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Commit 96d73c21 authored by Chao Liu's avatar Chao Liu
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Merge remote-tracking branch 'origin/build_0705' into implicit_gemm_v4r2

parents 8133713e 3276a5e9
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Showing with 1588 additions and 434 deletions
composable_kernel/include/kernel_algorithm/gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw_lds_double_buffer.hpp
View file @ 96d73c21
...@@ -191,7 +191,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer ...@@ -191,7 +191,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
// c_thread_mtx definition: this is a mess // c_thread_mtx definition: this is a mess
// TODO:: more elegent way of defining c_thread_mtx // 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>{}); Number<GemmMRepeat * GemmMPerThreadSubC>{}, Number<N1 * N2>{});
const auto blockwise_gemm = BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2< const auto blockwise_gemm = BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2<
...@@ -209,15 +209,6 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer ...@@ -209,15 +209,6 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
GemmDataPerReadA, GemmDataPerReadA,
GemmDataPerReadB>{}; GemmDataPerReadB>{};
// choose GEMM implementation here
const auto run_blockwise_gemm = [&](auto... Xs) {
#if 1
return blockwise_gemm.Run(Xs...);
#else
return blockwise_gemm.Run_amd_asm(Xs...);
#endif
};
// LDS allocation for input and weight: be careful of alignment // 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(InBlockCopyDstDataPerWrite_N2,
WeiBlockCopyDataPerAccess_K, WeiBlockCopyDataPerAccess_K,
...@@ -225,9 +216,10 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer ...@@ -225,9 +216,10 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
GemmDataPerReadB); GemmDataPerReadB);
constexpr index_t in_block_space = constexpr index_t in_block_space =
in_c_n1_b_n2_block_mem_desc.GetElementSpace(Number<max_align>{}); math::integer_least_multiple(in_c_n1_b_n2_block_mem_desc.GetElementSpace(), max_align);
constexpr index_t wei_block_space = wei_c_k_block_desc.GetElementSpace(Number<max_align>{}); constexpr index_t wei_block_space =
math::integer_least_multiple(wei_c_k_block_desc.GetElementSpace(), max_align);
__shared__ Float p_in_block_double[2 * in_block_space]; __shared__ Float p_in_block_double[2 * in_block_space];
__shared__ Float p_wei_block_double[2 * wei_block_space]; __shared__ Float p_wei_block_double[2 * wei_block_space];
...@@ -291,7 +283,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer ...@@ -291,7 +283,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
p_wei_register_clipboard); p_wei_register_clipboard);
// LDS double buffer: GEMM on current data // LDS double buffer: GEMM on current data
run_blockwise_gemm(p_wei_block_now, p_in_block_now, p_out_thread); blockwise_gemm.Run(p_wei_block_now, p_in_block_now, p_out_thread);
// LDS double buffer: store next data to LDS // LDS double buffer: store next data to LDS
blockwise_in_copy.RunStoreRegisterClipboard(p_in_register_clipboard, blockwise_in_copy.RunStoreRegisterClipboard(p_in_register_clipboard,
...@@ -319,7 +311,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer ...@@ -319,7 +311,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
p_wei_register_clipboard); p_wei_register_clipboard);
// LDS double buffer: GEMM on current data // LDS double buffer: GEMM on current data
run_blockwise_gemm(p_wei_block_double, p_in_block_double, p_out_thread); blockwise_gemm.Run(p_wei_block_double, p_in_block_double, p_out_thread);
// LDS double buffer: store next data to LDS // LDS double buffer: store next data to LDS
blockwise_in_copy.RunStoreRegisterClipboard(p_in_register_clipboard, blockwise_in_copy.RunStoreRegisterClipboard(p_in_register_clipboard,
...@@ -331,7 +323,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer ...@@ -331,7 +323,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
__syncthreads(); __syncthreads();
// LDS double buffer: GEMM on current data // LDS double buffer: GEMM on current data
run_blockwise_gemm(p_wei_block_double + wei_block_space, blockwise_gemm.Run(p_wei_block_double + wei_block_space,
p_in_block_double + in_block_space, p_in_block_double + in_block_space,
p_out_thread); p_out_thread);
} }
......
composable_kernel/include/tensor_description/ConstantMatrixDescriptor.hpp
View file @ 96d73c21
...@@ -53,8 +53,8 @@ __host__ __device__ constexpr auto ...@@ -53,8 +53,8 @@ __host__ __device__ constexpr auto
} }
template <index_t NRow, index_t NCol, index_t RowStride> template <index_t NRow, index_t NCol, index_t RowStride>
__host__ __device__ constexpr auto __host__ __device__ constexpr auto make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(
make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(ConstantTensorDescriptor<Sequence<NRow, NCol>, Sequence<RowStride, 1>> ConstantTensorDescriptor<Sequence<NRow, NCol>, Sequence<RowStride, 1>>)
{ {
return ConstantMatrixDescriptor<NRow, NCol, RowStride>{}; return ConstantMatrixDescriptor<NRow, NCol, RowStride>{};
} }
......
composable_kernel/include/tensor_description/ConstantMergedTensorDescriptor.hpp
View file @ 96d73c21
...@@ -177,8 +177,9 @@ struct ConstantMergedTensorDescriptor ...@@ -177,8 +177,9 @@ struct ConstantMergedTensorDescriptor
__host__ __device__ static constexpr auto Pack() __host__ __device__ static constexpr auto Pack()
{ {
using Strides = decltype(calculate_tensor_strides_packed(GetLengths())); constexpr auto lengths = GetLengths();
return ConstantTensorDescriptor<Lengths, Strides>{}; constexpr auto strides = calculate_tensor_strides_packed(lengths);
return ConstantTensorDescriptor<decltype(lengths), decltype(strides)>{};
} }
}; };
......
composable_kernel/include/tensor_description/ConstantTensorDescriptor.hpp
View file @ 96d73c21
...@@ -374,7 +374,7 @@ struct ConstantTensorDescriptor ...@@ -374,7 +374,7 @@ struct ConstantTensorDescriptor
template <index_t IDim, index_t... FoldIntervals> template <index_t IDim, index_t... FoldIntervals>
__host__ __device__ static constexpr auto Fold(Number<IDim>, Sequence<FoldIntervals...>) __host__ __device__ static constexpr auto Fold(Number<IDim>, Sequence<FoldIntervals...>)
{ {
return Fold(Number<IDim>{}, Number<FoldInterVals>{}...); return Fold(Number<IDim>{}, Number<FoldIntervals>{}...);
} }
// this function unfold dimension [FirstUnfoldDim, ..., LastUnfoldDim] into 1 dimension // this function unfold dimension [FirstUnfoldDim, ..., LastUnfoldDim] into 1 dimension
...@@ -417,8 +417,8 @@ struct ConstantTensorDescriptor ...@@ -417,8 +417,8 @@ struct ConstantTensorDescriptor
__host__ __device__ static constexpr auto Pack() __host__ __device__ static constexpr auto Pack()
{ {
using Strides = decltype(calculate_tensor_strides_packed(Lengths{})); using packed_strides = decltype(calculate_tensor_strides_packed(Lengths{}));
return ConstantTensorDescriptor<Lengths, Strides>{}; return ConstantTensorDescriptor<Lengths, packed_strides>{};
} }
template <class MapNew2Old> template <class MapNew2Old>
......
composable_kernel/include/tensor_operation/blockwise_gemm.hpp
View file @ 96d73c21
...@@ -205,10 +205,10 @@ struct BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2 ...@@ -205,10 +205,10 @@ struct BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2
// thread A, B for GEMM // thread A, B for GEMM
constexpr auto a_thread_mtx = constexpr auto a_thread_mtx =
make_ConstantMatrixDescriptor(Number<KPerThreadLoop>{}, Number<MPerThread>{}); make_ConstantMatrixDescriptor_packed(Number<KPerThreadLoop>{}, Number<MPerThread>{});
constexpr auto b_thread_mtx = constexpr auto b_thread_mtx =
make_ConstantMatrixDescriptor(Number<KPerThreadLoop>{}, Number<NPerThread>{}); make_ConstantMatrixDescriptor_packed(Number<KPerThreadLoop>{}, Number<NPerThread>{});
// thread A-sub, B-sub for copy // thread A-sub, B-sub for copy
constexpr auto a_thread_sub_mtx = make_ConstantMatrixDescriptor( constexpr auto a_thread_sub_mtx = make_ConstantMatrixDescriptor(
......
composable_kernel/include/utility/config_amd.hpp.in
View file @ 96d73c21
...@@ -18,7 +18,8 @@ typedef float float4_t __attribute__((ext_vector_type(4))); ...@@ -18,7 +18,8 @@ typedef float float4_t __attribute__((ext_vector_type(4)));
using index_t = uint32_t; using index_t = uint32_t;
__device__ void fused_multiply_accumulate(float& d, const float& s0, const float& s1) template <class T>
__device__ void fused_multiply_accumulate(T& d, const T& s0, const T& s1)
{ {
d += s0 * s1; d += s0 * s1;
} }
......
composable_kernel/include/utility/config_nvidia.hpp.in
View file @ 96d73c21
...@@ -22,7 +22,8 @@ using float4_t = float4; ...@@ -22,7 +22,8 @@ using float4_t = float4;
using index_t = uint32_t; using index_t = uint32_t;
__device__ void fused_multiply_accumulate(float& d, const float& s0, const float& s1) template <class T>
__device__ void fused_multiply_accumulate(T& d, const T& s0, const T& s1)
{ {
d += s0 * s1; d += s0 * s1;
} }
......
composable_kernel/include/utility/integral_constant.hpp
View file @ 96d73c21
...@@ -59,18 +59,5 @@ __host__ __device__ constexpr auto operator%(Number<X>, Number<Y>) ...@@ -59,18 +59,5 @@ __host__ __device__ constexpr auto operator%(Number<X>, Number<Y>)
return Number<X % Y>{}; return Number<X % Y>{};
} }
#if 0
static constexpr Number<0> 0_c;
static constexpr Number<1> 1_c;
static constexpr Number<2> 2_c;
static constexpr Number<3> 3_c;
static constexpr Number<4> 4_c;
static constexpr Number<5> 5_c;
static constexpr Number<6> 6_c;
static constexpr Number<7> 7_c;
static constexpr Number<8> 8_c;
static constexpr Number<9> 9_c;
#endif
} // namespace ck } // namespace ck
#endif #endif
driver/CMakeLists.txt
View file @ 96d73c21
...@@ -3,15 +3,15 @@ set(TENSOR_SOURCE ...@@ -3,15 +3,15 @@ set(TENSOR_SOURCE
src/device.cpp; src/device.cpp;
) )
add_library(tensor SHARED ${TENSOR_SOURCE}) add_library(host SHARED ${TENSOR_SOURCE})
target_compile_features(tensor PUBLIC) target_compile_features(host PUBLIC)
set_target_properties(tensor PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(host PROPERTIES POSITION_INDEPENDENT_CODE ON)
if(DEVICE_BACKEND STREQUAL "NVIDIA") if(DEVICE_BACKEND STREQUAL "NVIDIA")
target_link_libraries(tensor nvToolsExt cudart) target_link_libraries(host nvToolsExt cudart)
endif() endif()
install(TARGETS tensor LIBRARY DESTINATION lib) install(TARGETS host LIBRARY DESTINATION lib)
if(DEVICE_BACKEND STREQUAL "AMD") if(DEVICE_BACKEND STREQUAL "AMD")
...@@ -21,4 +21,4 @@ elseif(DEVICE_BACKEND STREQUAL "NVIDIA") ...@@ -21,4 +21,4 @@ elseif(DEVICE_BACKEND STREQUAL "NVIDIA")
endif() endif()
add_executable(driver ${DRIVER_SOURCE}) add_executable(driver ${DRIVER_SOURCE})
target_link_libraries(driver PRIVATE tensor) target_link_libraries(driver PRIVATE host)
driver/include/conv_common.hpp
View file @ 96d73c21
#ifndef CK_CONV_COMMON_HPP #ifndef CONV_COMMON_HPP
#define CK_CONV_COMMON_HPP #define CONV_COMMON_HPP
#include "ConstantTensorDescriptor.hpp" #include "ConstantTensorDescriptor.hpp"
using namespace ck;
// this is ugly, only for 4d // this is ugly, only for 4d
template <class InDesc, class WeiDesc> template <class InDesc, class WeiDesc>
constexpr auto get_convolution_output_default_4d_tensor_descriptor(InDesc, WeiDesc) constexpr auto get_convolution_output_default_4d_tensor_descriptor(InDesc, WeiDesc)
{ {
using namespace ck;
constexpr auto in_desc = InDesc{}; constexpr auto in_desc = InDesc{};
constexpr auto wei_desc = WeiDesc{}; constexpr auto wei_desc = WeiDesc{};
...@@ -45,6 +45,8 @@ template <class InDesc, ...@@ -45,6 +45,8 @@ template <class InDesc,
constexpr auto get_convolution_with_padding_output_default_4d_tensor_descriptor( constexpr auto get_convolution_with_padding_output_default_4d_tensor_descriptor(
InDesc, WeiDesc, ConvStrides, ConvDilations, LowerPads, UpperPads) InDesc, WeiDesc, ConvStrides, ConvDilations, LowerPads, UpperPads)
{ {
using namespace ck;
constexpr auto in_desc = InDesc{}; constexpr auto in_desc = InDesc{};
constexpr auto wei_desc = WeiDesc{}; constexpr auto wei_desc = WeiDesc{};
...@@ -84,6 +86,8 @@ constexpr auto get_convolution_with_padding_output_default_4d_tensor_descriptor( ...@@ -84,6 +86,8 @@ constexpr auto get_convolution_with_padding_output_default_4d_tensor_descriptor(
template <class InDesc, class WeiDesc, class OutDesc> template <class InDesc, class WeiDesc, class OutDesc>
constexpr std::size_t calculate_convolution_flops(InDesc, WeiDesc, OutDesc) constexpr std::size_t calculate_convolution_flops(InDesc, WeiDesc, OutDesc)
{ {
using namespace ck;
constexpr auto wei_desc = WeiDesc{}; constexpr auto wei_desc = WeiDesc{};
constexpr auto out_desc = OutDesc{}; constexpr auto out_desc = OutDesc{};
...@@ -107,6 +111,8 @@ constexpr std::size_t calculate_convolution_flops(InDesc, WeiDesc, OutDesc) ...@@ -107,6 +111,8 @@ constexpr std::size_t calculate_convolution_flops(InDesc, WeiDesc, OutDesc)
template <class Float, class InDesc, class WeiDesc, class OutDesc> template <class Float, class InDesc, class WeiDesc, class OutDesc>
constexpr std::size_t calculate_convolution_memory_size(Float, InDesc, WeiDesc, OutDesc) constexpr std::size_t calculate_convolution_memory_size(Float, InDesc, WeiDesc, OutDesc)
{ {
using namespace ck;
constexpr auto wei_desc = WeiDesc{}; constexpr auto wei_desc = WeiDesc{};
constexpr auto out_desc = OutDesc{}; constexpr auto out_desc = OutDesc{};
......
driver/include/device.hpp
View file @ 96d73c21
#ifndef CK_DEVICE_HPP #ifndef DEVICE_HPP
#define CK_DEVICE_HPP #define DEVICE_HPP
#include <memory> #include <memory>
#include "config.hpp" #include "config.hpp"
using namespace ck;
struct DeviceMem struct DeviceMem
{ {
DeviceMem() = delete; DeviceMem() = delete;
......
driver/include/device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp
View file @ 96d73c21
...@@ -6,8 +6,6 @@ ...@@ -6,8 +6,6 @@
#include "gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp" #include "gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp"
#include "gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw_lds_double_buffer.hpp" #include "gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw_lds_double_buffer.hpp"
using namespace ck;
template <class T, class InDesc, class WeiDesc, class OutDesc> template <class T, class InDesc, class WeiDesc, class OutDesc>
void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc, void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
const Tensor<T>& in_nchw, const Tensor<T>& in_nchw,
...@@ -17,6 +15,8 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc, ...@@ -17,6 +15,8 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
Tensor<T>& out_nkhw, Tensor<T>& out_nkhw,
index_t nrepeat) index_t nrepeat)
{ {
using namespace ck;
constexpr auto I0 = Number<0>{}; constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{}; constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{}; constexpr auto I2 = Number<2>{};
...@@ -135,7 +135,6 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc, ...@@ -135,7 +135,6 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
WeiBlockCopyClusterLengths_C_K, WeiBlockCopyClusterLengths_C_K,
WeiBlockCopyDataPerAccess_K>{}; WeiBlockCopyDataPerAccess_K>{};
#if 1
float time = launch_kernel(run_gridwise_convolution_kernel<decltype(gridwise_conv), T>, float time = launch_kernel(run_gridwise_convolution_kernel<decltype(gridwise_conv), T>,
dim3(GridSize), dim3(GridSize),
dim3(BlockSize), dim3(BlockSize),
...@@ -149,7 +148,6 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc, ...@@ -149,7 +148,6 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
(float)calculate_convolution_flops(InDesc{}, WeiDesc{}, OutDesc{}) / (float)calculate_convolution_flops(InDesc{}, WeiDesc{}, OutDesc{}) /
(std::size_t(1000) * 1000 * 1000) / time); (std::size_t(1000) * 1000 * 1000) / time);
usleep(std::min(time * 1000, float(10000))); usleep(std::min(time * 1000, float(10000)));
#endif
} }
out_nkhw_device_buf.FromDevice(out_nkhw.mData.data()); out_nkhw_device_buf.FromDevice(out_nkhw.mData.data());
......
driver/include/device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp
View file @ 96d73c21
...@@ -6,8 +6,6 @@ ...@@ -6,8 +6,6 @@
#include "gridwise_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp" #include "gridwise_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp"
#include "gridwise_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw_lds_double_buffer.hpp" #include "gridwise_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw_lds_double_buffer.hpp"
using namespace ck;
template <class T, template <class T,
class InDesc, class InDesc,
class WeiDesc, class WeiDesc,
...@@ -24,6 +22,8 @@ void device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw(InDesc, ...@@ -24,6 +22,8 @@ void device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw(InDesc,
ConvDilations, ConvDilations,
index_t nrepeat) index_t nrepeat)
{ {
using namespace ck;
constexpr auto I0 = Number<0>{}; constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{}; constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{}; constexpr auto I2 = Number<2>{};
......
driver/include/host_conv.hpp 0 → 100644
View file @ 96d73c21
#pragma once
#include "tensor.hpp"
#include "common_header.hpp"
#include "ConstantTensorDescriptor.hpp"
// this is ugly, only for 4d
template <class TConstTensorDesc>
void ostream_ConstantTensorDescriptor(TConstTensorDesc, std::ostream& os = std::cout)
{
using namespace ck;
static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto desc = TConstTensorDesc{};
os << "Lengths: {" << desc.GetLength(I0) << ", " << desc.GetLength(I1) << ", "
<< desc.GetLength(I2) << ", " << desc.GetLength(I3) << "}, "
<< "Strides: {" << desc.GetStride(I0) << ", " << desc.GetStride(I1) << ", "
<< desc.GetStride(I2) << ", " << desc.GetStride(I3) << "}" << std::endl;
}
// this is ugly, only for 4d
template <class TConstTensorDesc>
auto make_TensorDescriptor(TConstTensorDesc)
{
using namespace ck;
static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto desc = TConstTensorDesc{};
std::initializer_list<index_t> lengths = {
desc.GetLength(I0), desc.GetLength(I1), desc.GetLength(I2), desc.GetLength(I3)};
std::initializer_list<index_t> strides = {
desc.GetStride(I0), desc.GetStride(I1), desc.GetStride(I2), desc.GetStride(I3)};
return TensorDescriptor(lengths, strides);
}
template <class TIn,
class TWei,
class TOut,
class ConvStrides,
class ConvDilations,
class LowerPads,
class UpperPads>
void host_direct_convolution(const Tensor<TIn>& in_nchw,
const Tensor<TWei>& wei_kcyx,
Tensor<TOut>& out_nkhw,
ConvStrides,
ConvDilations,
LowerPads,
UpperPads)
{
using namespace ck;
index_t h_pad_low = LowerPads{}.Get(Number<0>{});
index_t w_pad_low = LowerPads{}.Get(Number<1>{});
index_t h_pad_up = UpperPads{}.Get(Number<0>{});
index_t w_pad_up = UpperPads{}.Get(Number<1>{});
auto f = [&](auto n, auto k, auto ho, auto wo) {
double v = 0;
for(int c = 0; c < wei_kcyx.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei_kcyx.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * ConvStrides{}[0] + y * ConvDilations{}[0] - h_pad_low;
for(int x = 0; x < wei_kcyx.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * ConvStrides{}[1] + x * ConvDilations{}[1] - w_pad_low;
if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in_nchw.mDesc.GetLengths()[3])
{
v += double(in_nchw(n, c, hi, wi)) * double(wei_kcyx(k, c, y, x));
}
}
}
}
out_nkhw(n, k, ho, wo) = v;
};
auto f_par = make_ParallelTensorFunctor(f,
out_nkhw.mDesc.GetLengths()[0],
out_nkhw.mDesc.GetLengths()[1],
out_nkhw.mDesc.GetLengths()[2],
out_nkhw.mDesc.GetLengths()[3]);
f_par(std::thread::hardware_concurrency());
}
template <class TIn, class TWei, class TOut, class LowerPads, class UpperPads>
void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
const Tensor<TWei>& wei_kcyx,
Tensor<TOut>& out_nkhw,
LowerPads,
UpperPads)
{
using namespace ck;
constexpr std::size_t HoPerTile = 2;
constexpr std::size_t WoPerTile = 2;
std::size_t N = in_nchw.mDesc.GetLengths()[0];
std::size_t C = in_nchw.mDesc.GetLengths()[1];
std::size_t HI = in_nchw.mDesc.GetLengths()[2];
std::size_t WI = in_nchw.mDesc.GetLengths()[3];
std::size_t K = wei_kcyx.mDesc.GetLengths()[0];
std::size_t Y = wei_kcyx.mDesc.GetLengths()[2];
std::size_t X = wei_kcyx.mDesc.GetLengths()[3];
std::size_t HO = out_nkhw.mDesc.GetLengths()[2];
std::size_t WO = out_nkhw.mDesc.GetLengths()[3];
index_t h_pad_low = LowerPads{}.Get(Number<0>{});
index_t w_pad_low = LowerPads{}.Get(Number<1>{});
index_t h_pad_up = UpperPads{}.Get(Number<0>{});
index_t w_pad_up = UpperPads{}.Get(Number<1>{});
std::size_t HiPerTile = HoPerTile + Y - 1;
std::size_t WiPerTile = WoPerTile + X - 1;
std::size_t HTile = (HO + HoPerTile - 1) / HoPerTile;
std::size_t WTile = (WO + WoPerTile - 1) / WoPerTile;
Tensor<double> in_hold({N, C, HTile, WTile, HiPerTile, WiPerTile});
Tensor<double> in_transform({N, C, HTile, WTile, HiPerTile, WiPerTile});
Tensor<double> wei_transform({K, C, HiPerTile, WiPerTile});
Tensor<double> out_transform({N, K, HTile, WTile, HiPerTile, HiPerTile});
Tensor<double> out_hold({N, K, HTile, WTile, HoPerTile, WoPerTile});
auto f_in_hold = [&](auto n, auto c, auto htile, auto wtile) {
for(int j = 0; j < HiPerTile; ++j)
{
int hi = HoPerTile * htile + j - h_pad_low;
for(int i = 0; i < WiPerTile; ++i)
{
int wi = WoPerTile * wtile + i - w_pad_low;
if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in_nchw.mDesc.GetLengths()[3])
{
in_hold(n, c, htile, wtile, j, i) = in_nchw(n, c, hi, wi);
}
else
{
in_hold(n, c, htile, wtile, j, i) = TIn(0);
}
}
}
};
auto f_in_transform = [&](auto n, auto c, auto htile, auto wtile) {
in_transform(n, c, htile, wtile, 0, 0) =
in_hold(n, c, htile, wtile, 0, 0) - in_hold(n, c, htile, wtile, 0, 2) -
in_hold(n, c, htile, wtile, 2, 0) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 0, 1) =
in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) -
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 0, 2) =
-in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) +
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 0, 3) =
in_hold(n, c, htile, wtile, 0, 1) - in_hold(n, c, htile, wtile, 0, 3) -
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 3);
in_transform(n, c, htile, wtile, 1, 0) =
in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 1, 1) =
in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 1, 2) =
-in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 1, 3) =
in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) +
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
in_transform(n, c, htile, wtile, 2, 0) =
-in_hold(n, c, htile, wtile, 1, 0) + in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 2, 1) =
-in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 2, 2) =
in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 2, 3) =
-in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 3) +
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
in_transform(n, c, htile, wtile, 3, 0) =
in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 3, 0) + in_hold(n, c, htile, wtile, 3, 2);
in_transform(n, c, htile, wtile, 3, 1) =
in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
in_transform(n, c, htile, wtile, 3, 2) =
-in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
in_transform(n, c, htile, wtile, 3, 3) =
in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) -
in_hold(n, c, htile, wtile, 3, 1) + in_hold(n, c, htile, wtile, 3, 3);
};
auto f_wei_transform = [&](auto k, auto c) {
wei_transform(k, c, 0, 0) = double(wei_kcyx(k, c, 0, 0));
wei_transform(k, c, 0, 1) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
0.5 * double(wei_kcyx(k, c, 0, 1)) +
0.5 * double(wei_kcyx(k, c, 0, 2));
wei_transform(k, c, 0, 2) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
0.5 * double(wei_kcyx(k, c, 0, 1)) +
0.5 * double(wei_kcyx(k, c, 0, 2));
wei_transform(k, c, 0, 3) = double(wei_kcyx(k, c, 0, 2));
wei_transform(k, c, 1, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
0.5 * double(wei_kcyx(k, c, 1, 0)) +
0.5 * double(wei_kcyx(k, c, 2, 0));
wei_transform(k, c, 1, 1) =
0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) +
0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 1, 2) =
0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) -
0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 1, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) +
0.5 * double(wei_kcyx(k, c, 1, 2)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 2, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
0.5 * double(wei_kcyx(k, c, 1, 0)) +
0.5 * double(wei_kcyx(k, c, 2, 0));
wei_transform(k, c, 2, 1) =
0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) -
0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 2, 2) =
0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) +
0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 2, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) -
0.5 * double(wei_kcyx(k, c, 1, 2)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 3, 0) = double(wei_kcyx(k, c, 2, 0));
wei_transform(k, c, 3, 1) = 0.5 * double(wei_kcyx(k, c, 2, 0)) +
0.5 * double(wei_kcyx(k, c, 2, 1)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 3, 2) = 0.5 * double(wei_kcyx(k, c, 2, 0)) -
0.5 * double(wei_kcyx(k, c, 2, 1)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 3, 3) = double(wei_kcyx(k, c, 2, 2));
};
auto f_out_transform = [&](auto n, auto k, auto htile, auto wtile) {
for(int j = 0; j < HiPerTile; ++j)
{
for(int i = 0; i < WiPerTile; ++i)
{
double v = 0;
for(int c = 0; c < C; ++c)
{
v += in_transform(n, c, htile, wtile, j, i) * wei_transform(k, c, j, i);
}
out_transform(n, k, htile, wtile, j, i) = v;
}
}
};
auto f_out_hold = [&](auto n, auto k, auto htile, auto wtile) {
out_hold(n, k, htile, wtile, 0, 0) =
out_transform(n, k, htile, wtile, 0, 0) + out_transform(n, k, htile, wtile, 0, 1) +
out_transform(n, k, htile, wtile, 0, 2) + out_transform(n, k, htile, wtile, 1, 0) +
out_transform(n, k, htile, wtile, 1, 1) + out_transform(n, k, htile, wtile, 1, 2) +
out_transform(n, k, htile, wtile, 2, 0) + out_transform(n, k, htile, wtile, 2, 1) +
out_transform(n, k, htile, wtile, 2, 2);
out_hold(n, k, htile, wtile, 0, 1) =
out_transform(n, k, htile, wtile, 0, 1) - out_transform(n, k, htile, wtile, 0, 2) -
out_transform(n, k, htile, wtile, 0, 3) + out_transform(n, k, htile, wtile, 1, 1) -
out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 1, 3) +
out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
out_transform(n, k, htile, wtile, 2, 3);
out_hold(n, k, htile, wtile, 1, 0) =
out_transform(n, k, htile, wtile, 1, 0) + out_transform(n, k, htile, wtile, 1, 1) +
out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 2, 0) -
out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
out_transform(n, k, htile, wtile, 3, 0) - out_transform(n, k, htile, wtile, 3, 1) -
out_transform(n, k, htile, wtile, 3, 2);
out_hold(n, k, htile, wtile, 1, 1) =
out_transform(n, k, htile, wtile, 1, 1) - out_transform(n, k, htile, wtile, 1, 2) -
out_transform(n, k, htile, wtile, 1, 3) - out_transform(n, k, htile, wtile, 2, 1) +
out_transform(n, k, htile, wtile, 2, 2) + out_transform(n, k, htile, wtile, 2, 3) -
out_transform(n, k, htile, wtile, 3, 1) + out_transform(n, k, htile, wtile, 3, 2) +
out_transform(n, k, htile, wtile, 3, 3);
};
auto f_out = [&](auto n, auto k, auto htile, auto wtile) {
for(int j = 0; j < HoPerTile; ++j)
{
std::size_t ho = HoPerTile * htile + j;
for(int i = 0; i < WoPerTile; ++i)
{
std::size_t wo = WoPerTile * wtile + i;
out_nkhw(n, k, ho, wo) = out_hold(n, k, htile, wtile, j, i);
}
}
};
std::size_t num_thread = std::thread::hardware_concurrency();
make_ParallelTensorFunctor(f_in_hold, N, C, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_in_transform, N, C, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_wei_transform, K, C)(num_thread);
make_ParallelTensorFunctor(f_out_transform, N, K, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_out_hold, N, K, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_out, N, K, HTile, WTile)(num_thread);
}
template <class T>
void check_error(const Tensor<T>& ref, const Tensor<T>& result)
{
float error = 0;
float max_diff = -1;
float ref_value = 0, result_value = 0;
for(int i = 0; i < ref.mData.size(); ++i)
{
error += std::abs(double(ref.mData[i]) - double(result.mData[i]));
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
if(max_diff < diff)
{
max_diff = diff;
ref_value = ref.mData[i];
result_value = result.mData[i];
}
}
std::cout << "error: " << error << std::endl;
std::cout << "max_diff: " << max_diff << ", " << ref_value << ", " << result_value << std::endl;
}
driver/include/tensor.hpp
View file @ 96d73c21
#ifndef CK_TENSOR_HPP #ifndef TENSOR_HPP
#define CK_TENSOR_HPP #define TENSOR_HPP
#include <thread> #include <thread>
#include <vector> #include <vector>
......
driver/src/driver.cpp
View file @ 96d73c21
This diff is collapsed.
driver/src/driver.cu deleted 120000 → 0
View file @ 8133713e
driver.cpp
\ No newline at end of file
driver/src/driver.cu 0 → 100644
View file @ 96d73c21
#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
}
}
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