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Unverified Commit bbcb67d0 authored by Chao Liu's avatar Chao Liu Committed by GitHub
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Bwd Data NHWC (#22) 

* fix buffer_store bug
* remove obsolete kernels
* add bwd-data-v5r1-nhwc
parent ac62d13e
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composable_kernel/include/kernel_algorithm/gridwise_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ ac62d13e
#ifndef CK_GRIDWISE_CONVOLUTION_BACKWARD_DATA_IMPLICIT_GEMM_V2R1_NCHW_KCYX_NKHW_HPP
#define CK_GRIDWISE_CONVOLUTION_BACKWARD_DATA_IMPLICIT_GEMM_V2R1_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm.hpp"
namespace ck {
// GemmM = C * YTilda * XTilda;
// GemmN = N * HTildaSlice * WTildaSlice;
// GemmK = K * YDot * XDot;
template <index_t GridSize,
index_t BlockSize,
typename Float,
typename AccFloat,
typename InGlobalDesc,
typename WeiGlobalDesc,
typename OutGlobalDesc,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmKPerBlock,
index_t GemmMPerThread,
index_t GemmNPerThread,
index_t GemmKPerThread,
index_t GemmMLevel0Cluster,
index_t GemmNLevel0Cluster,
index_t GemmMLevel1Cluster,
index_t GemmNLevel1Cluster,
index_t GemmThreadGemmDataPerReadM,
index_t GemmThreadGemmDataPerReadN,
typename GemmABlockCopyThreadSliceLengths_GemmK_GemmM,
typename GemmABlockCopyThreadClusterLengths_GemmK_GemmM,
index_t GemmABlockCopySrcDataPerRead_GemmM,
index_t GemmABlockCopyDstDataPerWrite_GemmM,
typename GemmBBlockCopyThreadSliceLengths_GemmK_GemmN,
typename GemmBBlockCopyThreadClusterLengths_GemmK_GemmN,
index_t GemmBBlockCopySrcDataPerRead_GemmN,
index_t GemmBBlockCopyDstDataPerWrite_GemmN,
index_t GemmCThreadCopyDstDataPerWrite_GemmN1>
struct GridwiseConvolutionBackwardDataImplicitGemm_v2r1_nchw_kcyx_nkhw
{
__device__ void Run(Float* __restrict__ p_in_global,
const Float* __restrict__ p_wei_global,
const Float* __restrict__ p_out_global) const
{
constexpr auto in_n_c_hi_wi_global_desc = InGlobalDesc{};
constexpr auto wei_k_c_y_x_global_desc = WeiGlobalDesc{};
constexpr auto out_n_k_ho_wo_global_desc = OutGlobalDesc{};
constexpr index_t N = in_n_c_hi_wi_global_desc.GetLengths()[0];
constexpr index_t C = in_n_c_hi_wi_global_desc.GetLengths()[1];
constexpr index_t Hi = in_n_c_hi_wi_global_desc.GetLengths()[2];
constexpr index_t Wi = in_n_c_hi_wi_global_desc.GetLengths()[3];
constexpr index_t K = out_n_k_ho_wo_global_desc.GetLengths()[1];
constexpr index_t Ho = out_n_k_ho_wo_global_desc.GetLengths()[2];
constexpr index_t Wo = out_n_k_ho_wo_global_desc.GetLengths()[3];
constexpr index_t Y = wei_k_c_y_x_global_desc.GetLengths()[2];
constexpr index_t X = wei_k_c_y_x_global_desc.GetLengths()[3];
constexpr index_t ConvStrideH = ConvStrides{}[0];
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
#if 0 // debug
// sanity-check for vectorized memory load
// TODO: this logic may not be correct for bwd-data
static_assert(
(Wo == 1 || (ConvStrideW == 1 || GemmCThreadCopyDstDataPerWrite_GemmN1 == 1)) &&
(X == 1 || ConvDilationW % GemmCThreadCopyDstDataPerWrite_GemmN1 == 0),
"wrong! aligment requirement for vectorized global load of input tensor will "
"be violated");
#endif
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t YTilda = ConvStrideH / GcdStrideDilationH;
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
constexpr index_t YDot = math::integer_divide_ceil(Y, YTilda);
constexpr index_t XDot = math::integer_divide_ceil(X, XTilda);
constexpr index_t HTilda =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - 1), ConvStrideH);
constexpr index_t WTilda =
Wo + math::integer_divide_ceil(ConvDilationW * (X - 1), ConvStrideW);
constexpr index_t HTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[0] - ConvDilationH * (YTilda - 1)), ConvStrides{}[0]);
constexpr index_t WTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[1] - ConvDilationW * (XTilda - 1)), ConvStrides{}[1]);
constexpr index_t HTildaRight = math::min(
HTilda, math::integer_divide_ceil(InLeftPads{}[0] + Hi - 1, ConvStrides{}[0]) + 1);
constexpr index_t WTildaRight = math::min(
WTilda, math::integer_divide_ceil(InLeftPads{}[1] + Wi - 1, ConvStrides{}[1]) + 1);
constexpr index_t HTildaSlice = HTildaRight - HTildaLeft;
constexpr index_t WTildaSlice = WTildaRight - WTildaLeft;
// weight tensor
constexpr auto wei_k_c_ydot_ytilda_xdot_xtilda_global_desc = transform_tensor_descriptor(
wei_k_c_y_x_global_desc,
make_tuple(PassThrough<K>{},
PassThrough<C>{},
Embed<Y,
Sequence<YDot, YTilda>,
Sequence<ConvStrideH / GcdStrideDilationH, 1, 0>>{},
Embed<X,
Sequence<XDot, XTilda>,
Sequence<ConvStrideW / GcdStrideDilationW, 1, 0>>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
constexpr auto wei_gemmk_gemmm_global_desc = transform_tensor_descriptor(
wei_k_c_ydot_ytilda_xdot_xtilda_global_desc,
make_tuple(Merge<Sequence<K, YDot, XDot>>{}, Merge<Sequence<C, YTilda, XTilda>>{}),
make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// output tensor
constexpr auto out_n_k_ydot_htilda_xdot_wtilda_global_desc = transform_tensor_descriptor(
out_n_k_ho_wo_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<K>{},
Embed<Ho,
Sequence<YDot, HTilda>,
Sequence<-ConvDilationH / GcdStrideDilationH, 1, 0>>{},
Embed<Wo,
Sequence<XDot, WTilda>,
Sequence<-ConvDilationW / GcdStrideDilationW, 1, 0>>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
constexpr auto out_n_k_ydot_htildaslice_xdot_wtildaslice_global_desc =
transform_tensor_descriptor(
out_n_k_ydot_htilda_xdot_wtilda_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<K>{},
PassThrough<YTilda>{},
PassThrough<XTilda>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<HTildaLeft, WTildaLeft>,
Sequence<HTildaRight, WTildaRight>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}));
constexpr auto out_gemmk_gemmn_global_desc =
transform_tensor_descriptor(out_n_k_ydot_htildaslice_xdot_wtildaslice_global_desc,
make_tuple(Merge<Sequence<K, YDot, XDot>>{},
Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
#if 1 // debug
constexpr bool in_skip_all_out_of_bound_check = false;
#else
constexpr bool in_skip_all_out_of_bound_check = true;
#endif
// input tensor
constexpr auto in_n_c_hip_wip_global_desc = transform_tensor_descriptor(
in_n_c_hi_wi_global_desc,
make_tuple(
PassThrough<N>{},
PassThrough<C>{},
Pad<Sequence<Hi, Wi>, InLeftPads, InRightPads, in_skip_all_out_of_bound_check>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}));
constexpr index_t Hip = in_n_c_hip_wip_global_desc.GetLengths()[2];
constexpr index_t Wip = in_n_c_hip_wip_global_desc.GetLengths()[3];
constexpr auto in_n_c_ytilda_htilda_xtilda_wtilda_global_desc = transform_tensor_descriptor(
in_n_c_hip_wip_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<C>{},
Embed<Hip,
Sequence<YTilda, HTilda>,
Sequence<ConvDilationH, ConvStrideH, 0>,
in_skip_all_out_of_bound_check>{},
Embed<Wip,
Sequence<XTilda, WTilda>,
Sequence<ConvDilationW, ConvStrideW, 0>,
in_skip_all_out_of_bound_check>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
constexpr auto in_n_c_ytilda_htildaslice_xtilda_wtildaslice_global_desc =
transform_tensor_descriptor(
in_n_c_ytilda_htilda_xtilda_wtilda_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<C>{},
PassThrough<YTilda>{},
PassThrough<XTilda>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<HTildaLeft, WTildaLeft>,
Sequence<HTildaRight, WTildaRight>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}));
constexpr auto in_gemmm_gemmn_global_desc =
transform_tensor_descriptor(in_n_c_ytilda_htildaslice_xtilda_wtildaslice_global_desc,
make_tuple(Merge<Sequence<C, YTilda, XTilda>>{},
Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// GEMM
constexpr auto gridwise_gemm =
GridwiseGemmTransposedANormalBNormalC_v1<GridSize,
BlockSize,
Float,
AccFloat,
decltype(wei_gemmk_gemmm_global_desc),
decltype(out_gemmk_gemmn_global_desc),
decltype(in_gemmm_gemmn_global_desc),
InMemoryDataOperation::Set,
GemmMPerBlock,
GemmNPerBlock,
GemmKPerBlock,
GemmMPerThread,
GemmNPerThread,
GemmKPerThread,
GemmMLevel0Cluster,
GemmNLevel0Cluster,
GemmMLevel1Cluster,
GemmNLevel1Cluster,
GemmThreadGemmDataPerReadM,
GemmThreadGemmDataPerReadN,
GemmABlockCopyThreadSliceLengths_GemmK_GemmM,
GemmABlockCopyThreadClusterLengths_GemmK_GemmM,
Sequence<0, 1>,
Sequence<0, 1>,
1,
GemmABlockCopySrcDataPerRead_GemmM,
GemmABlockCopyDstDataPerWrite_GemmM,
GemmBBlockCopyThreadSliceLengths_GemmK_GemmN,
GemmBBlockCopyThreadClusterLengths_GemmK_GemmN,
Sequence<0, 1>,
Sequence<0, 1>,
1,
GemmBBlockCopySrcDataPerRead_GemmN,
GemmBBlockCopyDstDataPerWrite_GemmN,
Sequence<0, 1, 2, 3>,
3,
GemmCThreadCopyDstDataPerWrite_GemmN1>{};
gridwise_gemm.Run(p_wei_global, p_out_global, p_in_global);
}
};
} // namespace ck
#endif
composable_kernel/include/kernel_algorithm/gridwise_convolution_backward_data_implicit_gemm_v3r1_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ ac62d13e
#ifndef CK_GRIDWISE_CONVOLUTION_BACKWARD_DATA_IMPLICIT_GEMM_V3R1_NCHW_KCYX_NKHW_HPP
#define CK_GRIDWISE_CONVOLUTION_BACKWARD_DATA_IMPLICIT_GEMM_V3R1_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm.hpp"
namespace ck {
// Number of GEMMs: YTilda * XTilda
// GemmM = C
// GemmN = N * HTildaSlice * WTildaSlice
// GemmK = K * YDotSlice * XDotSlice
template <index_t GridSize,
index_t BlockSize,
typename Float,
typename AccFloat,
typename InGlobalDesc,
typename WeiGlobalDesc,
typename OutGlobalDesc,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmKPerBlock,
index_t GemmMPerThread,
index_t GemmNPerThread,
index_t GemmKPerThread,
index_t GemmMLevel0Cluster,
index_t GemmNLevel0Cluster,
index_t GemmMLevel1Cluster,
index_t GemmNLevel1Cluster,
index_t GemmThreadGemmDataPerReadM,
index_t GemmThreadGemmDataPerReadN,
typename GemmABlockCopyThreadSliceLengths_GemmK_GemmM,
typename GemmABlockCopyThreadClusterLengths_GemmK_GemmM,
index_t GemmABlockCopySrcDataPerRead_GemmM,
index_t GemmABlockCopyDstDataPerWrite_GemmM,
typename GemmBBlockCopyThreadSliceLengths_GemmK_GemmN,
typename GemmBBlockCopyThreadClusterLengths_GemmK_GemmN,
index_t GemmBBlockCopySrcDataPerRead_GemmN,
index_t GemmBBlockCopyDstDataPerWrite_GemmN,
index_t GemmCThreadCopyDstDataPerWrite_GemmN1>
struct GridwiseConvolutionBackwardDataImplicitGemm_v3r1_nchw_kcyx_nkhw
{
// this is a hack, should query this info from gridwise_gemm instead of duplicate its logic
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
constexpr index_t max_lds_align = math::lcm(GemmABlockCopyDstDataPerWrite_GemmM,
GemmBBlockCopyDstDataPerWrite_GemmN,
GemmThreadGemmDataPerReadM,
GemmThreadGemmDataPerReadN);
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_gemmk_gemmm_block_desc = make_native_tensor_descriptor_aligned(
Sequence<GemmKPerBlock, GemmMPerBlock>{}, Number<max_lds_align>{});
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_gemmk_gemmn_block_desc = make_native_tensor_descriptor_aligned(
Sequence<GemmKPerBlock, GemmNPerBlock>{}, Number<max_lds_align>{});
// LDS allocation for A and B: be careful of alignment
constexpr index_t a_block_space =
math::integer_least_multiple(a_gemmk_gemmm_block_desc.GetElementSpace(), max_lds_align);
constexpr index_t b_block_space =
math::integer_least_multiple(b_gemmk_gemmn_block_desc.GetElementSpace(), max_lds_align);
return 2 * (a_block_space + b_block_space) * sizeof(Float);
}
__device__ void Run(Float* __restrict__ p_in_global,
const Float* __restrict__ p_wei_global,
const Float* __restrict__ p_out_global) const
{
constexpr auto in_n_c_hi_wi_global_desc = InGlobalDesc{};
constexpr auto wei_k_c_y_x_global_desc = WeiGlobalDesc{};
constexpr auto out_n_k_ho_wo_global_desc = OutGlobalDesc{};
constexpr index_t N = in_n_c_hi_wi_global_desc.GetLengths()[0];
constexpr index_t C = in_n_c_hi_wi_global_desc.GetLengths()[1];
constexpr index_t Hi = in_n_c_hi_wi_global_desc.GetLengths()[2];
constexpr index_t Wi = in_n_c_hi_wi_global_desc.GetLengths()[3];
constexpr index_t K = out_n_k_ho_wo_global_desc.GetLengths()[1];
constexpr index_t Ho = out_n_k_ho_wo_global_desc.GetLengths()[2];
constexpr index_t Wo = out_n_k_ho_wo_global_desc.GetLengths()[3];
constexpr index_t Y = wei_k_c_y_x_global_desc.GetLengths()[2];
constexpr index_t X = wei_k_c_y_x_global_desc.GetLengths()[3];
constexpr index_t ConvStrideH = ConvStrides{}[0];
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
#if 0 // debug
// sanity-check for vectorized memory load
// TODO: this logic may not be correct for bwd-data
static_assert(
(Wo == 1 || (ConvStrideW == 1 || GemmCThreadCopyDstDataPerWrite_GemmN1 == 1)) &&
(X == 1 || ConvDilationW % GemmCThreadCopyDstDataPerWrite_GemmN1 == 0),
"wrong! aligment requirement for vectorized global load of input tensor will "
"be violated");
#endif
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t YTilda = ConvStrideH / GcdStrideDilationH;
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
constexpr index_t YDot = math::integer_divide_ceil(Y, YTilda);
constexpr index_t XDot = math::integer_divide_ceil(X, XTilda);
constexpr index_t HTilda =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - 1), ConvStrideH);
constexpr index_t WTilda =
Wo + math::integer_divide_ceil(ConvDilationW * (X - 1), ConvStrideW);
constexpr index_t HTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[0] - ConvDilationH * (YTilda - 1)), ConvStrides{}[0]);
constexpr index_t WTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[1] - ConvDilationW * (XTilda - 1)), ConvStrides{}[1]);
constexpr index_t HTildaRight = math::min(
HTilda, math::integer_divide_ceil(InLeftPads{}[0] + Hi - 1, ConvStrides{}[0]) + 1);
constexpr index_t WTildaRight = math::min(
WTilda, math::integer_divide_ceil(InLeftPads{}[1] + Wi - 1, ConvStrides{}[1]) + 1);
constexpr index_t HTildaSlice = HTildaRight - HTildaLeft;
constexpr index_t WTildaSlice = WTildaRight - WTildaLeft;
constexpr bool wei_skip_all_out_of_bound_check = true;
// weight tensor
constexpr auto wei_k_c_ydot_ytilda_xdot_xtilda_global_desc = transform_tensor_descriptor(
wei_k_c_y_x_global_desc,
make_tuple(PassThrough<K>{},
PassThrough<C>{},
Embed<Y,
Sequence<YDot, YTilda>,
Sequence<ConvStrideH / GcdStrideDilationH, 1, 0>,
wei_skip_all_out_of_bound_check>{},
Embed<X,
Sequence<XDot, XTilda>,
Sequence<ConvStrideW / GcdStrideDilationW, 1, 0>,
wei_skip_all_out_of_bound_check>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
#if 1 // debug
constexpr bool out_skip_all_out_of_bound_check = false;
#else
constexpr bool out_skip_all_out_of_bound_check = true;
#endif
// output tensor
constexpr auto out_n_k_ydot_htilda_xdot_wtilda_global_desc = transform_tensor_descriptor(
out_n_k_ho_wo_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<K>{},
Embed<Ho,
Sequence<YDot, HTilda>,
Sequence<-ConvDilationH / GcdStrideDilationH, 1, 0>,
out_skip_all_out_of_bound_check>{},
Embed<Wo,
Sequence<XDot, WTilda>,
Sequence<-ConvDilationW / GcdStrideDilationW, 1, 0>,
out_skip_all_out_of_bound_check>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
constexpr auto out_n_k_ydot_htildaslice_xdot_wtildaslice_global_desc =
transform_tensor_descriptor(
out_n_k_ydot_htilda_xdot_wtilda_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<K>{},
PassThrough<YTilda>{},
PassThrough<XTilda>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<HTildaLeft, WTildaLeft>,
Sequence<HTildaRight, WTildaRight>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}));
#if 1 // debug
constexpr bool in_skip_all_out_of_bound_check = false;
#else
constexpr bool in_skip_all_out_of_bound_check = true;
#endif
// input tensor
constexpr auto in_n_c_hip_wip_global_desc = transform_tensor_descriptor(
in_n_c_hi_wi_global_desc,
make_tuple(
PassThrough<N>{},
PassThrough<C>{},
Pad<Sequence<Hi, Wi>, InLeftPads, InRightPads, in_skip_all_out_of_bound_check>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}));
constexpr index_t Hip = in_n_c_hip_wip_global_desc.GetLengths()[2];
constexpr index_t Wip = in_n_c_hip_wip_global_desc.GetLengths()[3];
constexpr auto in_n_c_ytilda_htilda_xtilda_wtilda_global_desc = transform_tensor_descriptor(
in_n_c_hip_wip_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<C>{},
Embed<Hip,
Sequence<YTilda, HTilda>,
Sequence<ConvDilationH, ConvStrideH, 0>,
in_skip_all_out_of_bound_check>{},
Embed<Wip,
Sequence<XTilda, WTilda>,
Sequence<ConvDilationW, ConvStrideW, 0>,
in_skip_all_out_of_bound_check>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
constexpr auto in_n_c_ytilda_htildaslice_xtilda_wtildaslice_global_desc =
transform_tensor_descriptor(
in_n_c_ytilda_htilda_xtilda_wtilda_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<C>{},
PassThrough<YTilda>{},
PassThrough<XTilda>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<HTildaLeft, WTildaLeft>,
Sequence<HTildaRight, WTildaRight>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}));
// GEMMs
constexpr index_t shared_block_size = GetSharedMemoryNumberOfByte() / sizeof(Float);
__shared__ Float p_shared_block[shared_block_size];
static_for<0, YTilda, 1>{}([&](auto iYTilda_) {
static_for<0, XTilda, 1>{}([&](auto iXTilda_) {
constexpr index_t iYTilda = decltype(iYTilda_){};
constexpr index_t iXTilda = decltype(iXTilda_){};
constexpr index_t YDotSlice = (iYTilda + 1) * YDot <= Y ? YDot : Y % YDot;
constexpr index_t XDotSlice = (iXTilda + 1) * XDot <= X ? XDot : X % XDot;
// A matrix
constexpr auto wei_k_c_ydotslice_ytidaslice_xdotslice_xtildaslice_global_desc =
transform_tensor_descriptor(
wei_k_c_ydot_ytilda_xdot_xtilda_global_desc,
make_tuple(PassThrough<K>{},
PassThrough<C>{},
Slice<Sequence<YDot, XDot>,
Sequence<0, 0>,
Sequence<YDotSlice, XDotSlice>>{},
Slice<Sequence<YTilda, XTilda>,
Sequence<iYTilda, iXTilda>,
Sequence<iYTilda + 1, iXTilda + 1>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2, 4>{}, Sequence<3, 5>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2, 4>{}, Sequence<3, 5>{}));
constexpr auto wei_gemmk_gemmm_global_desc = transform_tensor_descriptor(
wei_k_c_ydotslice_ytidaslice_xdotslice_xtildaslice_global_desc,
make_tuple(Merge<Sequence<K, YDotSlice, XDotSlice>>{},
Merge<Sequence<C, 1, 1>>{}),
make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// B matrix
constexpr auto out_n_k_ydotslice_htildaslice_xdotslice_wtildaslice_global_desc =
transform_tensor_descriptor(
out_n_k_ydot_htildaslice_xdot_wtildaslice_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<K>{},
PassThrough<HTildaSlice>{},
PassThrough<WTildaSlice>{},
Slice<Sequence<YDot, XDot>,
Sequence<0, 0>,
Sequence<YDotSlice, XDotSlice>>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<5>{},
Sequence<2, 4>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<5>{},
Sequence<2, 4>{}));
constexpr auto out_gemmk_gemmn_global_desc = transform_tensor_descriptor(
out_n_k_ydotslice_htildaslice_xdotslice_wtildaslice_global_desc,
make_tuple(Merge<Sequence<K, YDotSlice, XDotSlice>>{},
Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// C matrix
constexpr auto in_n_c_ytildaslice_htildaslice_xtildaslice_wtildaslice_global_desc =
transform_tensor_descriptor(
in_n_c_ytilda_htildaslice_xtilda_wtildaslice_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<C>{},
PassThrough<HTildaSlice>{},
PassThrough<WTildaSlice>{},
Slice<Sequence<YTilda, XTilda>,
Sequence<iYTilda, iXTilda>,
Sequence<iYTilda + 1, iXTilda + 1>>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<5>{},
Sequence<2, 4>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<5>{},
Sequence<2, 4>{}));
constexpr auto in_gemmm_gemmn_global_desc = transform_tensor_descriptor(
in_n_c_ytildaslice_htildaslice_xtildaslice_wtildaslice_global_desc,
make_tuple(Merge<Sequence<C, 1, 1>>{},
Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
constexpr auto gridwise_gemm = GridwiseGemmTransposedANormalBNormalC_v1<
GridSize,
BlockSize,
Float,
AccFloat,
decltype(wei_gemmk_gemmm_global_desc),
decltype(out_gemmk_gemmn_global_desc),
decltype(in_gemmm_gemmn_global_desc),
InMemoryDataOperation::Set,
GemmMPerBlock,
GemmNPerBlock,
GemmKPerBlock,
GemmMPerThread,
GemmNPerThread,
GemmKPerThread,
GemmMLevel0Cluster,
GemmNLevel0Cluster,
GemmMLevel1Cluster,
GemmNLevel1Cluster,
GemmThreadGemmDataPerReadM,
GemmThreadGemmDataPerReadN,
GemmABlockCopyThreadSliceLengths_GemmK_GemmM,
GemmABlockCopyThreadClusterLengths_GemmK_GemmM,
Sequence<0, 1>,
Sequence<0, 1>,
1,
GemmABlockCopySrcDataPerRead_GemmM,
GemmABlockCopyDstDataPerWrite_GemmM,
GemmBBlockCopyThreadSliceLengths_GemmK_GemmN,
GemmBBlockCopyThreadClusterLengths_GemmK_GemmN,
Sequence<0, 1>,
Sequence<0, 1>,
1,
GemmBBlockCopySrcDataPerRead_GemmN,
GemmBBlockCopyDstDataPerWrite_GemmN,
Sequence<0, 1, 2, 3>,
3,
GemmCThreadCopyDstDataPerWrite_GemmN1>{};
gridwise_gemm.Run(p_wei_global, p_out_global, p_in_global, p_shared_block);
// is synchronization necessary?
__syncthreads();
});
});
}
};
} // namespace ck
#endif
composable_kernel/include/kernel_algorithm/gridwise_convolution_backward_data_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp
View file @ bbcb67d0
......@@ -167,9 +167,6 @@ struct GridwiseConvolutionBackwardDataImplicitGemm_v4r1_nchw_kcyx_nkhw
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
//\todo static_assert for global vector load/store
// statc_assert();
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
......@@ -179,6 +176,9 @@ struct GridwiseConvolutionBackwardDataImplicitGemm_v4r1_nchw_kcyx_nkhw
constexpr index_t YDot = math::integer_divide_ceil(Y, YTilda);
constexpr index_t XDot = math::integer_divide_ceil(X, XTilda);
constexpr index_t YDotSlice = (iYTilda + 1) * YDot <= Y ? YDot : Y % YDot;
constexpr index_t XDotSlice = (iXTilda + 1) * XDot <= X ? XDot : X % XDot;
constexpr index_t HTilda =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - 1), ConvStrideH);
constexpr index_t WTilda =
......@@ -198,10 +198,10 @@ struct GridwiseConvolutionBackwardDataImplicitGemm_v4r1_nchw_kcyx_nkhw
constexpr index_t HTildaSlice = iHTildaRight - iHTildaLeft;
constexpr index_t WTildaSlice = iWTildaRight - iWTildaLeft;
// A matrix: weight
// weight out-of-bound check can be skipped
constexpr bool wei_skip_out_of_bound_check = true;
// weight tensor
constexpr auto wei_k_c_ydot_ytilda_xdot_xtilda_global_desc = transform_tensor_descriptor(
wei_k_c_y_x_global_desc,
make_tuple(PassThrough<K>{},
......@@ -217,15 +217,31 @@ struct GridwiseConvolutionBackwardDataImplicitGemm_v4r1_nchw_kcyx_nkhw
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
constexpr auto wei_k_c_ydotslice_xdotslice_global_desc = transform_tensor_descriptor(
wei_k_c_ydot_ytilda_xdot_xtilda_global_desc,
make_tuple(
PassThrough<K>{},
PassThrough<C>{},
Slice<Sequence<YDot, XDot>, Sequence<0, 0>, Sequence<YDotSlice, XDotSlice>>{},
Freeze<Sequence<YTilda, XTilda>, Sequence<iYTilda, iXTilda>>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 4>{}, Sequence<3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<>{}));
constexpr auto wei_gemmk_gemmm_global_desc = transform_tensor_descriptor(
wei_k_c_ydotslice_xdotslice_global_desc,
make_tuple(Merge<Sequence<K, YDotSlice, XDotSlice>>{}, PassThrough<C>{}),
make_tuple(Sequence<0, 2, 3>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// B matrix: output tensor
// TODO sometimes output tensor out-of-bound check can be skipped, find out all such
// situations
#if !CK_EXPERIMENTAL_IMPLICIT_GEMM_BACKWARD_DATA_V4R1_OUTPUT_SKIP_OUT_OF_BOUND_CHECK
constexpr bool out_skip_out_of_bound_check = false;
#else
//\todo sometimes output tensor out-of-bound check can be skipped, find out all such
// situations
constexpr bool out_skip_out_of_bound_check = true;
#endif
// output tensor
constexpr auto out_n_k_ydot_htilda_xdot_wtilda_global_desc = transform_tensor_descriptor(
out_n_k_ho_wo_global_desc,
make_tuple(PassThrough<N>{},
......@@ -246,8 +262,8 @@ struct GridwiseConvolutionBackwardDataImplicitGemm_v4r1_nchw_kcyx_nkhw
out_n_k_ydot_htilda_xdot_wtilda_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<K>{},
PassThrough<YTilda>{},
PassThrough<XTilda>{},
PassThrough<YDot>{},
PassThrough<XDot>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<iHTildaLeft, iWTildaLeft>,
Sequence<iHTildaRight, iWTildaRight>>{}),
......@@ -256,14 +272,35 @@ struct GridwiseConvolutionBackwardDataImplicitGemm_v4r1_nchw_kcyx_nkhw
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}));
constexpr auto out_n_k_ydotslice_htildaslice_xdotslice_wtildaslice_global_desc =
transform_tensor_descriptor(
out_n_k_ydot_htildaslice_xdot_wtildaslice_global_desc,
make_tuple(
PassThrough<N>{},
PassThrough<K>{},
PassThrough<HTildaSlice>{},
PassThrough<WTildaSlice>{},
Slice<Sequence<YDot, XDot>, Sequence<0, 0>, Sequence<YDotSlice, XDotSlice>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<3>{}, Sequence<5>{}, Sequence<2, 4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<3>{}, Sequence<5>{}, Sequence<2, 4>{}));
constexpr auto out_gemmk_gemmn_global_desc = transform_tensor_descriptor(
out_n_k_ydotslice_htildaslice_xdotslice_wtildaslice_global_desc,
make_tuple(Merge<Sequence<K, YDotSlice, XDotSlice>>{},
Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// C matrix: input tensor
// TODO sometimes input out-of-bound check can be skipped, find out all such situations
#if !CK_EXPERIMENTAL_IMPLICIT_GEMM_BACKWARD_DATA_V4R1_INPUT_SKIP_OUT_OF_BOUND_CHECK
constexpr bool in_skip_out_of_bound_check = false;
#else
//\todo sometimes input out-of-bound check can be skipped, find out all such situations
constexpr bool in_skip_out_of_bound_check = true;
#endif
// input tensor
constexpr auto in_n_c_hip_wip_global_desc = transform_tensor_descriptor(
in_n_c_hi_wi_global_desc,
make_tuple(
......@@ -291,87 +328,21 @@ struct GridwiseConvolutionBackwardDataImplicitGemm_v4r1_nchw_kcyx_nkhw
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
constexpr auto in_n_c_ytilda_htildaslice_xtilda_wtildaslice_global_desc =
transform_tensor_descriptor(
constexpr auto in_n_c_htildaslice_wtildaslice_global_desc = transform_tensor_descriptor(
in_n_c_ytilda_htilda_xtilda_wtilda_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<C>{},
PassThrough<YTilda>{},
PassThrough<XTilda>{},
Freeze<Sequence<YTilda, XTilda>, Sequence<iYTilda, iXTilda>>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<iHTildaLeft, iWTildaLeft>,
Sequence<iHTildaRight, iWTildaRight>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}, Sequence<3, 5>{}));
// GEMM
constexpr index_t YDotSlice = (iYTilda + 1) * YDot <= Y ? YDot : Y % YDot;
constexpr index_t XDotSlice = (iXTilda + 1) * XDot <= X ? XDot : X % XDot;
// A matrix
constexpr auto wei_k_c_ydotslice_ytidaslice_xdotslice_xtildaslice_global_desc =
transform_tensor_descriptor(
wei_k_c_ydot_ytilda_xdot_xtilda_global_desc,
make_tuple(
PassThrough<K>{},
PassThrough<C>{},
Slice<Sequence<YDot, XDot>, Sequence<0, 0>, Sequence<YDotSlice, XDotSlice>>{},
Slice<Sequence<YTilda, XTilda>,
Sequence<iYTilda, iXTilda>,
Sequence<iYTilda + 1, iXTilda + 1>>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 4>{}, Sequence<3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 4>{}, Sequence<3, 5>{}));
constexpr auto wei_gemmk_gemmm_global_desc = transform_tensor_descriptor(
wei_k_c_ydotslice_ytidaslice_xdotslice_xtildaslice_global_desc,
make_tuple(Merge<Sequence<K, YDotSlice, XDotSlice>>{}, Merge<Sequence<C, 1, 1>>{}),
make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// B matrix
constexpr auto out_n_k_ydotslice_htildaslice_xdotslice_wtildaslice_global_desc =
transform_tensor_descriptor(
out_n_k_ydot_htildaslice_xdot_wtildaslice_global_desc,
make_tuple(
PassThrough<N>{},
PassThrough<K>{},
PassThrough<HTildaSlice>{},
PassThrough<WTildaSlice>{},
Slice<Sequence<YDot, XDot>, Sequence<0, 0>, Sequence<YDotSlice, XDotSlice>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<3>{}, Sequence<5>{}, Sequence<2, 4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<3>{}, Sequence<5>{}, Sequence<2, 4>{}));
constexpr auto out_gemmk_gemmn_global_desc = transform_tensor_descriptor(
out_n_k_ydotslice_htildaslice_xdotslice_wtildaslice_global_desc,
make_tuple(Merge<Sequence<K, YDotSlice, XDotSlice>>{},
Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// C matrix
constexpr auto in_n_c_ytildaslice_htildaslice_xtildaslice_wtildaslice_global_desc =
transform_tensor_descriptor(
in_n_c_ytilda_htildaslice_xtilda_wtildaslice_global_desc,
make_tuple(PassThrough<N>{},
PassThrough<C>{},
PassThrough<HTildaSlice>{},
PassThrough<WTildaSlice>{},
Slice<Sequence<YTilda, XTilda>,
Sequence<iYTilda, iXTilda>,
Sequence<iYTilda + 1, iXTilda + 1>>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<3>{}, Sequence<5>{}, Sequence<2, 4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<3>{}, Sequence<5>{}, Sequence<2, 4>{}));
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<>{}, Sequence<2, 3>{}));
constexpr auto in_gemmm_gemmn_global_desc = transform_tensor_descriptor(
in_n_c_ytildaslice_htildaslice_xtildaslice_wtildaslice_global_desc,
make_tuple(Merge<Sequence<C, 1, 1>>{}, Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
in_n_c_htildaslice_wtildaslice_global_desc,
make_tuple(PassThrough<C>{}, Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1>{}, Sequence<0, 2, 3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
constexpr auto gridwise_gemm =
......
composable_kernel/include/kernel_algorithm/gridwise_convolution_backward_data_implicit_gemm_v5r1_nhwc_kyxc_nhwk.hpp 0 → 100644
View file @ bbcb67d0
#ifndef CK_GRIDWISE_CONVOLUTION_BACKWARD_DATA_IMPLICIT_GEMM_V5R1_NHWC_KYXC_NHWK_HPP
#define CK_GRIDWISE_CONVOLUTION_BACKWARD_DATA_IMPLICIT_GEMM_V5R1_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm.hpp"
namespace ck {
// Number of GEMMs = YTilda * XTilda
// GemmM = C
// GemmN = N * HTildaSlice * WTildaSlice
// GemmK0 = YDotSlice
// GemmK1 = XDotSlice
// GemmK2 = K
template <index_t GridSize,
index_t BlockSize,
typename Float,
typename AccFloat,
typename InGlobalDesc,
typename WeiGlobalDesc,
typename OutGlobalDesc,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t GemmMPerBlock,
index_t GemmNPerBlock,
index_t GemmKPerBlock,
index_t GemmMPerThread,
index_t GemmNPerThread,
index_t GemmKPerThread,
index_t GemmMLevel0Cluster,
index_t GemmNLevel0Cluster,
index_t GemmMLevel1Cluster,
index_t GemmNLevel1Cluster,
index_t ThreadGemmDataPerRead_GemmM,
index_t ThreadGemmDataPerRead_GemmN,
typename GemmABlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmM,
typename GemmABlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmM,
index_t GemmABlockCopySrcDataPerRead_GemmM,
index_t GemmABlockCopyDstDataPerWrite_GemmM,
typename GemmBBlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmN,
typename GemmBBlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmN,
index_t GemmBBlockCopySrcDataPerRead_GemmK2,
index_t GemmBBlockCopyDstDataPerWrite_GemmN,
index_t GemmCThreadCopyDstDataPerWrite_GemmN1>
struct GridwiseConvolutionBackwardDataImplicitGemm_v5r1_nhwc_kyxc_nhwk
{
__host__ __device__ static constexpr index_t GetNumberOfGemm()
{
constexpr index_t ConvStrideH = ConvStrides{}[0];
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t YTilda = ConvStrideH / GcdStrideDilationH;
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
return YTilda * XTilda;
}
__host__ __device__ static constexpr auto GetGemmSizeImpl(index_t iYTilda, index_t iXTilda)
{
constexpr index_t N = InGlobalDesc::GetLengths()[0];
constexpr index_t Hi = InGlobalDesc::GetLengths()[1];
constexpr index_t Wi = InGlobalDesc::GetLengths()[2];
constexpr index_t C = InGlobalDesc::GetLengths()[3];
constexpr index_t Ho = OutGlobalDesc::GetLengths()[1];
constexpr index_t Wo = OutGlobalDesc::GetLengths()[2];
constexpr index_t K = OutGlobalDesc::GetLengths()[3];
constexpr index_t Y = WeiGlobalDesc::GetLengths()[1];
constexpr index_t X = WeiGlobalDesc::GetLengths()[2];
constexpr index_t ConvStrideH = ConvStrides{}[0];
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t YTilda = ConvStrideH / GcdStrideDilationH;
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
constexpr index_t YDot = math::integer_divide_ceil(Y, YTilda);
constexpr index_t XDot = math::integer_divide_ceil(X, XTilda);
constexpr index_t HTilda =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - 1), ConvStrideH);
constexpr index_t WTilda =
Wo + math::integer_divide_ceil(ConvDilationW * (X - 1), ConvStrideW);
// only work on HTilda and WTilda that contribute to non-padding area of input tensor
constexpr index_t iHTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[0] - ConvDilationH * (YTilda - 1)), ConvStrides{}[0]);
constexpr index_t iWTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[1] - ConvDilationW * (XTilda - 1)), ConvStrides{}[1]);
constexpr index_t iHTildaRight = math::min(
HTilda, math::integer_divide_ceil(InLeftPads{}[0] + Hi - 1, ConvStrides{}[0]) + 1);
constexpr index_t iWTildaRight = math::min(
WTilda, math::integer_divide_ceil(InLeftPads{}[1] + Wi - 1, ConvStrides{}[1]) + 1);
constexpr index_t HTildaSlice = iHTildaRight - iHTildaLeft;
constexpr index_t WTildaSlice = iWTildaRight - iWTildaLeft;
// GemmM and GemmN
constexpr index_t GemmM = C;
constexpr index_t GemmN = N * HTildaSlice * WTildaSlice;
// GemmK is different for each GEMM
index_t YDotSlice = (iYTilda + 1) * YDot <= Y ? YDot : Y % YDot;
index_t XDotSlice = (iXTilda + 1) * XDot <= X ? XDot : X % XDot;
index_t GemmK0 = YDotSlice;
index_t GemmK1 = XDotSlice;
index_t GemmK2 = K;
return Array<index_t, 5>{GemmM, GemmN, GemmK0, GemmK1, GemmK2};
}
__host__ __device__ static constexpr auto GetGemmSize(index_t gemm_id)
{
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationW = ConvDilations{}[1];
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
index_t iYTilda = gemm_id / XTilda;
index_t iXTilda = gemm_id % XTilda;
return GetGemmSizeImpl(iYTilda, iXTilda);
}
template <index_t iYTilda, index_t iXTilda>
__device__ static void RunImpl(Float* __restrict__ p_in_global,
const Float* __restrict__ p_wei_global,
const Float* __restrict__ p_out_global)
{
constexpr auto in_n_hi_wi_c_global_desc = InGlobalDesc{};
constexpr auto wei_k_y_x_c_global_desc = WeiGlobalDesc{};
constexpr auto out_n_ho_wo_k_global_desc = OutGlobalDesc{};
constexpr index_t N = in_n_hi_wi_c_global_desc.GetLengths()[0];
constexpr index_t Hi = in_n_hi_wi_c_global_desc.GetLengths()[1];
constexpr index_t Wi = in_n_hi_wi_c_global_desc.GetLengths()[2];
constexpr index_t C = in_n_hi_wi_c_global_desc.GetLengths()[3];
constexpr index_t Ho = out_n_ho_wo_k_global_desc.GetLengths()[1];
constexpr index_t Wo = out_n_ho_wo_k_global_desc.GetLengths()[2];
constexpr index_t K = out_n_ho_wo_k_global_desc.GetLengths()[3];
constexpr index_t Y = wei_k_y_x_c_global_desc.GetLengths()[1];
constexpr index_t X = wei_k_y_x_c_global_desc.GetLengths()[2];
constexpr index_t ConvStrideH = ConvStrides{}[0];
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t YTilda = ConvStrideH / GcdStrideDilationH;
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
constexpr index_t YDot = math::integer_divide_ceil(Y, YTilda);
constexpr index_t XDot = math::integer_divide_ceil(X, XTilda);
constexpr index_t YDotSlice = (iYTilda + 1) * YDot <= Y ? YDot : Y % YDot;
constexpr index_t XDotSlice = (iXTilda + 1) * XDot <= X ? XDot : X % XDot;
constexpr index_t HTilda =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - 1), ConvStrideH);
constexpr index_t WTilda =
Wo + math::integer_divide_ceil(ConvDilationW * (X - 1), ConvStrideW);
// only work on HTilda and WTilda that contribute to non-padding area of input tensor
constexpr index_t iHTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[0] - ConvDilationH * (YTilda - 1)), ConvStrides{}[0]);
constexpr index_t iWTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[1] - ConvDilationW * (XTilda - 1)), ConvStrides{}[1]);
constexpr index_t iHTildaRight = math::min(
HTilda, math::integer_divide_ceil(InLeftPads{}[0] + Hi - 1, ConvStrides{}[0]) + 1);
constexpr index_t iWTildaRight = math::min(
WTilda, math::integer_divide_ceil(InLeftPads{}[1] + Wi - 1, ConvStrides{}[1]) + 1);
constexpr index_t HTildaSlice = iHTildaRight - iHTildaLeft;
constexpr index_t WTildaSlice = iWTildaRight - iWTildaLeft;
// A matrix: weight
// weight out-of-bound check can be skipped
constexpr bool wei_skip_out_of_bound_check = true;
constexpr auto wei_k_ydot_ytilda_xdot_xtilda_c_global_desc = transform_tensor_descriptor(
wei_k_y_x_c_global_desc,
make_tuple(PassThrough<K>{},
Embed<Y,
Sequence<YDot, YTilda>,
Sequence<ConvStrideH / GcdStrideDilationH, 1, 0>,
wei_skip_out_of_bound_check>{},
Embed<X,
Sequence<XDot, XTilda>,
Sequence<ConvStrideW / GcdStrideDilationW, 1, 0>,
wei_skip_out_of_bound_check>{},
PassThrough<C>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
constexpr auto wei_k_ydotslice_xdotslice_c_global_desc = transform_tensor_descriptor(
wei_k_ydot_ytilda_xdot_xtilda_c_global_desc,
make_tuple(
PassThrough<K>{},
Slice<Sequence<YDot, XDot>, Sequence<0, 0>, Sequence<YDotSlice, XDotSlice>>{},
Freeze<Sequence<YTilda, XTilda>, Sequence<iYTilda, iXTilda>>{},
PassThrough<C>{}),
make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}, Sequence<5>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<>{}, Sequence<3>{}));
constexpr auto wei_gemmk0_gemmk1_gemmk2_gemmm_global_desc =
reorder_tensor_descriptor_given_lower2upper(wei_k_ydotslice_xdotslice_c_global_desc,
Sequence<2, 0, 1, 3>{});
// B matrix: output tensor
// TODO sometimes output tensor out-of-bound check can be skipped, find out all such
// situations
#if !CK_EXPERIMENTAL_IMPLICIT_GEMM_BACKWARD_DATA_V4R1_OUTPUT_SKIP_OUT_OF_BOUND_CHECK
constexpr bool out_skip_out_of_bound_check = false;
#else
constexpr bool out_skip_out_of_bound_check = true;
#endif
constexpr auto out_n_ydot_htilda_xdot_wtilda_k_global_desc = transform_tensor_descriptor(
out_n_ho_wo_k_global_desc,
make_tuple(PassThrough<N>{},
Embed<Ho,
Sequence<YDot, HTilda>,
Sequence<-ConvDilationH / GcdStrideDilationH, 1, 0>,
out_skip_out_of_bound_check>{},
Embed<Wo,
Sequence<XDot, WTilda>,
Sequence<-ConvDilationW / GcdStrideDilationW, 1, 0>,
out_skip_out_of_bound_check>{},
PassThrough<K>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
constexpr auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k_global_desc =
transform_tensor_descriptor(
out_n_ydot_htilda_xdot_wtilda_k_global_desc,
make_tuple(
PassThrough<N>{},
Slice<Sequence<YDot, XDot>, Sequence<0, 0>, Sequence<YDotSlice, XDotSlice>>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<iHTildaLeft, iWTildaLeft>,
Sequence<iHTildaRight, iWTildaRight>>{},
PassThrough<K>{}),
make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}, Sequence<5>{}),
make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}, Sequence<5>{}));
constexpr auto out_gemmk0_gemmk1_gemmk2_gemmn_global_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k_global_desc,
make_tuple(PassThrough<YDotSlice>{},
PassThrough<XDotSlice>{},
PassThrough<K>{},
Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<1>{}, Sequence<3>{}, Sequence<5>{}, Sequence<0, 2, 4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// C matrix: input tensor
// TODO sometimes input out-of-bound check can be skipped, find out all such situations
#if !CK_EXPERIMENTAL_IMPLICIT_GEMM_BACKWARD_DATA_V4R1_INPUT_SKIP_OUT_OF_BOUND_CHECK
constexpr bool in_skip_out_of_bound_check = false;
#else
constexpr bool in_skip_out_of_bound_check = true;
#endif
constexpr auto in_n_hip_wip_c_global_desc = transform_tensor_descriptor(
in_n_hi_wi_c_global_desc,
make_tuple(PassThrough<N>{},
Pad<Sequence<Hi, Wi>, InLeftPads, InRightPads, in_skip_out_of_bound_check>{},
PassThrough<C>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
constexpr index_t Hip = in_n_hip_wip_c_global_desc.GetLengths()[1];
constexpr index_t Wip = in_n_hip_wip_c_global_desc.GetLengths()[2];
constexpr auto in_n_ytilda_htilda_xtilda_wtilda_c_global_desc = transform_tensor_descriptor(
in_n_hip_wip_c_global_desc,
make_tuple(PassThrough<N>{},
Embed<Hip,
Sequence<YTilda, HTilda>,
Sequence<ConvDilationH, ConvStrideH, 0>,
in_skip_out_of_bound_check>{},
Embed<Wip,
Sequence<XTilda, WTilda>,
Sequence<ConvDilationW, ConvStrideW, 0>,
in_skip_out_of_bound_check>{},
PassThrough<C>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
constexpr auto in_n_htildaslice_wtildaslice_c_global_desc = transform_tensor_descriptor(
in_n_ytilda_htilda_xtilda_wtilda_c_global_desc,
make_tuple(PassThrough<N>{},
Freeze<Sequence<YTilda, XTilda>, Sequence<iYTilda, iXTilda>>{},
Slice<Sequence<HTilda, WTilda>,
Sequence<iHTildaLeft, iWTildaLeft>,
Sequence<iHTildaRight, iWTildaRight>>{},
PassThrough<C>{}),
make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}, Sequence<5>{}),
make_tuple(Sequence<0>{}, Sequence<>{}, Sequence<1, 2>{}, Sequence<3>{}));
constexpr auto in_gemmm_gemmn_global_desc = transform_tensor_descriptor(
in_n_htildaslice_wtildaslice_c_global_desc,
make_tuple(PassThrough<C>{}, Merge<Sequence<N, HTildaSlice, WTildaSlice>>{}),
make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// call GEMM
constexpr auto gridwise_gemm = GridwiseGemmTransposedANormalBNormalC_v2<
GridSize,
BlockSize,
Float,
AccFloat,
decltype(wei_gemmk0_gemmk1_gemmk2_gemmm_global_desc),
decltype(out_gemmk0_gemmk1_gemmk2_gemmn_global_desc),
decltype(in_gemmm_gemmn_global_desc),
InMemoryDataOperation::Set,
GemmMPerBlock,
GemmNPerBlock,
GemmKPerBlock,
GemmMPerThread,
GemmNPerThread,
GemmKPerThread,
GemmMLevel0Cluster,
GemmNLevel0Cluster,
GemmMLevel1Cluster,
GemmNLevel1Cluster,
ThreadGemmDataPerRead_GemmM,
ThreadGemmDataPerRead_GemmN,
GemmABlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmM,
GemmABlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmM,
Sequence<0, 1, 2, 3>,
Sequence<0, 1, 2, 3>,
3,
GemmABlockCopySrcDataPerRead_GemmM,
GemmABlockCopyDstDataPerWrite_GemmM,
GemmBBlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmN,
GemmBBlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmN,
Sequence<0, 1, 3, 2>,
Sequence<0, 1, 3, 2>,
2,
GemmBBlockCopySrcDataPerRead_GemmK2,
GemmBBlockCopyDstDataPerWrite_GemmN,
Sequence<2, 3, 0, 1>,
3,
GemmCThreadCopyDstDataPerWrite_GemmN1>{};
gridwise_gemm.Run(p_wei_global, p_out_global, p_in_global);
}
template <index_t GemmId>
__device__ static void Run(Float* __restrict__ p_in_global,
const Float* __restrict__ p_wei_global,
const Float* __restrict__ p_out_global,
Number<GemmId>)
{
constexpr index_t ConvStrideH = ConvStrides{}[0];
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t YTilda = ConvStrideH / GcdStrideDilationH;
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
constexpr index_t iYTilda = GemmId / XTilda;
constexpr index_t iXTilda = GemmId % XTilda;
static_assert(iYTilda < YTilda && iXTilda < XTilda, "wrong! iYtilda, iXtilda");
RunImpl<iYTilda, iXTilda>(p_in_global, p_wei_global, p_out_global);
}
};
} // namespace ck
#endif
composable_kernel/include/tensor_description/multi_index_transform.hpp
View file @ bbcb67d0
......@@ -488,6 +488,49 @@ struct Embed
}
};
// LowerLengths: Sequence<...>
// LowerFreezePoint: Sequence<...>
template <typename LowerLengths, typename LowerFreezePoint>
struct Freeze
{
static constexpr index_t nDimLow = LowerLengths::Size();
static constexpr index_t nDimUp = 0;
using LowerIndex = MultiIndex<nDimLow>;
using UpperIndex = MultiIndex<nDimUp>;
__host__ __device__ explicit constexpr Freeze()
{
// TODO: sanity check: LowerFreezePoint should be within range of LowerLengths
}
__host__ __device__ static constexpr auto GetNumOfLowerDimension() { return Number<nDimLow>{}; }
__host__ __device__ static constexpr auto GetNumOfUpperDimension() { return Number<0>{}; }
__host__ __device__ static constexpr auto GetUpperLengths() { return Sequence<>{}; }
__host__ __device__ static constexpr auto CalculateLowerIndex(const UpperIndex& /*idx_up*/)
{
return to_array(LowerFreezePoint{});
}
__host__ __device__ static constexpr auto
CalculateLowerIndexDiff(const UpperIndex& /* idx_up_diff */,
const UpperIndex& /* idx_up_old */,
const LowerIndex& /* idx_low_old */)
{
return make_zero_array<index_t, nDimLow>();
}
__host__ __device__ static constexpr bool IsLinearTransform() { return true; }
__host__ __device__ static constexpr bool IsValidUpperIndexAlwaysMappedToValidLowerIndex()
{
return true;
}
};
template <index_t LowerLength, index_t VectorSize>
struct Vectorize
{
......
composable_kernel/include/tensor_operation/gridwise_gemm.hpp
View file @ bbcb67d0
......@@ -376,5 +376,400 @@ struct GridwiseGemmTransposedANormalBNormalC_v1
}
};
template <index_t GridSize,
index_t BlockSize,
typename Float,
typename AccFloat,
typename AGlobalDesc,
typename BGlobalDesc,
typename CGlobalDesc,
InMemoryDataOperation CGlobalMemoryDataOperation,
index_t MPerBlock,
index_t NPerBlock,
index_t KPerBlock,
index_t MPerThread,
index_t NPerThread,
index_t KPerThread,
index_t MLevel0Cluster,
index_t NLevel0Cluster,
index_t MLevel1Cluster,
index_t NLevel1Cluster,
index_t ThreadGemmAThreadCopySrcDataPerRead_M,
index_t ThreadGemmBThreadCopySrcDataPerRead_N,
typename ABlockCopyThreadSliceLengths_K0_K1_K2_M,
typename ABlockCopyThreadClusterLengths_K0_K1_K2_M,
typename ABlockCopyThreadClusterArrangeOrder,
typename ABlockCopySrcAccessOrder,
index_t ABlockCopySrcVectorReadDim,
index_t ABlockCopySrcDataPerRead,
index_t ABlockCopyDstDataPerWrite_M,
typename BBlockCopyThreadSliceLengths_K0_K1_K2_N,
typename BBlockCopyThreadClusterLengths_K0_K1_K2_N,
typename BBlockCopyThreadClusterArrangeOrder,
typename BBlockCopySrcAccessOrder,
index_t BBlockCopySrcVectorReadDim,
index_t BBlockCopySrcDataPerRead,
index_t BBlockCopyDstDataPerWrite_N,
typename CThreadCopySrcDstAccessOrder,
index_t CThreadCopySrcDstVectorReadWriteDim,
index_t CThreadCopyDstDataPerWrite>
struct GridwiseGemmTransposedANormalBNormalC_v2
{
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
constexpr index_t max_lds_align = math::lcm(ABlockCopyDstDataPerWrite_M,
BBlockCopyDstDataPerWrite_N,
ThreadGemmAThreadCopySrcDataPerRead_M,
ThreadGemmBThreadCopySrcDataPerRead_N);
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k_m_block_desc = make_native_tensor_descriptor_aligned(
Sequence<KPerBlock, MPerBlock>{}, Number<max_lds_align>{});
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k_n_block_desc = make_native_tensor_descriptor_aligned(
Sequence<KPerBlock, NPerBlock>{}, Number<max_lds_align>{});
// LDS allocation for A and B: be careful of alignment
constexpr index_t a_block_space =
math::integer_least_multiple(a_k_m_block_desc.GetElementSpace(), max_lds_align);
constexpr index_t b_block_space =
math::integer_least_multiple(b_k_n_block_desc.GetElementSpace(), max_lds_align);
return 2 * (a_block_space + b_block_space) * sizeof(Float);
}
__device__ void Run(const Float* __restrict__ p_a_global,
const Float* __restrict__ p_b_global,
Float* __restrict__ p_c_global,
Float* __restrict__ p_shared_block) const
{
constexpr auto True = integral_constant<bool, true>{};
constexpr auto False = integral_constant<bool, false>{};
constexpr auto I0 = Number<0>{};
constexpr auto I2 = Number<2>{};
constexpr auto a_k0_k1_k2_m_global_desc = AGlobalDesc{};
constexpr auto b_k0_k1_k2_n_global_desc = BGlobalDesc{};
constexpr auto c_m_n_global_desc = CGlobalDesc{};
constexpr auto K0 = a_k0_k1_k2_m_global_desc.GetLengths()[0];
constexpr auto K1 = a_k0_k1_k2_m_global_desc.GetLengths()[1];
constexpr auto K = a_k0_k1_k2_m_global_desc.GetLengths()[2];
constexpr auto M = c_m_n_global_desc.GetLengths()[0];
constexpr auto N = c_m_n_global_desc.GetLengths()[1];
// don't do anything if K == 0
if(K == 0)
{
return;
}
// lds max alignment
constexpr index_t max_lds_align = math::lcm(ABlockCopyDstDataPerWrite_M,
BBlockCopyDstDataPerWrite_N,
ThreadGemmAThreadCopySrcDataPerRead_M,
ThreadGemmBThreadCopySrcDataPerRead_N);
// divide block work by [M, N]
static_assert(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0,
"wrong! cannot divide work evenly among block");
constexpr index_t MBlockWork = M / MPerBlock;
constexpr index_t NBlockWork = N / NPerBlock;
constexpr auto block_work_desc =
make_cluster_descriptor(Sequence<MBlockWork, NBlockWork>{});
const auto block_work_id = block_work_desc.CalculateClusterIndex(get_block_1d_id());
const index_t m_block_data_on_global = block_work_id[0] * MPerBlock;
const index_t n_block_data_on_global = block_work_id[1] * NPerBlock;
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k0_k1_k2_m_block_desc = make_native_tensor_descriptor_aligned(
Sequence<1, 1, KPerBlock, MPerBlock>{}, Number<max_lds_align>{});
// A matrix blockwise copy
auto a_blockwise_copy =
BlockwiseGenericTensorSliceCopy_v4<BlockSize,
decltype(a_k0_k1_k2_m_global_desc),
decltype(a_k0_k1_k2_m_block_desc),
decltype(a_k0_k1_k2_m_block_desc.GetLengths()),
ABlockCopyThreadSliceLengths_K0_K1_K2_M,
ABlockCopyThreadClusterLengths_K0_K1_K2_M,
ABlockCopyThreadClusterArrangeOrder,
ABlockCopySrcAccessOrder,
Sequence<0, 1, 2, 3>,
ABlockCopySrcVectorReadDim,
3,
ABlockCopySrcDataPerRead,
ABlockCopyDstDataPerWrite_M,
AddressSpace::Global,
AddressSpace::Vgpr,
AddressSpace::Lds,
InMemoryDataOperation::Set>(
{0, 0, 0, m_block_data_on_global}, {0, 0, 0, 0});
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k0_k1_k2_n_block_desc = make_native_tensor_descriptor_aligned(
Sequence<1, 1, KPerBlock, NPerBlock>{}, Number<max_lds_align>{});
// B matrix blockwise copy
auto b_blockwise_copy =
BlockwiseGenericTensorSliceCopy_v4<BlockSize,
decltype(b_k0_k1_k2_n_global_desc),
decltype(b_k0_k1_k2_n_block_desc),
decltype(b_k0_k1_k2_n_block_desc.GetLengths()),
BBlockCopyThreadSliceLengths_K0_K1_K2_N,
BBlockCopyThreadClusterLengths_K0_K1_K2_N,
BBlockCopyThreadClusterArrangeOrder,
BBlockCopySrcAccessOrder,
Sequence<0, 1, 2, 3>,
BBlockCopySrcVectorReadDim,
3,
BBlockCopySrcDataPerRead,
BBlockCopyDstDataPerWrite_N,
AddressSpace::Global,
AddressSpace::Vgpr,
AddressSpace::Lds,
InMemoryDataOperation::Set>(
{0, 0, 0, n_block_data_on_global}, {0, 0, 0, 0});
// GEMM definition
// c_mtx += transpose(a_mtx) * b_mtx
// a_mtx[KPerBlock, MPerBlock] is in LDS
// b_mtx[KPerBlocl, NPerBlock] is in LDS
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
constexpr auto a_k_m_block_mtx_desc = make_ConstantMatrixDescriptor(
unfold_tensor_descriptor(a_k0_k1_k2_m_block_desc, I0, I2));
constexpr auto b_k_n_block_mtx_desc = make_ConstantMatrixDescriptor(
unfold_tensor_descriptor(b_k0_k1_k2_n_block_desc, I0, I2));
// sanity check
static_assert(MPerBlock % (MPerThread * MLevel0Cluster * MLevel1Cluster) == 0 &&
NPerBlock % (NPerThread * NLevel0Cluster * NLevel1Cluster) == 0,
"wrong!");
constexpr index_t GemmMRepeat = MPerBlock / (MPerThread * MLevel0Cluster * MLevel1Cluster);
constexpr index_t GemmNRepeat = NPerBlock / (NPerThread * NLevel0Cluster * NLevel1Cluster);
// c_thread_mtx definition: this is a mess
// TODO:: more elegent way of defining c_thread_mtx
constexpr auto c_m0m1_n0n1_thread_mtx_desc = make_ConstantMatrixDescriptor_packed(
Number<GemmMRepeat * MPerThread>{}, Number<GemmNRepeat * NPerThread>{});
const auto blockwise_gemm = BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2<
BlockSize,
decltype(a_k_m_block_mtx_desc),
decltype(b_k_n_block_mtx_desc),
decltype(c_m0m1_n0n1_thread_mtx_desc),
MPerThread,
NPerThread,
KPerThread,
MLevel0Cluster,
NLevel0Cluster,
MLevel1Cluster,
NLevel1Cluster,
ThreadGemmAThreadCopySrcDataPerRead_M,
ThreadGemmBThreadCopySrcDataPerRead_N>{};
// LDS allocation for A and B: be careful of alignment
constexpr index_t a_block_space =
math::integer_least_multiple(a_k0_k1_k2_m_block_desc.GetElementSpace(), max_lds_align);
constexpr index_t b_block_space =
math::integer_least_multiple(b_k0_k1_k2_n_block_desc.GetElementSpace(), max_lds_align);
Float* p_a_block_double = p_shared_block;
Float* p_b_block_double = p_shared_block + 2 * a_block_space;
// register allocation for output
AccFloat p_c_thread[c_m0m1_n0n1_thread_mtx_desc.GetElementSpace()];
// zero out threadwise output
threadwise_matrix_set_zero(c_m0m1_n0n1_thread_mtx_desc, p_c_thread);
for(index_t k0 = 0; k0 < K0; ++k0)
{
for(index_t k1 = 0; k1 < K1; ++k1)
{
// LDS double buffer: preload data into LDS
{
a_blockwise_copy.Run(p_a_global, p_a_block_double);
b_blockwise_copy.Run(p_b_global, p_b_block_double);
}
constexpr auto a_block_slice_copy_steps = Sequence<0, 0, KPerBlock, 0>{};
constexpr auto b_block_slice_copy_steps = Sequence<0, 0, KPerBlock, 0>{};
// LDS double buffer: main body
for(index_t k_block_data_begin = 0; k_block_data_begin + 2 * KPerBlock < K;
k_block_data_begin += 2 * KPerBlock)
{
#pragma unroll
for(index_t iloop = 0; iloop < 2; ++iloop)
{
const bool even_loop = (iloop % 2 == 0);
Float* p_a_block_now =
even_loop ? p_a_block_double : p_a_block_double + a_block_space;
Float* p_b_block_now =
even_loop ? p_b_block_double : p_b_block_double + b_block_space;
Float* p_a_block_next =
even_loop ? p_a_block_double + a_block_space : p_a_block_double;
Float* p_b_block_next =
even_loop ? p_b_block_double + b_block_space : p_b_block_double;
Float p_a_thread_buffer[a_blockwise_copy.GetThreadBufferSize()];
Float p_b_thread_buffer[b_blockwise_copy.GetThreadBufferSize()];
a_blockwise_copy.MoveSrcSliceWindow(a_block_slice_copy_steps, True);
b_blockwise_copy.MoveSrcSliceWindow(b_block_slice_copy_steps, True);
__syncthreads();
// LDS doubel buffer: load next data from device mem
a_blockwise_copy.RunLoadThreadBuffer(p_a_global, p_a_thread_buffer);
b_blockwise_copy.RunLoadThreadBuffer(p_b_global, p_b_thread_buffer);
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(p_a_block_now, p_b_block_now, p_c_thread);
// LDS double buffer: store next data to LDS
a_blockwise_copy.RunStoreThreadBuffer(p_a_thread_buffer, p_a_block_next);
b_blockwise_copy.RunStoreThreadBuffer(p_b_thread_buffer, p_b_block_next);
}
}
// LDS double buffer: tail
{
constexpr bool has_two_iteration_left = (K % (2 * KPerBlock) == 0);
if(has_two_iteration_left) // if has 2 iteration left
{
Float p_a_thread_buffer[a_blockwise_copy.GetThreadBufferSize()];
Float p_b_thread_buffer[b_blockwise_copy.GetThreadBufferSize()];
a_blockwise_copy.MoveSrcSliceWindow(a_block_slice_copy_steps, True);
b_blockwise_copy.MoveSrcSliceWindow(b_block_slice_copy_steps, True);
__syncthreads();
// LDS double buffer: load last data from device mem
a_blockwise_copy.RunLoadThreadBuffer(p_a_global, p_a_thread_buffer);
b_blockwise_copy.RunLoadThreadBuffer(p_b_global, p_b_thread_buffer);
// LDS double buffer: GEMM on 2nd-last data
blockwise_gemm.Run(p_a_block_double, p_b_block_double, p_c_thread);
// LDS double buffer: store last data to LDS
a_blockwise_copy.RunStoreThreadBuffer(p_a_thread_buffer,
p_a_block_double + a_block_space);
b_blockwise_copy.RunStoreThreadBuffer(p_b_thread_buffer,
p_b_block_double + b_block_space);
__syncthreads();
// LDS double buffer: GEMM on last data
blockwise_gemm.Run(p_a_block_double + a_block_space,
p_b_block_double + b_block_space,
p_c_thread);
}
else // if has 1 iteration left
{
__syncthreads();
// LDS double buffer: GEMM on last data
blockwise_gemm.Run(p_a_block_double, p_b_block_double, p_c_thread);
}
}
// reset slice windoww on K2 dimension, then move forward on K1 dimension
a_blockwise_copy.MoveSrcSliceWindow(Sequence<0, 0, K - KPerBlock, 0>{}, False);
b_blockwise_copy.MoveSrcSliceWindow(Sequence<0, 0, K - KPerBlock, 0>{}, False);
a_blockwise_copy.MoveSrcSliceWindow(Sequence<0, 1, 0, 0>{}, True);
b_blockwise_copy.MoveSrcSliceWindow(Sequence<0, 1, 0, 0>{}, True);
}
// reset slice windoww on K1 dimension, then move forward on K0 dimension
a_blockwise_copy.MoveSrcSliceWindow(Sequence<0, K1, 0, 0>{}, False);
b_blockwise_copy.MoveSrcSliceWindow(Sequence<0, K1, 0, 0>{}, False);
a_blockwise_copy.MoveSrcSliceWindow(Sequence<1, 0, 0, 0>{}, True);
b_blockwise_copy.MoveSrcSliceWindow(Sequence<1, 0, 0, 0>{}, True);
}
// input: register to global memory
{
constexpr index_t M1 = MPerThread * MLevel0Cluster * MLevel1Cluster;
constexpr index_t M0 = M / M1;
constexpr index_t N1 = NPerThread * NLevel0Cluster * NLevel1Cluster;
constexpr index_t N0 = N / N1;
// define input tensor descriptor for threadwise copy
// thread input tensor, src of threadwise copy
constexpr auto c_m0_m1_n0_n1_thread_desc = make_native_tensor_descriptor_packed(
Sequence<GemmMRepeat, MPerThread, GemmNRepeat, NPerThread>{});
constexpr auto c_m0_m1_n0_n1_global_desc = transform_tensor_descriptor(
c_m_n_global_desc,
make_tuple(UnMerge<Sequence<M0, M1>>{}, UnMerge<Sequence<N0, N1>>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
// calculate origin of thread input tensor on global memory
// blockwise GEMM c matrix starting index
const auto c_thread_mtx_on_block =
blockwise_gemm.GetBeginOfThreadMatrixC(get_thread_local_1d_id());
const index_t m_thread_data_on_global =
m_block_data_on_global + c_thread_mtx_on_block.row;
const index_t n_thread_data_on_global =
n_block_data_on_global + c_thread_mtx_on_block.col;
ThreadwiseGenericTensorSliceCopy_v4r2<decltype(c_m0_m1_n0_n1_thread_desc),
decltype(c_m0_m1_n0_n1_global_desc),
decltype(c_m0_m1_n0_n1_thread_desc.GetLengths()),
CThreadCopySrcDstAccessOrder,
CThreadCopySrcDstVectorReadWriteDim,
1,
CThreadCopyDstDataPerWrite,
AddressSpace::Vgpr,
AddressSpace::Global,
CGlobalMemoryDataOperation>(
{0, 0, 0, 0},
{m_thread_data_on_global / M1,
m_thread_data_on_global % M1,
n_thread_data_on_global / N1,
n_thread_data_on_global % N1})
.Run(p_c_thread, p_c_global);
}
}
__device__ void Run(const Float* __restrict__ p_a_global,
const Float* __restrict__ p_b_global,
Float* __restrict__ p_c_global) const
{
constexpr index_t shared_block_size = GetSharedMemoryNumberOfByte() / sizeof(Float);
__shared__ Float p_shared_block[shared_block_size];
Run(p_a_global, p_b_global, p_c_global, p_shared_block);
}
};
} // namespace ck
#endif
composable_kernel/include/utility/array.hpp
View file @ bbcb67d0
......@@ -12,6 +12,7 @@ struct Array
using type = Array<TData, NSize>;
using data_type = TData;
// TODO: implement empty Array
index_t mData[NSize];
__host__ __device__ explicit constexpr Array() {}
......
composable_kernel/include/utility/common_header.hpp
View file @ bbcb67d0
......@@ -24,6 +24,7 @@
#if CK_USE_AMD_XDLOPS
#include "amd_xdlops.hpp"
#include "amd_xdlops_inline_asm.hpp"
#endif
#endif
composable_kernel/include/utility/in_memory_operation.amd.hpp.in
View file @ bbcb67d0
......@@ -108,8 +108,12 @@ struct SetData
{
const auto zeros = vector_t(0);
amd_buffer_store<T, DataPerAccess>(
src_valid ? &(p_src[src_offset]) : &zeros, p_dst, dst_offset, dst_valid, dst_range);
amd_buffer_store<T, DataPerAccess>(src_valid ? &(p_src[src_offset])
: reinterpret_cast<const T*>(&zeros),
p_dst,
dst_offset,
dst_valid,
dst_range);
}
#endif
};
......@@ -145,19 +149,17 @@ struct AtomicAddData
template <>
__device__ void Run<AddressSpace::Vgpr, AddressSpace::Global>(const T* p_src,
index_t src_offset,
bool src_valid,
index_t /* src_range */,
bool src_valid T* p_dst,
T* p_dst,
index_t dst_offset,
bool dst_valid,
index_t dst_range) const
{
const auto zeros = vector_t(0);
amd_buffer_atomic_add<T, DataPerAccess>(src_valid ? &(p_src[src_offset]) : &zeros,
p_dst,
dst_offset,
dst_valid,
index_t dst_range);
amd_buffer_atomic_add<T, DataPerAccess>(
src_valid ? &(p_src[src_offset]) : &zeros, p_dst, dst_offset, dst_valid, dst_range);
}
#endif
};
......
driver/CMakeLists.txt
View file @ bbcb67d0
......@@ -16,15 +16,14 @@ install(TARGETS host LIBRARY DESTINATION lib)
if(DEVICE_BACKEND STREQUAL "AMD")
set(CONV_SOURCE src/conv_driver.cpp)
set(COL2IM_SOURCE src/col2im_driver.cpp)
set(CONV_BWD_DATA_SOURCE src/conv_bwd_data_driver.cpp)
elseif(DEVICE_BACKEND STREQUAL "NVIDIA")
set(CONV_SOURCE src/conv_driver.cu)
set(COL2IM_SOURCE src/col2im_driver.cu)
set(CONV_BWD_DATA_SOURCE src/conv_bwd_data_driver.cu)
endif()
add_executable(conv_driver ${CONV_SOURCE})
add_executable(conv_bwd_data_driver ${CONV_BWD_DATA_SOURCE})
target_link_libraries(conv_driver PRIVATE host)
target_link_libraries(conv_bwd_data_driver PRIVATE host)
driver/include/device_convolution_backward_data_implicit_gemm_v3r1_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ ac62d13e
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "gridwise_operation_wrapper.hpp"
#include "gridwise_convolution_backward_data_implicit_gemm_v3r1_nchw_kcyx_nkhw.hpp"
namespace launcher {
using namespace ck;
template <typename T,
typename InDesc,
typename WeiDesc,
typename OutDesc,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_backward_data_implicit_gemm_v3r1_nchw_kcyx_nkhw(InDesc in_nchw_desc,
Tensor<T>& in_nchw,
WeiDesc wei_kcyx_desc,
const Tensor<T>& wei_kcyx,
OutDesc out_nkhw_desc,
const Tensor<T>& out_nkhw,
ConvStrides,
ConvDilations,
InLeftPads,
InRightPads,
std::size_t nrepeat)
{
using namespace ck;
constexpr index_t N = out_nkhw_desc.GetLengths()[0];
constexpr index_t K = out_nkhw_desc.GetLengths()[1];
constexpr index_t C = wei_kcyx_desc.GetLengths()[1];
constexpr index_t Hi = in_nchw_desc.GetLengths()[2];
constexpr index_t Wi = in_nchw_desc.GetLengths()[3];
constexpr index_t Ho = out_nkhw_desc.GetLengths()[2];
constexpr index_t Wo = out_nkhw_desc.GetLengths()[3];
constexpr index_t Y = wei_kcyx_desc.GetLengths()[2];
constexpr index_t X = wei_kcyx_desc.GetLengths()[3];
constexpr index_t ConvStrideH = ConvStrides{}[0];
constexpr index_t ConvStrideW = ConvStrides{}[1];
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
std::size_t data_sz = sizeof(T);
DeviceMem in_nchw_device_buf(data_sz * in_nchw.mDesc.GetElementSpace());
DeviceMem wei_kcyx_device_buf(data_sz * wei_kcyx.mDesc.GetElementSpace());
DeviceMem out_nkhw_device_buf(data_sz * out_nkhw.mDesc.GetElementSpace());
in_nchw_device_buf.ToDevice(in_nchw.mData.data());
wei_kcyx_device_buf.ToDevice(wei_kcyx.mData.data());
out_nkhw_device_buf.ToDevice(out_nkhw.mData.data());
#if 1
// BlockSize = 256, each thread hold 64 data
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 8;
constexpr index_t GemmMPerThread = 4;
constexpr index_t GemmNPerThread = 4;
constexpr index_t GemmKPerThread = 1;
constexpr index_t GemmMLevel0Cluster = 4;
constexpr index_t GemmNLevel0Cluster = 4;
constexpr index_t GemmMLevel1Cluster = 4;
constexpr index_t GemmNLevel1Cluster = 4;
constexpr index_t GemmThreadGemmDataPerReadM = 4;
constexpr index_t GemmThreadGemmDataPerReadN = 4;
using GemmABlockCopyThreadSliceLengths_GemmK_GemmM = Sequence<4, 1>;
using GemmABlockCopyThreadClusterLengths_GemmK_GemmM = Sequence<2, 128>;
constexpr index_t GemmABlockCopySrcDataPerRead_GemmM = 1;
constexpr index_t GemmABlockCopyDstDataPerWrite_GemmM = 1;
using GemmBBlockCopyThreadSliceLengths_GemmK_GemmN = Sequence<4, 1>;
using GemmBBlockCopyThreadClusterLengths_GemmK_GemmN = Sequence<2, 128>;
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmN = 1;
constexpr index_t GemmBBlockCopyDstDataPerWrite_GemmN = 1;
constexpr index_t GemmCThreadCopyDstDataPerWrite_GemmN1 = 1;
#endif
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
constexpr index_t GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
constexpr index_t YTilda = ConvStrideH / GcdStrideDilationH;
constexpr index_t XTilda = ConvStrideW / GcdStrideDilationW;
constexpr index_t YDot = math::integer_divide_ceil(Y, YTilda);
constexpr index_t XDot = math::integer_divide_ceil(X, XTilda);
constexpr index_t HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - 1), ConvStrideH);
constexpr index_t WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - 1), ConvStrideW);
constexpr index_t HTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[0] - ConvDilationH * (YTilda - 1)), ConvStrides{}[0]);
constexpr index_t WTildaLeft = math::integer_divide_floor(
math::max(0, InLeftPads{}[1] - ConvDilationW * (XTilda - 1)), ConvStrides{}[1]);
constexpr index_t HTildaRight = math::min(
HTilda, math::integer_divide_ceil(InLeftPads{}[0] + Hi - 1, ConvStrides{}[0]) + 1);
constexpr index_t WTildaRight = math::min(
WTilda, math::integer_divide_ceil(InLeftPads{}[1] + Wi - 1, ConvStrides{}[1]) + 1);
constexpr index_t HTildaSlice = HTildaRight - HTildaLeft;
constexpr index_t WTildaSlice = WTildaRight - WTildaLeft;
constexpr index_t GemmM = C;
constexpr index_t GemmN = N * HTildaSlice * WTildaSlice;
constexpr index_t GridSize = math::integer_divide_ceil(GemmM, GemmMPerBlock) *
math::integer_divide_ceil(GemmN, GemmNPerBlock);
printf("%s: BlockSize %u, GridSize %u \n", __func__, BlockSize, GridSize);
using gridwise_conv_bwd_data = GridwiseConvolutionBackwardDataImplicitGemm_v3r1_nchw_kcyx_nkhw<
GridSize,
BlockSize,
T,
T,
decltype(in_nchw_desc),
decltype(wei_kcyx_desc),
decltype(out_nkhw_desc),
ConvStrides,
ConvDilations,
InLeftPads,
InRightPads,
GemmMPerBlock,
GemmNPerBlock,
GemmKPerBlock,
GemmMPerThread,
GemmNPerThread,
GemmKPerThread,
GemmMLevel0Cluster,
GemmNLevel0Cluster,
GemmMLevel1Cluster,
GemmNLevel1Cluster,
GemmThreadGemmDataPerReadM,
GemmThreadGemmDataPerReadN,
GemmABlockCopyThreadSliceLengths_GemmK_GemmM,
GemmABlockCopyThreadClusterLengths_GemmK_GemmM,
GemmABlockCopySrcDataPerRead_GemmM,
GemmABlockCopyDstDataPerWrite_GemmM,
GemmBBlockCopyThreadSliceLengths_GemmK_GemmN,
GemmBBlockCopyThreadClusterLengths_GemmK_GemmN,
GemmBBlockCopySrcDataPerRead_GemmN,
GemmBBlockCopyDstDataPerWrite_GemmN,
GemmCThreadCopyDstDataPerWrite_GemmN1>;
for(index_t i = 0; i < 5; ++i)
{
std::cout << "Start running " << nrepeat << " times..." << std::endl;
KernelTimer timer;
timer.Start();
for(index_t j = 0; j < nrepeat; ++j)
{
launch_kernel(run_gridwise_operation<gridwise_conv_bwd_data,
T* const __restrict__,
const T* const __restrict__,
const T* const __restrict__>,
dim3(GridSize),
dim3(BlockSize),
0,
0,
static_cast<T*>(in_nchw_device_buf.GetDeviceBuffer()),
static_cast<T*>(wei_kcyx_device_buf.GetDeviceBuffer()),
static_cast<T*>(out_nkhw_device_buf.GetDeviceBuffer()));
}
timer.End();
float ave_time = timer.GetElapsedTime() / nrepeat;
float perf = (float)calculate_convolution_flops(InDesc{}, WeiDesc{}, OutDesc{}) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
in_nchw_device_buf.FromDevice(in_nchw.mData.data());
}
} // namespace launcher
driver/include/device_convolution_backward_data_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp
View file @ bbcb67d0
......@@ -57,8 +57,8 @@ void device_convolution_backward_data_implicit_gemm_v4r1_nchw_kcyx_nkhw(InDesc i
wei_kcyx_device_buf.ToDevice(wei_kcyx.mData.data());
out_nkhw_device_buf.ToDevice(out_nkhw.mData.data());
#if 1
// BlockSize = 256, each thread hold 64 data
#if 0
// cdata = 64, BlockSize = 256, 128x128x8
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
......@@ -86,6 +86,36 @@ void device_convolution_backward_data_implicit_gemm_v4r1_nchw_kcyx_nkhw(InDesc i
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmN = 1;
constexpr index_t GemmBBlockCopyDstDataPerWrite_GemmN = 1;
constexpr index_t GemmCThreadCopyDstDataPerWrite_GemmN1 = 1;
#elif 1
// cdata = 64, BlockSize = 256, 128x128x16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 16;
constexpr index_t GemmMPerThread = 4;
constexpr index_t GemmNPerThread = 4;
constexpr index_t GemmKPerThread = 1;
constexpr index_t GemmMLevel0Cluster = 4;
constexpr index_t GemmNLevel0Cluster = 4;
constexpr index_t GemmMLevel1Cluster = 4;
constexpr index_t GemmNLevel1Cluster = 4;
constexpr index_t GemmThreadGemmDataPerReadM = 4;
constexpr index_t GemmThreadGemmDataPerReadN = 4;
using GemmABlockCopyThreadSliceLengths_GemmK_GemmM = Sequence<8, 1>;
using GemmABlockCopyThreadClusterLengths_GemmK_GemmM = Sequence<2, 128>;
constexpr index_t GemmABlockCopySrcDataPerRead_GemmM = 1;
constexpr index_t GemmABlockCopyDstDataPerWrite_GemmM = 1;
using GemmBBlockCopyThreadSliceLengths_GemmK_GemmN = Sequence<8, 1>;
using GemmBBlockCopyThreadClusterLengths_GemmK_GemmN = Sequence<2, 128>;
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmN = 1;
constexpr index_t GemmBBlockCopyDstDataPerWrite_GemmN = 1;
constexpr index_t GemmCThreadCopyDstDataPerWrite_GemmN1 = 1;
#endif
......
driver/include/device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw.hpp → driver/include/device_convolution_backward_data_implicit_gemm_v5r1_nhwc_kyxc_nhwk.hpp
View file @ bbcb67d0
......@@ -3,7 +3,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "gridwise_operation_wrapper.hpp"
#include "gridwise_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw.hpp"
#include "gridwise_convolution_backward_data_implicit_gemm_v5r1_nhwc_kyxc_nhwk.hpp"
namespace launcher {
......@@ -17,7 +17,7 @@ template <typename T,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc in_nchw_desc,
void device_convolution_backward_data_implicit_gemm_v5r1_nhwc_kyxc_nhwk(InDesc in_nchw_desc,
Tensor<T>& in_nchw,
WeiDesc wei_kcyx_desc,
const Tensor<T>& wei_kcyx,
......@@ -29,8 +29,6 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
InRightPads,
std::size_t nrepeat)
{
using namespace ck;
constexpr index_t N = out_nkhw_desc.GetLengths()[0];
constexpr index_t K = out_nkhw_desc.GetLengths()[1];
constexpr index_t C = wei_kcyx_desc.GetLengths()[1];
......@@ -50,47 +48,41 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
constexpr index_t ConvDilationH = ConvDilations{}[0];
constexpr index_t ConvDilationW = ConvDilations{}[1];
std::size_t data_sz = sizeof(T);
DeviceMem in_nchw_device_buf(data_sz * in_nchw.mDesc.GetElementSpace());
DeviceMem wei_kcyx_device_buf(data_sz * wei_kcyx.mDesc.GetElementSpace());
DeviceMem out_nkhw_device_buf(data_sz * out_nkhw.mDesc.GetElementSpace());
constexpr auto in_nhwc_desc = make_native_tensor_descriptor_packed(Sequence<N, Hi, Wi, C>{});
constexpr auto wei_kyxc_desc = make_native_tensor_descriptor_packed(Sequence<K, Y, X, C>{});
constexpr auto out_nhwk_desc = make_native_tensor_descriptor_packed(Sequence<N, Ho, Wo, K>{});
in_nchw_device_buf.ToDevice(in_nchw.mData.data());
wei_kcyx_device_buf.ToDevice(wei_kcyx.mData.data());
out_nkhw_device_buf.ToDevice(out_nkhw.mData.data());
Tensor<float> in_nhwc(make_HostTensorDescriptor(in_nhwc_desc));
Tensor<float> wei_kyxc(make_HostTensorDescriptor(wei_kyxc_desc));
Tensor<float> out_nhwk(make_HostTensorDescriptor(out_nhwk_desc));
#if 1
// BlockSize = 256, each thread hold 64 data
constexpr index_t BlockSize = 256;
auto f_nchw2nhwc = [&](auto n, auto hi, auto wi, auto c) {
in_nhwc(n, hi, wi, c) = in_nchw(n, c, hi, wi);
};
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 8;
constexpr index_t GemmMPerThread = 4;
constexpr index_t GemmNPerThread = 4;
constexpr index_t GemmKPerThread = 1;
constexpr index_t GemmMLevel0Cluster = 4;
constexpr index_t GemmNLevel0Cluster = 4;
constexpr index_t GemmMLevel1Cluster = 4;
constexpr index_t GemmNLevel1Cluster = 4;
constexpr index_t GemmThreadGemmDataPerReadM = 4;
constexpr index_t GemmThreadGemmDataPerReadN = 4;
auto f_kcyx2kyxc = [&](auto k, auto y, auto x, auto c) {
wei_kyxc(k, y, x, c) = wei_kcyx(k, c, y, x);
};
using GemmABlockCopyThreadSliceLengths_GemmK_GemmM = Sequence<4, 1>;
using GemmABlockCopyThreadClusterLengths_GemmK_GemmM = Sequence<2, 128>;
auto f_nkhw2nhwk = [&](auto n, auto ho, auto wo, auto k) {
out_nhwk(n, ho, wo, k) = out_nkhw(n, k, ho, wo);
};
constexpr index_t GemmABlockCopySrcDataPerRead_GemmM = 1;
constexpr index_t GemmABlockCopyDstDataPerWrite_GemmM = 1;
make_ParallelTensorFunctor(f_nchw2nhwc, N, Hi, Wi, C)(std::thread::hardware_concurrency());
make_ParallelTensorFunctor(f_kcyx2kyxc, K, Y, X, C)(std::thread::hardware_concurrency());
make_ParallelTensorFunctor(f_nkhw2nhwk, N, Ho, Wo, K)(std::thread::hardware_concurrency());
using GemmBBlockCopyThreadSliceLengths_GemmK_GemmN = Sequence<4, 1>;
using GemmBBlockCopyThreadClusterLengths_GemmK_GemmN = Sequence<2, 128>;
std::size_t data_sz = sizeof(T);
DeviceMem in_nhwc_device_buf(data_sz * in_nhwc.mDesc.GetElementSpace());
DeviceMem wei_kyxc_device_buf(data_sz * wei_kyxc.mDesc.GetElementSpace());
DeviceMem out_nhwk_device_buf(data_sz * out_nhwk.mDesc.GetElementSpace());
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmN = 1;
constexpr index_t GemmBBlockCopyDstDataPerWrite_GemmN = 1;
in_nhwc_device_buf.ToDevice(in_nhwc.mData.data());
wei_kyxc_device_buf.ToDevice(wei_kyxc.mData.data());
out_nhwk_device_buf.ToDevice(out_nhwk.mData.data());
constexpr index_t GemmCThreadCopyDstDataPerWrite_GemmN1 = 1;
#elif 1
// BlockSize = 256, each thread hold 64 data
#if 0
// cdata = 64, BlockSize = 256, 128x128x8
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
......@@ -106,27 +98,26 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
constexpr index_t GemmThreadGemmDataPerReadM = 4;
constexpr index_t GemmThreadGemmDataPerReadN = 4;
using GemmABlockCopyThreadSliceLengths_GemmK_GemmM = Sequence<1, 4>;
using GemmABlockCopyThreadClusterLengths_GemmK_GemmM = Sequence<8, 32>;
using GemmABlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmM = Sequence<1, 1, 1, 4>;
using GemmABlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmM = Sequence<1, 1, 8, 32>;
constexpr index_t GemmABlockCopySrcDataPerRead_GemmM = 1;
constexpr index_t GemmABlockCopyDstDataPerWrite_GemmM = 1;
constexpr index_t GemmABlockCopySrcDataPerRead_GemmM = 4;
constexpr index_t GemmABlockCopyDstDataPerWrite_GemmM = 4;
using GemmBBlockCopyThreadSliceLengths_GemmK_GemmN = Sequence<1, 4>;
using GemmBBlockCopyThreadClusterLengths_GemmK_GemmN = Sequence<8, 32>;
using GemmBBlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmN = Sequence<1, 1, 4, 1>;
using GemmBBlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmN = Sequence<1, 1, 2, 128>;
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmN = 1;
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmK2 = 4;
constexpr index_t GemmBBlockCopyDstDataPerWrite_GemmN = 1;
constexpr index_t GemmCThreadCopyDstDataPerWrite_GemmN1 = 1;
#elif 1
// BlockSize = 256, each thread hold 64 data
// for 1x1 weight, 8x8 input
// cdata = 64, BlockSize = 256, 128x128x16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 8;
constexpr index_t GemmKPerBlock = 16;
constexpr index_t GemmMPerThread = 4;
constexpr index_t GemmNPerThread = 4;
constexpr index_t GemmKPerThread = 1;
......@@ -137,19 +128,19 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
constexpr index_t GemmThreadGemmDataPerReadM = 4;
constexpr index_t GemmThreadGemmDataPerReadN = 4;
using GemmABlockCopyThreadSliceLengths_GemmK_GemmM = Sequence<1, 4>;
using GemmABlockCopyThreadClusterLengths_GemmK_GemmM = Sequence<8, 32>;
using GemmABlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmM = Sequence<1, 1, 2, 4>;
using GemmABlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmM = Sequence<1, 1, 8, 32>;
constexpr index_t GemmABlockCopySrcDataPerRead_GemmM = 4;
constexpr index_t GemmABlockCopyDstDataPerWrite_GemmM = 4;
using GemmBBlockCopyThreadSliceLengths_GemmK_GemmN = Sequence<1, 4>;
using GemmBBlockCopyThreadClusterLengths_GemmK_GemmN = Sequence<8, 32>;
using GemmBBlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmN = Sequence<1, 1, 8, 1>;
using GemmBBlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmN = Sequence<1, 1, 2, 128>;
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmN = 4;
constexpr index_t GemmBBlockCopyDstDataPerWrite_GemmN = 4;
constexpr index_t GemmBBlockCopySrcDataPerRead_GemmK2 = 4;
constexpr index_t GemmBBlockCopyDstDataPerWrite_GemmN = 1;
constexpr index_t GemmCThreadCopyDstDataPerWrite_GemmN1 = 4;
constexpr index_t GemmCThreadCopyDstDataPerWrite_GemmN1 = 1;
#endif
constexpr index_t GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
......@@ -177,7 +168,7 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
constexpr index_t HTildaSlice = HTildaRight - HTildaLeft;
constexpr index_t WTildaSlice = WTildaRight - WTildaLeft;
constexpr index_t GemmM = C * YTilda * XTilda;
constexpr index_t GemmM = C;
constexpr index_t GemmN = N * HTildaSlice * WTildaSlice;
constexpr index_t GridSize = math::integer_divide_ceil(GemmM, GemmMPerBlock) *
......@@ -185,14 +176,24 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
printf("%s: BlockSize %u, GridSize %u \n", __func__, BlockSize, GridSize);
using gridwise_conv_bwd_data = GridwiseConvolutionBackwardDataImplicitGemm_v2r1_nchw_kcyx_nkhw<
for(index_t i = 0; i < 5; ++i)
{
std::cout << "Start running " << nrepeat << " times..." << std::endl;
KernelTimer timer;
timer.Start();
for(index_t i = 0; i < nrepeat; ++i)
{
using GridwiseConvBwdData =
GridwiseConvolutionBackwardDataImplicitGemm_v5r1_nhwc_kyxc_nhwk<
GridSize,
BlockSize,
T,
T,
decltype(in_nchw_desc),
decltype(wei_kcyx_desc),
decltype(out_nkhw_desc),
decltype(in_nhwc_desc),
decltype(wei_kyxc_desc),
decltype(out_nhwk_desc),
ConvStrides,
ConvDilations,
InLeftPads,
......@@ -209,36 +210,38 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
GemmNLevel1Cluster,
GemmThreadGemmDataPerReadM,
GemmThreadGemmDataPerReadN,
GemmABlockCopyThreadSliceLengths_GemmK_GemmM,
GemmABlockCopyThreadClusterLengths_GemmK_GemmM,
GemmABlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmM,
GemmABlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmM,
GemmABlockCopySrcDataPerRead_GemmM,
GemmABlockCopyDstDataPerWrite_GemmM,
GemmBBlockCopyThreadSliceLengths_GemmK_GemmN,
GemmBBlockCopyThreadClusterLengths_GemmK_GemmN,
GemmBBlockCopySrcDataPerRead_GemmN,
GemmBBlockCopyThreadSliceLengths_GemmK0_GemmK1_GemmK2_GemmN,
GemmBBlockCopyThreadClusterLengths_GemmK0_GemmK1_GemmK2_GemmN,
GemmBBlockCopySrcDataPerRead_GemmK2,
GemmBBlockCopyDstDataPerWrite_GemmN,
GemmCThreadCopyDstDataPerWrite_GemmN1>;
for(index_t i = 0; i < 5; ++i)
{
std::cout << "Start running " << nrepeat << " times..." << std::endl;
KernelTimer timer;
timer.Start();
static_for<0, GridwiseConvBwdData::GetNumberOfGemm(), 1>{}([&](auto gemm_id) {
constexpr auto gemm_sizes = GridwiseConvBwdData::GetGemmSize(gemm_id);
constexpr index_t gemm_k2 = gemm_sizes.At(4);
constexpr bool is_gemm_not_empty = gemm_k2 > 0;
for(index_t j = 0; j < nrepeat; ++j)
{
launch_kernel(run_gridwise_operation<gridwise_conv_bwd_data,
// only compile and run if GEMM is no empty
static_if<is_gemm_not_empty>{}([&](auto fwd) {
launch_kernel(run_gridwise_operation<GridwiseConvBwdData,
T* const __restrict__,
const T* const __restrict__,
const T* const __restrict__>,
const T* const __restrict__,
decltype(gemm_id)>,
dim3(GridSize),
dim3(BlockSize),
0,
0,
static_cast<T*>(in_nchw_device_buf.GetDeviceBuffer()),
static_cast<T*>(wei_kcyx_device_buf.GetDeviceBuffer()),
static_cast<T*>(out_nkhw_device_buf.GetDeviceBuffer()));
static_cast<T*>(in_nhwc_device_buf.GetDeviceBuffer()),
static_cast<T*>(wei_kyxc_device_buf.GetDeviceBuffer()),
static_cast<T*>(out_nhwk_device_buf.GetDeviceBuffer()),
fwd(gemm_id));
});
});
}
timer.End();
......@@ -251,7 +254,13 @@ void device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw(InDesc i
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
in_nchw_device_buf.FromDevice(in_nchw.mData.data());
in_nhwc_device_buf.FromDevice(in_nhwc.mData.data());
auto f_nhwc2nchw = [&](auto n, auto c, auto hi, auto wi) {
in_nchw(n, c, hi, wi) = in_nhwc(n, hi, wi, c);
};
make_ParallelTensorFunctor(f_nhwc2nchw, N, C, Hi, Wi)(std::thread::hardware_concurrency());
}
} // namespace launcher
driver/include/device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp
View file @ bbcb67d0
......@@ -133,7 +133,7 @@ void device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw(InDesc,
constexpr index_t WeiBlockCopySrcDataPerRead_E = 2;
constexpr index_t WeiBlockCopyDstDataPerWrite_K = 1;
#elif 1
#elif 0
// cdata = 64, BlockSize = 256, 128x128x8
constexpr index_t BlockSize = 256;
......@@ -172,7 +172,7 @@ void device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw(InDesc,
constexpr index_t WeiBlockCopySrcDataPerRead_E = 4;
constexpr index_t WeiBlockCopyDstDataPerWrite_K = 1;
#elif 0
#elif 1
// cdata = 64, BlockSize = 256, 128x128x16
constexpr index_t BlockSize = 256;
......
driver/src/conv_bwd_data_driver.cpp
View file @ bbcb67d0
......@@ -15,9 +15,8 @@
#include "host_conv_bwd_data.hpp"
#include "device_convolution_backward_data_implicit_gemm_v1r1_nchw_kcyx_nkhw.hpp"
#include "device_convolution_backward_data_implicit_gemm_v1r2_nchw_kcyx_nkhw.hpp"
#include "device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw.hpp"
#include "device_convolution_backward_data_implicit_gemm_v3r1_nchw_kcyx_nkhw.hpp"
#include "device_convolution_backward_data_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp"
#include "device_convolution_backward_data_implicit_gemm_v5r1_nhwc_kyxc_nhwk.hpp"
int main(int argc, char* argv[])
{
......@@ -55,7 +54,7 @@ int main(int argc, char* argv[])
#elif 0
// 3x3, 28x28
constexpr index_t N = 128;
constexpr index_t C = 1024;
constexpr index_t C = 256;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 1024;
......@@ -160,7 +159,7 @@ int main(int argc, char* argv[])
#elif 0
// 1x7 filter, 0x3 pad, 17x17 input
constexpr index_t N = 128;
constexpr index_t C = 1024;
constexpr index_t C = 256;
constexpr index_t HI = 17;
constexpr index_t WI = 17;
constexpr index_t K = 1024;
......@@ -175,7 +174,7 @@ int main(int argc, char* argv[])
#elif 0
// 7x1 filter, 3x0 pad, 17x17 input
constexpr index_t N = 128;
constexpr index_t C = 1024;
constexpr index_t C = 256;
constexpr index_t HI = 17;
constexpr index_t WI = 17;
constexpr index_t K = 1024;
......@@ -190,10 +189,10 @@ int main(int argc, char* argv[])
#elif 1
// 3x3 filter, 2x2 stride, 35x35 input, 17x17 output
constexpr index_t N = 128;
constexpr index_t C = 128;
constexpr index_t C = 256;
constexpr index_t HI = 35;
constexpr index_t WI = 35;
constexpr index_t K = 1024;
constexpr index_t K = 1280;
constexpr index_t Y = 3;
constexpr index_t X = 3;
......@@ -247,14 +246,12 @@ int main(int argc, char* argv[])
#if 0
device_convolution_backward_data_implicit_gemm_v1r1_nchw_kcyx_nkhw
#elif 1
device_convolution_backward_data_implicit_gemm_v1r2_nchw_kcyx_nkhw
#elif 0
device_convolution_backward_data_implicit_gemm_v2r1_nchw_kcyx_nkhw
device_convolution_backward_data_implicit_gemm_v1r2_nchw_kcyx_nkhw
#elif 0
device_convolution_backward_data_implicit_gemm_v3r1_nchw_kcyx_nkhw
#elif 1
device_convolution_backward_data_implicit_gemm_v4r1_nchw_kcyx_nkhw
#elif 1
device_convolution_backward_data_implicit_gemm_v5r1_nhwc_kyxc_nhwk
#endif
(in_nchw_desc,
in_nchw_device,
......
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