2_h_w_thread_desc.GetLengths(),
arithmetic_sequence_gen<0, 8, 1>::type{},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_thread_desc.GetLengths(),
arithmetic_sequence_gen<0, 12, 1>::type{},
Number<1>{});
#else
if(get_thread_local_1d_id() == 0 && get_block_1d_id() == 0)
{
print_ConstantTensorDescriptor(
"out thread: ", out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_thread_desc);
printf("size: %d\n",
out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_thread_desc.GetElementSize());
for(index_t i = 0;
i < out_n0_n1_n2_k0_k1_k2_ho0_ho1_ho2_wo0_wo1_wo2_thread_desc.GetElementSize();
++i)
{
p_out_global[0] = p_out_thread[i];
}
}
#endif
}
}
};
......
composable_kernel/include/tensor_description/ConstantMatrixDescriptor.hpp
View file @ e87aa851
......@@ -52,11 +52,15 @@ __host__ __device__ constexpr auto
return ConstantMatrixDescriptor<NRow, NCol, RowStride>{};
}
template <index_t NRow, index_t NCol, index_t RowStride>
__host__ __device__ constexpr auto make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(
ConstantTensorDescriptor<Sequence<NRow, NCol>, Sequence<RowStride, 1>>)
template <class TDesc>
__host__ __device__ constexpr auto
make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(TDesc)
{
return ConstantMatrixDescriptor<NRow, NCol, RowStride>{};
static_assert(TDesc::GetNumOfDimension() == 2, "wrong");
static_assert(TDesc::GetStrides()[1] == 1, "wrong");
return ConstantMatrixDescriptor<TDesc::GetLengths()[0],
TDesc::GetLengths()[1],
TDesc::GetStrides()[0]>{};
}
template <class TDesc>
......
composable_kernel/include/tensor_description/ConstantTensorDescriptor.hpp
View file @ e87aa851
......@@ -456,7 +456,7 @@ print_ConstantTensorDescriptor(const char* s,
{
constexpr index_t ndim = sizeof...(Lengths);
static_assert(ndim > 0 && ndim <= 10, "wrong!");
static_assert(ndim > 0 && ndim <= 12, "wrong!");
static_if<ndim == 1>{}([&](auto) {
printf("%s dim %u, lengths {%u}, strides {%u}\n", s, ndim, Lengths..., Strides...);
......@@ -528,6 +528,26 @@ print_ConstantTensorDescriptor(const char* s,
Lengths...,
Strides...);
});
static_if<ndim == 11>{}([&](auto) {
printf("%s dim %u, lengths {%u %u %u %u %u %u %u %u %u %u %u}, strides {%u %u %u %u %u %u "
"%u %u "
"%u %u %u}\n",
s,
ndim,
Lengths...,
Strides...);
});
static_if<ndim == 12>{}([&](auto) {
printf("%s dim %u, lengths {%u %u %u %u %u %u %u %u %u %u %u %u}, strides {%u %u %u %u %u "
"%u %u %u %u "
"%u %u %u}\n",
s,
ndim,
Lengths...,
Strides...);
});
}
} // namespace ck
......
driver/include/device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw.hpp
View file @ e87aa851
......@@ -53,18 +53,27 @@ void device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw(InDesc,
wei_kcyx_device_buf.ToDevice(wei_kcyx.mData.data());
out_nkhw_device_buf.ToDevice(out_nkhw.mData.data());
#if 1
// 1x1 filter, 8x8 image
constexpr index_t N1 = 2;
constexpr index_t N2 = 4;
constexpr index_t N2 = 1;
constexpr index_t B = (N * Ho * Wo) / (N1 * N2);
constexpr index_t Ho1 = 8;
constexpr index_t Ho2 = 1;
constexpr index_t Wo1 = 1;
constexpr index_t Wo2 = 4;
#if 1
constexpr index_t BlockSize = 256;
constexpr index_t BPerBlock = 16;
constexpr index_t KPerBlock = 128;
constexpr index_t EPerBlock = 8;
constexpr index_t N0PerBlock = 1;
constexpr index_t Ho0PerBlock = 1;
constexpr index_t Wo0PerBlock = 2;
constexpr index_t GemmMPerThreadSubC = 4;
constexpr index_t GemmNPerThreadSubC = 4;
constexpr index_t GemmMLevel0Cluster = 4;
......@@ -75,14 +84,16 @@ void device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw(InDesc,
constexpr index_t GemmDataPerReadA = 4;
constexpr index_t GemmDataPerReadB = 4;
using InBlockCopySubLengths_E_N1_B_N2 = Sequence<1, 1, 1, 4>;
using InBlockCopyClusterLengths_E_N1_B_N2 = Sequence<8, 2, 16, 1>;
using InBlockCopyThreadClusterArrangeOrder = Sequence<0, 1, 3, 2>; // [E, N1, N2, B]
using InBlockCopySrcAccessOrder = Sequence<0, 1, 3, 2>; // [E, N1, N2, B]
using InBlockCopyDstAccessOrder = Sequence<0, 1, 2, 3>; // [E, N1, B, N2]
using InBlockCopySubLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2 = Sequence<1, 1, 1, 1, 1, 1, 1, 4>;
using InBlockCopyClusterLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2 = Sequence<8, 1, 1, 2, 16, 1, 1, 1>;
using InBlockCopyThreadClusterArrangeOrder =
Sequence<0, 1, 5, 2, 6, 3, 4, 7>; // [E, N0, N2, Ho0, Ho2, Wo0, B, Wo2]
using InBlockCopySrcAccessOrder =
Sequence<0, 1, 5, 2, 6, 3, 4, 7>; // [E, N0, N2, Ho0, Ho2, Wo0, B, Wo2]
using InBlockCopyDstAccessOrder =
Sequence<0, 1, 2, 3, 4, 5, 6, 7>; // [E, N0, Ho0, Wo0, B, N2, Ho2, Wo2]
constexpr index_t InBlockCopySrcDataPerRead_B = 1;
constexpr index_t InBlockCopyDstDataPerWrite_N2 = 4;
constexpr index_t InBlockCopyDataPerAccess_W2 = 4;
using WeiBlockCopySubLengths_E_K = Sequence<4, 1>;
using WeiBlockCopyClusterLengths_E_K = Sequence<2, 128>;
......@@ -94,6 +105,8 @@ void device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw(InDesc,
constexpr index_t WeiBlockCopyDstDataPerWrite_K = 1;
#endif
constexpr index_t B = N1 * Ho1 * Wo1;
constexpr index_t GridSize =
((B + BPerBlock - 1) / BPerBlock) * ((K + KPerBlock - 1) / KPerBlock);
......@@ -111,11 +124,18 @@ void device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw(InDesc,
decltype(out_nkhw_desc),
ConvStrides,
ConvDilations,
N1,
N2,
Ho1,
Ho2,
Wo1,
Wo2,
BPerBlock,
KPerBlock,
EPerBlock,
N1,
N2,
N0PerBlock,
Ho0PerBlock,
Wo0PerBlock,
GemmMPerThreadSubC,
GemmNPerThreadSubC,
GemmMLevel0Cluster,
......@@ -125,13 +145,12 @@ void device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw(InDesc,
GemmKPerThreadLoop,
GemmDataPerReadA,
GemmDataPerReadB,
InBlockCopySubLengths_E_N1_B_N2,
InBlockCopyClusterLengths_E_N1_B_N2,
InBlockCopySubLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2,
InBlockCopyClusterLengths_E_N0_Ho0_Wo0_B_N2_Ho2_Wo2,
InBlockCopyThreadClusterArrangeOrder,
InBlockCopySrcAccessOrder,
InBlockCopyDstAccessOrder,
InBlockCopySrcDataPerRead_B,
InBlockCopyDstDataPerWrite_N2,
InBlockCopyDataPerAccess_W2,
WeiBlockCopySubLengths_E_K,
WeiBlockCopyClusterLengths_E_K,
WeiBlockCopyThreadClusterArrangeOrder,
......
driver/src/driver.cpp
View file @ e87aa851
......@@ -14,7 +14,7 @@
#include "device_convolution_implicit_gemm_v2_chwn_cyxk_khwn.hpp"
#include "device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp"
#include "device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp"
//#include "device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw.hpp"
#include "device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw.hpp"
struct GeneratorTensor_1
{
......@@ -524,19 +524,29 @@ int main(int argc, char* argv[])
#elif 0
device_convolution_implicit_gemm_v2_chwn_cyxk_khwn
in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 1
#elif 0
device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(
in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 0
device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw(in_nchw_desc,
in_nchw,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
ConvStrides{},
ConvDilations{},
nrepeat);
device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw(in_nchw_desc,
in_nchw,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
ConvStrides{},
ConvDilations{},
nrepeat);
#elif 1
device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw(in_nchw_desc,
in_nchw,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
ConvStrides{},
ConvDilations{},
nrepeat);
#elif 0
device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded(in_nchw_desc,
in_nchw,
......
driver/src/driver.cu deleted 100644 → 0
View file @ ce4ec7dc
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include "config.hpp"
#include "ConstantTensorDescriptor.hpp"
#include "device.hpp"
#include "conv_common.hpp"
#include "host_conv.hpp"
#include "device_convolution_direct_v2_nchw_kcyx_nkhw.hpp"
#include "device_convolution_implicit_gemm_v1_chwn_cyxk_khwn.hpp"
#include "device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw.hpp"
#include "device_convolution_implicit_gemm_v2_chwn_cyxk_khwn.hpp"
#include "device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp"
#include "device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp"
//#include "device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw.hpp"
struct GeneratorTensor_1
{
template <class... Is>
double operator()(Is... is)
{
return 1;
}
};
struct GeneratorTensor_2
{
int min_value = 0;
int max_value = 1;
template <class... Is>
double operator()(Is...)
{
return (std::rand() % (max_value - min_value)) + min_value;
}
};
struct GeneratorTensor_3
{
template <class... Is>
double operator()(Is... is)
{
std::array<index_t, sizeof...(Is)> dims = {{static_cast<index_t>(is)...}};
auto f_acc = [](auto a, auto b) { return 100 * a + b; };
return std::accumulate(dims.begin(), dims.end(), index_t(0), f_acc);
}
};
struct GeneratorTensor_Checkboard
{
template <class... Ts>
double operator()(Ts... Xs) const
{
std::array<index_t, sizeof...(Ts)> dims = {{Xs...}};
return std::accumulate(dims.begin(),
dims.end(),
true,
[](bool init, index_t x) -> int { return init != (x % 2); })
? 1
: -1;
}
};
int main(int argc, char* argv[])
{
using namespace ck;
#if 0
constexpr index_t N = 8;
constexpr index_t C = 16;
constexpr index_t HI = 3;
constexpr index_t WI = 18;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3, 34x34
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 34;
constexpr index_t WI = 34;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
using ConvStrides = Sequence<2, 2>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3, 56x56
constexpr index_t N = 64;
constexpr index_t C = 64;
constexpr index_t HI = 56;
constexpr index_t WI = 56;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3 filter, 28x28 image
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 28x28 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 512;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3 filter, 20x84 image, 1x1 padding
constexpr index_t N = 16;
constexpr index_t C = 256;
constexpr index_t HI = 20;
constexpr index_t WI = 84;
constexpr index_t K = 256;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 1;
constexpr index_t WPad = 1;
#elif 0
// 3x3 filter, 112x112 image, 1x1 padding
constexpr index_t N = 16;
constexpr index_t C = 64;
constexpr index_t HI = 112;
constexpr index_t WI = 112;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 1;
constexpr index_t WPad = 1;
#elif 0
// 5x5 filter, 20x86 image
constexpr index_t N = 16;
constexpr index_t C = 256;
constexpr index_t HI = 20;
constexpr index_t WI = 86;
constexpr index_t K = 512;
constexpr index_t Y = 5;
constexpr index_t X = 5;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 5x5 filter, 20x86 image, 1x1 padding
constexpr index_t N = 16;
constexpr index_t C = 256;
constexpr index_t HI = 20;
constexpr index_t WI = 86;
constexpr index_t K = 512;
constexpr index_t Y = 5;
constexpr index_t X = 5;
constexpr index_t HPad = 1;
constexpr index_t WPad = 1;
#elif 0
// 5x5 filter, 28x28 image, 2x2 padding
constexpr index_t N = 16;
constexpr index_t C = 192;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 32;
constexpr index_t Y = 5;
constexpr index_t X = 5;
constexpr index_t HPad = 2;
constexpr index_t WPad = 2;
#elif 0
// 3x3 filter, 14x14 image
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 14x14 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 512;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 7x7 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 2048;
constexpr index_t Y = 1;
constexpr index_t X = 1;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 73x73 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 73;
constexpr index_t WI = 73;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 8x8 image
// cudnn@V100 68%, ck@V100 72%, ck@P100 52%, ck@VII 42%
constexpr index_t N = 64;
constexpr index_t C = 1536;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 256;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 8x8 image
// cudnn@V100 77%, ck@V100 76%, ck@P100 79%, ck@VII 51%
constexpr index_t N = 128;
constexpr index_t C = 2048;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 7x7 image
// cudnn@V100 82%, ck@V100 76%, ck@P100 67%, ck@VII 64%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 8x8 image
// cudnn@V100 83%, ck@V100 75%, ck@P100 78%, ck@VII 65%
constexpr index_t N = 128;
constexpr index_t C = 1280;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 14x14 image
// cudnn@V100 62%, ck@V100 68%, ck@P100 70%, ck@VII 50%
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 8x8 image
// cudnn@V100 74%, ck@V100 57%, ck@P100 78%, ck@VII 61%
constexpr index_t N = 64;
constexpr index_t C = 1536;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 28x28 image
// cudnn@V100 86%, ck@V100 84%, ck@P100 80%, ck@VII 69%
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 7x7 image
// cudnn@V100 71%, ck@V100 55%, ck@P100 70%, ck@VII 62%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 256;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3 filter, 2x2 stride, 35x35 input, 17x17 output
// cudnn@V100 90%, ck@V100 93%, ck@P100 83%, ck@VII 81%
constexpr index_t N = 128;
constexpr index_t C = 288;
constexpr index_t HI = 35;
constexpr index_t WI = 35;
constexpr index_t K = 384;
constexpr index_t Y = 3;
constexpr index_t X = 3;
using ConvStrides = Sequence<2, 2>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 17x17 input
// cudnn@V100 81%, ck@V100 76%, ck@P100 70%, ck@VII 76%
constexpr index_t N = 128;
constexpr index_t C = 768;
constexpr index_t HI = 17;
constexpr index_t WI = 17;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 14x14 image
// cudnn@V100 73%, ck@V100 71%, ck@P100 70%, ck@VII 64%
constexpr index_t N = 128;
constexpr index_t C = 528;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 14x14 image
// cudnn@V100 73%, ck@V100 72%, ck@P100 79%, ck@VII 75%
constexpr index_t N = 128;
constexpr index_t C = 528;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 256;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 7x7 image
// cudnn@V100 49%, ck@V100 50%, ck@P100 61%, ck@VII 52%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#endif
auto lower_pads = Sequence<HPad, WPad>{};
auto upper_pads = Sequence<HPad, WPad>{};
auto in_nchw_desc = make_ConstantTensorDescriptor_packed(Sequence<N, C, HI, WI>{});
auto wei_kcyx_desc = make_ConstantTensorDescriptor_packed(Sequence<K, C, Y, X>{});
auto out_nkhw_desc = get_convolution_with_padding_output_default_4d_tensor_descriptor(
in_nchw_desc, wei_kcyx_desc, ConvStrides{}, ConvDilations{}, lower_pads, upper_pads);
ostream_ConstantTensorDescriptor(in_nchw_desc, std::cout << "in_nchw_desc: ");
ostream_ConstantTensorDescriptor(wei_kcyx_desc, std::cout << "wei_kcyx_desc: ");
ostream_ConstantTensorDescriptor(out_nkhw_desc, std::cout << "out_nkhw_desc: ");
using in_data_t = float;
using out_data_t = float;
Tensor<in_data_t> in_nchw(make_TensorDescriptor(in_nchw_desc));
Tensor<in_data_t> wei_kcyx(make_TensorDescriptor(wei_kcyx_desc));
Tensor<out_data_t> out_nkhw_host(make_TensorDescriptor(out_nkhw_desc));
Tensor<out_data_t> out_nkhw_device(make_TensorDescriptor(out_nkhw_desc));
std::size_t num_thread = std::thread::hardware_concurrency();
if(argc != 3)
{
printf("arg1: do_verification, arg2: nrepeat\n");
exit(1);
}
bool do_verification = atoi(argv[1]);
index_t nrepeat = atoi(argv[2]);
if(do_verification)
{
#if 0
in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#elif 0
in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
#elif 0
in_nchw.GenerateTensorValue(GeneratorTensor_3{}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#elif 1
in_nchw.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
#elif 0
in_nchw.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
auto gen_wei = [](auto... is) {
return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
};
wei_kcyx.GenerateTensorValue(gen_wei, num_thread);
#endif
}
#if 0
device_convolution_direct_v2_nchw_kcyx_nkhw
in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 0
device_convolution_implicit_gemm_v1_chwn_cyxk_khwn
in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 0
device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw
in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 0
device_convolution_implicit_gemm_v2_chwn_cyxk_khwn
in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 1
device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(
in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 0
device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw(in_nchw_desc,
in_nchw,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
ConvStrides{},
ConvDilations{},
nrepeat);
#elif 0
device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded(in_nchw_desc,
in_nchw,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
lower_pads,
upper_pads,
nrepeat);
#endif
if(do_verification)
{
#if 1
if(Y == 3 && X == 3 && ConvStrides{}[0] == 1 && ConvStrides{}[1] == 1 &&
ConvDilations{}[0] == 1 && ConvDilations{}[1] == 1)
{
host_winograd_3x3_convolution(in_nchw, wei_kcyx, out_nkhw_host, lower_pads, upper_pads);
}
else
#endif
{
host_direct_convolution(in_nchw,
wei_kcyx,
out_nkhw_host,
ConvStrides{},
ConvDilations{},
lower_pads,
upper_pads);
}
check_error(out_nkhw_host, out_nkhw_device);
#if 0
LogRange(std::cout << "in_nchw : ", in_nchw.mData, ",") << std::endl;
LogRange(std::cout << "wei_kcyx: ", wei_kcyx.mData, ",") << std::endl;
LogRange(std::cout << "out_nkhw_host : ", out_nkhw_host.mData, ",") << std::endl;
LogRange(std::cout << "out_nkhw_device: ", out_nkhw_device.mData, ",") << std::endl;
#endif
}
}
driver/src/driver.cu 0 → 120000
View file @ e87aa851
driver.cpp
\ No newline at end of file
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