mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-13 01:36:06 +00:00
add nchw atomic , nhwc and nhwc atomic method for backward weight (#30)
* add add new algorithm from v4r4r2 * program once issue * add split k functiion * redefine code * add a matrix unmerge * add b matrix unmerge k0 * trans a and b to gridegemm * nhwc init * no hacks and vector load * add hacks * modify some parameter * fix tuning prometer for fp32 * fix tuning prometer for fp16 * start change gridwise k split * init ok * revome a b matrix k0mk1 desc in grid * carewrite lculate gridsize * add kbatch to CalculateBottomIndex * remove some unused funtion * add clear data function before call kernel * out hacks * in hacks * rename device convolution file and function name * modify kBatch value * fix some tuning code * start from v4r4 nhwc * nhwc atomic is able to run * just for fp32 * enable nchw atomic * tweak * tweak * re-arrange gridwise gemm hot loop for wrw * add wrw v4r5 * v4r4r5 fp16 * v4r4r4 fp16 * v4r4r2 fp16 * V4R4R4XDLNHWC fp16 * V4R4R2XDLATOMICNCHW fp16 * adjust for fp16 * input gridsize * change kbatch to gridsize * testing wrw * clean up * k_batch to gridsize * fix bug * wrw v4r4r4 kbatch change to gride size * wrw v4r4r2 kbatch change to gride size * after merge , change gridwise gemm v2r4 * change MakeCBlockClusterAdaptor * other method use new gridwise gemm * clean up * chapad method nge to make_right_pad_transform * kbatch out from transform function * clean up and fix bug * fix bug * using function type reduce template parameters * using auto replace define fuction type * clean up Co-authored-by: ltqin <letaoqin@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com> Co-authored-by: Jing Zhang <jizhan@amd.com>
This commit is contained in:
ltqin
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#ifndef CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R2_ATOMIC_NCHW_KCYX_NKHW_HPP
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#define CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R2_ATOMIC_NCHW_KCYX_NKHW_HPP
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#include "common_header.hpp"
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#include "tensor_descriptor.hpp"
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#include "tensor_descriptor_helper.hpp"
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namespace ck {
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// GemmM = K
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// GemmK = N * Ho * Wo
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// GemmN = C * Y * X
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template <typename... Wei,
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typename... In,
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typename... Out,
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typename ConvStrides,
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typename ConvDilations,
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typename InLeftPads,
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typename InRightPads,
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index_t GemmK1Value,
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typename GemmKBatchType,
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typename GemmKPadType>
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__host__ __device__ constexpr auto
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transform_backward_weight_convolution_into_gemm_v4r4r2_atomic_nchw_kcyx_nkhw_pad(
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const TensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
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const TensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
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const TensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
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const ConvStrides& conv_strides,
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const ConvDilations& conv_dilations,
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const InLeftPads& in_left_pads,
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const InRightPads& in_right_pads,
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Number<GemmK1Value>,
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GemmKBatchType GemmKBatch,
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GemmKPadType GemmKPad)
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{
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constexpr auto I0 = Number<0>{};
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constexpr auto I1 = Number<1>{};
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constexpr auto I2 = Number<2>{};
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constexpr auto I3 = Number<3>{};
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constexpr auto GemmK1 = Number<GemmK1Value>{};
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const auto N = in_n_c_hi_wi_grid_desc.GetLength(I0);
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const auto C = in_n_c_hi_wi_grid_desc.GetLength(I1);
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const auto K = out_n_k_ho_wo_grid_desc.GetLength(I1);
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const auto Hi = in_n_c_hi_wi_grid_desc.GetLength(I2);
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const auto Wi = in_n_c_hi_wi_grid_desc.GetLength(I3);
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const auto Ho = out_n_k_ho_wo_grid_desc.GetLength(I2);
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const auto Wo = out_n_k_ho_wo_grid_desc.GetLength(I3);
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const auto Y = wei_k_c_y_x_grid_desc.GetLength(I2);
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const auto X = wei_k_c_y_x_grid_desc.GetLength(I3);
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const auto ConvStrideH = conv_strides[I0];
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const auto ConvStrideW = conv_strides[I1];
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const auto ConvDilationH = conv_dilations[I0];
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const auto ConvDilationW = conv_dilations[I1];
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const auto InLeftPadH = in_left_pads[I0];
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const auto InLeftPadW = in_left_pads[I1];
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const auto InRightPadH = in_right_pads[I0];
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const auto InRightPadW = in_right_pads[I1];
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const auto GemmM = K;
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const auto GemmN = C * Y * X;
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const auto GemmKTotal = N * Ho * Wo;
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const index_t GemmK0 = GemmKPad / (GemmKBatch * GemmK1);
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// A: output tensor
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const auto out_gemmktotal_gemmm_grid_desc = transform_tensor_descriptor(
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make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo)),
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make_tuple(make_pass_through_transform(K), make_merge_transform(make_tuple(N, Ho * Wo))),
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make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
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make_tuple(Sequence<1>{}, Sequence<0>{}));
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const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
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out_gemmktotal_gemmm_grid_desc,
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make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
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make_pass_through_transform(GemmM)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
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out_gemmkpad_gemmm_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1)),
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make_pass_through_transform(GemmM)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
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// B: input tensor
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const auto in_n_c_hip_wip_grid_desc = transform_tensor_descriptor(
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in_n_c_hi_wi_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_pass_through_transform(C),
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make_pad_transform(Hi, InLeftPadH, InRightPadH),
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make_pad_transform(Wi, InLeftPadW, InRightPadW)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
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const auto in_n_c_y_ho_x_wo_grid_desc = transform_tensor_descriptor(
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in_n_c_hip_wip_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_pass_through_transform(C),
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make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
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make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
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const auto in_gemmktotal_gemmn_grid_desc =
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transform_tensor_descriptor(in_n_c_y_ho_x_wo_grid_desc,
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make_tuple(make_merge_transform(make_tuple(C, Y, X)),
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make_merge_transform(make_tuple(N, Ho, Wo))),
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make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
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make_tuple(Sequence<1>{}, Sequence<0>{}));
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const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
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in_gemmktotal_gemmn_grid_desc,
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make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
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make_pass_through_transform(GemmN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
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in_gemmkpad_gemmn_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1)),
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make_pass_through_transform(GemmN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
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// C: weight tensor
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const auto wei_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
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make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
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make_tuple(make_pass_through_transform(K), make_pass_through_transform(C * Y * X)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
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in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
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wei_gemmm_gemmn_grid_desc);
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}
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} // namespace ck
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#endif
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@@ -0,0 +1,147 @@
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#ifndef CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R4_ATOMIC_NHWC_KYXC_NHWK_HPP
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#define CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R4_ATOMIC_NHWC_KYXC_NHWK_HPP
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#include "common_header.hpp"
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#include "tensor_descriptor.hpp"
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#include "tensor_descriptor_helper.hpp"
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namespace ck {
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// A: in
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// B: wei
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// C: out
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// GemmM = N * Ho * Wo
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// GemmN = K
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// GemmK = Y * X * C
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template <typename... In,
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typename... Wei,
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typename... Out,
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typename ConvStrides,
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typename ConvDilations,
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typename InLeftPads,
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typename InRightPads,
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index_t GemmK1Value,
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typename GemmKBatchType,
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typename GemmKPadType>
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__host__ __device__ constexpr auto
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transform_backward_weight_convolution_into_gemm_v4r4r4_atomic_nhwc_kyxc_nhwk_pad(
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const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
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const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
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const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
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const ConvStrides& conv_strides,
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const ConvDilations& conv_dilations,
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const InLeftPads& in_left_pads,
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const InRightPads& in_right_pads,
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Number<GemmK1Value>,
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GemmKBatchType GemmKBatch,
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GemmKPadType GemmKPad)
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{
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constexpr auto I0 = Number<0>{};
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constexpr auto I1 = Number<1>{};
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constexpr auto I2 = Number<2>{};
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constexpr auto I3 = Number<3>{};
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constexpr auto GemmK1 = Number<GemmK1Value>{};
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const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
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const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
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const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
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const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
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const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
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const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
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const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
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const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
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const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
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const auto ConvStrideH = conv_strides[I0];
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const auto ConvStrideW = conv_strides[I1];
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const auto ConvDilationH = conv_dilations[I0];
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const auto ConvDilationW = conv_dilations[I1];
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const auto InLeftPadH = in_left_pads[I0];
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const auto InLeftPadW = in_left_pads[I1];
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const auto InRightPadH = in_right_pads[I0];
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const auto InRightPadW = in_right_pads[I1];
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const auto GemmM = Y * X * C;
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const auto GemmN = K;
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const auto GemmKTotal = N * Ho * Wo;
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const index_t GemmK0 = GemmKPad / (GemmKBatch * GemmK1);
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// A: input tensor
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const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
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in_n_hi_wi_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_pad_transform(Hi, InLeftPadH, InRightPadH),
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make_pad_transform(Wi, InLeftPadW, InRightPadW),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
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const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
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in_n_hip_wip_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
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make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
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const auto in_gemmktotal_gemmm_grid_desc =
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transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
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make_tuple(make_merge_transform(make_tuple(Y, X, C)),
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make_merge_transform(make_tuple(N, Ho, Wo))),
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make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
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make_tuple(Sequence<1>{}, Sequence<0>{}));
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const auto in_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
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in_gemmktotal_gemmm_grid_desc,
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make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
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make_pass_through_transform(GemmM)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto in_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
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in_gemmkpad_gemmm_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1)),
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make_pass_through_transform(GemmM)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
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// B: output tensor
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const auto out_gemmktotal_gemmn_grid_desc =
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make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
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const auto out_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
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out_gemmktotal_gemmn_grid_desc,
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make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
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make_pass_through_transform(GemmN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto out_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
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out_gemmkpad_gemmn_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1)),
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make_pass_through_transform(GemmN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
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// C: weight tensor
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const auto wei_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
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make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C)),
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make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<1>{}, Sequence<0>{}));
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return make_tuple(in_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
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out_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
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wei_gemmm_gemmn_grid_desc);
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}
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} // namespace ck
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#endif
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@@ -0,0 +1,132 @@
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#ifndef CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R4_NHWC_KYXC_NHWK_HPP
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#define CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R4_NHWC_KYXC_NHWK_HPP
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#include "common_header.hpp"
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#include "tensor_descriptor.hpp"
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#include "tensor_descriptor_helper.hpp"
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namespace ck {
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// A: in
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// B: wei
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// C: out
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// GemmM = N * Ho * Wo
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// GemmN = K
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// GemmK = Y * X * C
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template <typename... In,
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typename... Wei,
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typename... Out,
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typename ConvStrides,
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typename ConvDilations,
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typename InLeftPads,
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typename InRightPads,
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index_t GemmK1Value>
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__host__ __device__ constexpr auto
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transform_backward_weight_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
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const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
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const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
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const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
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const ConvStrides& conv_strides,
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const ConvDilations& conv_dilations,
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const InLeftPads& in_left_pads,
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const InRightPads& in_right_pads,
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Number<GemmK1Value>)
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{
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constexpr auto I0 = Number<0>{};
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constexpr auto I1 = Number<1>{};
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constexpr auto I2 = Number<2>{};
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constexpr auto I3 = Number<3>{};
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constexpr auto GemmK1 = Number<GemmK1Value>{};
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const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
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const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
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const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
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const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
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const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
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|
||||
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
|
||||
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
|
||||
|
||||
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
|
||||
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
|
||||
|
||||
const auto ConvStrideH = conv_strides[I0];
|
||||
const auto ConvStrideW = conv_strides[I1];
|
||||
|
||||
const auto ConvDilationH = conv_dilations[I0];
|
||||
const auto ConvDilationW = conv_dilations[I1];
|
||||
|
||||
const auto InLeftPadH = in_left_pads[I0];
|
||||
const auto InLeftPadW = in_left_pads[I1];
|
||||
|
||||
const auto InRightPadH = in_right_pads[I0];
|
||||
const auto InRightPadW = in_right_pads[I1];
|
||||
|
||||
const auto GemmM = Y * X * C;
|
||||
const auto GemmN = K;
|
||||
const auto GemmK = N * Ho * Wo;
|
||||
const auto GemmK0 = GemmK / GemmK1;
|
||||
|
||||
// A: input tensor
|
||||
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_hi_wi_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N),
|
||||
make_pad_transform(Hi, InLeftPadH, InRightPadH),
|
||||
make_pad_transform(Wi, InLeftPadW, InRightPadW),
|
||||
make_pass_through_transform(C)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
|
||||
|
||||
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_hip_wip_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N),
|
||||
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
|
||||
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
|
||||
make_pass_through_transform(C)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
|
||||
|
||||
const auto in_gemmk_gemmm_grid_desc =
|
||||
transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
|
||||
make_tuple(make_merge_transform(make_tuple(Y, X, C)),
|
||||
make_merge_transform(make_tuple(N, Ho, Wo))),
|
||||
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}));
|
||||
|
||||
const auto in_gemmk0_gemmm_gemmk1_grid_desc =
|
||||
transform_tensor_descriptor(in_gemmk_gemmm_grid_desc,
|
||||
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
|
||||
make_pass_through_transform(GemmM)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
// B: output tensor
|
||||
const auto out_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
|
||||
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
|
||||
make_tuple(make_pass_through_transform(N * Ho * Wo), make_pass_through_transform(K)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto out_gemmk0_gemmn_gemmk1_grid_desc =
|
||||
transform_tensor_descriptor(out_gemmk_gemmn_grid_desc,
|
||||
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
|
||||
make_pass_through_transform(GemmN)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
// C: weight tensor
|
||||
const auto wei_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
|
||||
make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C)),
|
||||
make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}));
|
||||
|
||||
return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
|
||||
out_gemmk0_gemmn_gemmk1_grid_desc,
|
||||
wei_gemmm_gemmn_grid_desc);
|
||||
}
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
@@ -0,0 +1,144 @@
|
||||
#ifndef CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R5_NHWC_KYXC_NHWK_HPP
|
||||
#define CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R5_NHWC_KYXC_NHWK_HPP
|
||||
|
||||
#include "common_header.hpp"
|
||||
#include "tensor_descriptor.hpp"
|
||||
#include "tensor_descriptor_helper.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
// A: out
|
||||
// B: in
|
||||
// C: wei
|
||||
// GemmM = K
|
||||
// GemmN = Y * X * C
|
||||
// GemmKTotal = N * Ho * Wo
|
||||
template <typename... In,
|
||||
typename... Wei,
|
||||
typename... Out,
|
||||
typename ConvStrides,
|
||||
typename ConvDilations,
|
||||
typename InLeftPads,
|
||||
typename InRightPads,
|
||||
index_t GemmK1Value,
|
||||
typename GemmKBatchType,
|
||||
typename GemmKPadType>
|
||||
__host__ __device__ constexpr auto
|
||||
transform_backward_weight_convolution_into_gemm_v4r4r5_nhwc_kyxc_nhwk_pad(
|
||||
const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
|
||||
const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
|
||||
const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
|
||||
const ConvStrides& conv_strides,
|
||||
const ConvDilations& conv_dilations,
|
||||
const InLeftPads& in_left_pads,
|
||||
const InRightPads& in_right_pads,
|
||||
Number<GemmK1Value>,
|
||||
GemmKBatchType GemmKBatch,
|
||||
GemmKPadType GemmKPad)
|
||||
{
|
||||
constexpr auto I0 = Number<0>{};
|
||||
constexpr auto I1 = Number<1>{};
|
||||
constexpr auto I2 = Number<2>{};
|
||||
constexpr auto I3 = Number<3>{};
|
||||
|
||||
constexpr auto GemmK1 = Number<GemmK1Value>{};
|
||||
|
||||
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
|
||||
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
|
||||
const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
|
||||
|
||||
const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
|
||||
const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
|
||||
|
||||
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
|
||||
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
|
||||
|
||||
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
|
||||
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
|
||||
|
||||
const auto ConvStrideH = conv_strides[I0];
|
||||
const auto ConvStrideW = conv_strides[I1];
|
||||
|
||||
const auto ConvDilationH = conv_dilations[I0];
|
||||
const auto ConvDilationW = conv_dilations[I1];
|
||||
|
||||
const auto InLeftPadH = in_left_pads[I0];
|
||||
const auto InLeftPadW = in_left_pads[I1];
|
||||
|
||||
const auto InRightPadH = in_right_pads[I0];
|
||||
const auto InRightPadW = in_right_pads[I1];
|
||||
|
||||
const auto GemmM = K;
|
||||
const auto GemmN = Y * X * C;
|
||||
const auto GemmKTotal = N * Ho * Wo;
|
||||
const index_t GemmK0 = GemmKPad / (GemmKBatch * GemmK1);
|
||||
|
||||
// A: output tensor
|
||||
const auto out_gemmktotal_gemmm_grid_desc =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
|
||||
|
||||
const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
|
||||
out_gemmktotal_gemmm_grid_desc,
|
||||
make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
|
||||
make_pass_through_transform(GemmM)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
|
||||
out_gemmkpad_gemmm_grid_desc,
|
||||
make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1)),
|
||||
make_pass_through_transform(GemmM)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
|
||||
|
||||
// B: input tensor
|
||||
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_hi_wi_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N),
|
||||
make_pad_transform(Hi, InLeftPadH, InRightPadH),
|
||||
make_pad_transform(Wi, InLeftPadW, InRightPadW),
|
||||
make_pass_through_transform(C)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
|
||||
|
||||
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
|
||||
in_n_hip_wip_c_grid_desc,
|
||||
make_tuple(make_pass_through_transform(N),
|
||||
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
|
||||
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
|
||||
make_pass_through_transform(C)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
|
||||
|
||||
const auto in_gemmktotal_gemmn_grid_desc =
|
||||
transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
|
||||
make_tuple(make_merge_transform(make_tuple(Y, X, C)),
|
||||
make_merge_transform(make_tuple(N, Ho, Wo))),
|
||||
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}));
|
||||
|
||||
const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
|
||||
in_gemmktotal_gemmn_grid_desc,
|
||||
make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
|
||||
make_pass_through_transform(GemmN)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
|
||||
in_gemmkpad_gemmn_grid_desc,
|
||||
make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1)),
|
||||
make_pass_through_transform(GemmN)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
|
||||
|
||||
// C: weight tensor
|
||||
const auto wei_gemmm_gemmn_grid_desc =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
|
||||
|
||||
return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
|
||||
in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
|
||||
wei_gemmm_gemmn_grid_desc);
|
||||
}
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
@@ -0,0 +1,666 @@
|
||||
#ifndef CK_GRIDWISE_GEMM_XDLOPS_V2R4_HPP
|
||||
#define CK_GRIDWISE_GEMM_XDLOPS_V2R4_HPP
|
||||
|
||||
#include "common_header.hpp"
|
||||
#include "multi_index_transform_helper.hpp"
|
||||
#include "tensor_descriptor.hpp"
|
||||
#include "tensor_descriptor_helper.hpp"
|
||||
#include "blockwise_gemm_xdlops.hpp"
|
||||
#include "blockwise_tensor_slice_transfer.hpp"
|
||||
#include "threadwise_tensor_slice_transfer.hpp"
|
||||
#include "threadwise_tensor_slice_set.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
|
||||
template <typename GridwiseGemm,
|
||||
typename FloatAB,
|
||||
typename FloatC,
|
||||
typename ABK0MK1GridDesc,
|
||||
typename BBK0NK1GridDesc,
|
||||
typename CM0N0M1N1M2M3M4N2GridDesc,
|
||||
typename CBlockClusterAdaptor>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_gemm_xdlops_v2r4(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatC* __restrict__ p_c_grid,
|
||||
const ABK0MK1GridDesc a_b_k0_m_k1_grid_desc,
|
||||
const BBK0NK1GridDesc b_b_k0_n_k1_grid_desc,
|
||||
const CM0N0M1N1M2M3M4N2GridDesc c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
|
||||
const CBlockClusterAdaptor c_block_cluster_adaptor)
|
||||
{
|
||||
constexpr index_t shared_block_size =
|
||||
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
|
||||
|
||||
__shared__ FloatAB p_shared_block[shared_block_size];
|
||||
|
||||
GridwiseGemm::Run(p_a_grid,
|
||||
p_b_grid,
|
||||
p_c_grid,
|
||||
p_shared_block,
|
||||
a_b_k0_m_k1_grid_desc,
|
||||
b_b_k0_n_k1_grid_desc,
|
||||
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
|
||||
c_block_cluster_adaptor);
|
||||
}
|
||||
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
|
||||
template <typename GridwiseGemm,
|
||||
typename FloatAB,
|
||||
typename FloatC,
|
||||
typename ABK0MK1GridDesc,
|
||||
typename BBK0NK1GridDesc,
|
||||
typename CM0N0M1N1M2M3M4N2GridDesc,
|
||||
typename CBlockClusterAdaptor>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_gemm_xdlops_v2r4(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatC* __restrict__ p_c_grid,
|
||||
const void CONSTANT* p_a_b_k0_m_k1_grid_desc,
|
||||
const void CONSTANT* p_b_b_k0_n_k1_grid_desc,
|
||||
const void CONSTANT* p_c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
|
||||
const void CONSTANT* p_c_block_cluster_adaptor)
|
||||
{
|
||||
constexpr index_t shared_block_size =
|
||||
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
|
||||
|
||||
const auto a_b_k0_m_k1_grid_desc = *reinterpret_cast<const ABK0MK1GridDesc*>(
|
||||
cast_pointer_to_generic_address_space(p_a_b_k0_m_k1_grid_desc));
|
||||
const auto b_b_k0_n_k1_grid_desc = *reinterpret_cast<const BBK0NK1GridDesc*>(
|
||||
cast_pointer_to_generic_address_space(p_b_b_k0_n_k1_grid_desc));
|
||||
const auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc =
|
||||
*reinterpret_cast<const CM0N0M1N1M2M3M4N2GridDesc*>(
|
||||
cast_pointer_to_generic_address_space(p_c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc));
|
||||
const auto c_block_cluster_adaptor = *reinterpret_cast<const CBlockClusterAdaptor*>(
|
||||
cast_pointer_to_generic_address_space(p_c_block_cluster_adaptor));
|
||||
|
||||
__shared__ FloatAB p_shared_block[shared_block_size];
|
||||
|
||||
GridwiseGemm::Run(p_a_grid,
|
||||
p_b_grid,
|
||||
p_c_grid,
|
||||
p_shared_block,
|
||||
a_b_k0_m_k1_grid_desc,
|
||||
b_b_k0_n_k1_grid_desc,
|
||||
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
|
||||
c_block_cluster_adaptor);
|
||||
}
|
||||
#endif
|
||||
|
||||
template <index_t BlockSize,
|
||||
typename FloatAB,
|
||||
typename FloatAcc,
|
||||
typename FloatC,
|
||||
InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
|
||||
typename ABK0MK1GridDesc,
|
||||
typename BBK0NK1GridDesc,
|
||||
typename CMNGridDesc,
|
||||
index_t MPerBlock,
|
||||
index_t NPerBlock,
|
||||
index_t KPerBlock,
|
||||
index_t MPerXDL,
|
||||
index_t NPerXDL,
|
||||
index_t K1Value,
|
||||
index_t MRepeat,
|
||||
index_t NRepeat,
|
||||
typename ABlockTransferThreadSliceLengths_K0_M_K1,
|
||||
typename ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
index_t ABlockTransferSrcVectorDim,
|
||||
index_t ABlockTransferSrcScalarPerVector,
|
||||
index_t ABlockTransferDstScalarPerVector_K1,
|
||||
bool AThreadTransferSrcResetCoordinateAfterRun,
|
||||
typename BBlockTransferThreadSliceLengths_K0_N_K1,
|
||||
typename BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
index_t BBlockTransferSrcVectorDim,
|
||||
index_t BBlockTransferSrcScalarPerVector,
|
||||
index_t BBlockTransferDstScalarPerVector_K1,
|
||||
bool BThreadTransferSrcResetCoordinateAfterRun,
|
||||
typename CThreadTransferSrcDstAccessOrder,
|
||||
index_t CThreadTransferSrcDstVectorDim,
|
||||
index_t CThreadTransferDstScalarPerVector,
|
||||
typename AGridStepHacks,
|
||||
typename BGridStepHacks,
|
||||
typename CGridStepHacks,
|
||||
typename AGridMoveSliceWindowStepHacks,
|
||||
typename BGridMoveSliceWindowStepHacks,
|
||||
bool CAccessOrderMRepeatNRepeat,
|
||||
bool ABlockLdsExtraM,
|
||||
bool BBlockLdsExtraN>
|
||||
struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
static constexpr auto I4 = Number<4>{};
|
||||
static constexpr auto I5 = Number<5>{};
|
||||
static constexpr auto I6 = Number<6>{};
|
||||
static constexpr auto I7 = Number<7>{};
|
||||
|
||||
// K1 should be Number<...>
|
||||
static constexpr auto K1 = Number<K1Value>{};
|
||||
|
||||
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
|
||||
{
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_k0_m_k1_block_desc = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_k0_n_k1_block_desc = [&]() {
|
||||
if constexpr(BBlockLdsExtraN)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_space_size =
|
||||
math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
constexpr auto b_block_space_size =
|
||||
math::integer_least_multiple(b_k0_n_k1_block_desc.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
return (a_block_space_size + b_block_space_size) * sizeof(FloatAB);
|
||||
}
|
||||
|
||||
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
|
||||
__host__ __device__ static constexpr bool
|
||||
CheckValidity(const ABK0MK1GridDesc& a_b_k0_m_k1_grid_desc,
|
||||
const BBK0NK1GridDesc& b_b_k0_n_k1_grid_desc,
|
||||
const CMNGridDesc& c_m_n_grid_desc,
|
||||
index_t M01,
|
||||
index_t N01)
|
||||
{
|
||||
static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
|
||||
"wrong! K1 need to be known at compile-time");
|
||||
|
||||
static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
|
||||
(NPerBlock % (NRepeat * NPerXDL)) == 0,
|
||||
"Invalid tuning param!");
|
||||
|
||||
const auto M = a_b_k0_m_k1_grid_desc.GetLength(I2);
|
||||
const auto N = b_b_k0_n_k1_grid_desc.GetLength(I2);
|
||||
const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
|
||||
const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);
|
||||
|
||||
if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
|
||||
K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
|
||||
K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
|
||||
K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
|
||||
KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
|
||||
return false;
|
||||
|
||||
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % KPerBlock == 0))
|
||||
return false;
|
||||
|
||||
// check M01, N01
|
||||
constexpr auto M1 = Number<MPerBlock>{};
|
||||
constexpr auto N1 = Number<NPerBlock>{};
|
||||
|
||||
const auto M0 = M / M1;
|
||||
const auto N0 = N / N1;
|
||||
|
||||
if(!(M0 % M01 == 0 && N0 % N01 == 0))
|
||||
return false;
|
||||
|
||||
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
|
||||
return true;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t
|
||||
CalculateGridSize(const CMNGridDesc& c_m_n_grid_desc, index_t KBatch)
|
||||
{
|
||||
const auto M = c_m_n_grid_desc.GetLength(I0);
|
||||
const auto N = c_m_n_grid_desc.GetLength(I1);
|
||||
|
||||
const index_t grid_size = (M / MPerBlock) * (N / NPerBlock) * KBatch;
|
||||
|
||||
return grid_size;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeCM0N0M1N1M2M3M4N2GridDescriptor(const CMNGridDesc& c_m_n_grid_desc)
|
||||
{
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_k0_m_k1_block_desc = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_k0_n_k1_block_desc = [&]() {
|
||||
if constexpr(BBlockLdsExtraN)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
using BlockwiseGemm =
|
||||
BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
|
||||
FloatAB,
|
||||
FloatAcc,
|
||||
decltype(a_k0_m_k1_block_desc),
|
||||
decltype(b_k0_n_k1_block_desc),
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MRepeat,
|
||||
NRepeat,
|
||||
K1>;
|
||||
|
||||
return BlockwiseGemm::MakeCM0N0M1N1M2M3M4N2GridDescriptor(c_m_n_grid_desc);
|
||||
}
|
||||
|
||||
// return block_id to C matrix tile idx (m0, n0) mapping
|
||||
__host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
|
||||
const CMNGridDesc& c_m_n_grid_desc, index_t M01, index_t N01, index_t KBatch)
|
||||
{
|
||||
const auto M = c_m_n_grid_desc.GetLength(I0);
|
||||
const auto N = c_m_n_grid_desc.GetLength(I1);
|
||||
|
||||
constexpr auto M1 = Number<MPerBlock>{};
|
||||
constexpr auto N1 = Number<NPerBlock>{};
|
||||
|
||||
const auto M0 = M / M1;
|
||||
const auto N0 = N / N1;
|
||||
|
||||
const auto M00 = M0 / M01;
|
||||
const auto N00 = N0 / N01;
|
||||
|
||||
const auto kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_pass_through_transform(KBatch),
|
||||
make_unmerge_transform(make_tuple(M00, M01)),
|
||||
make_unmerge_transform(make_tuple(N00, N01))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
|
||||
|
||||
const auto c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(KBatch, M00, N00, M01, N01))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto c_blockid_to_kbatch_m0_n0_block_cluster_adaptor =
|
||||
chain_tensor_adaptors(kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
|
||||
c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor);
|
||||
|
||||
return c_blockid_to_kbatch_m0_n0_block_cluster_adaptor;
|
||||
}
|
||||
|
||||
using CM0N0M1N1M2M3M4N2GridDesc = decltype(MakeCM0N0M1N1M2M3M4N2GridDescriptor(CMNGridDesc{}));
|
||||
using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));
|
||||
|
||||
__device__ static void Run(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
FloatC* __restrict__ p_c_grid,
|
||||
FloatAB* __restrict__ p_shared_block,
|
||||
const ABK0MK1GridDesc& a_b_k0_m_k1_grid_desc,
|
||||
const BBK0NK1GridDesc& b_b_k0_n_k1_grid_desc,
|
||||
const CM0N0M1N1M2M3M4N2GridDesc& c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
|
||||
const CBlockClusterAdaptor& c_block_cluster_adaptor)
|
||||
{
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
|
||||
p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
|
||||
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
|
||||
p_b_grid, b_b_k0_n_k1_grid_desc.GetElementSpaceSize());
|
||||
auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
|
||||
p_c_grid, c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc.GetElementSpaceSize());
|
||||
|
||||
const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
|
||||
|
||||
// divide block work by [M, N]
|
||||
const auto block_work_idx =
|
||||
c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
const index_t k_batch_id = block_work_idx[I0];
|
||||
// HACK: this force m/n_block_data_idx_on_grid into SGPR
|
||||
const index_t m_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
|
||||
|
||||
const index_t n_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
|
||||
|
||||
// lds max alignment
|
||||
constexpr auto max_lds_align = K1;
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_k0_m_k1_block_desc = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
constexpr auto a_b_k0_m_k1_block_desc = [&]() {
|
||||
if constexpr(ABlockLdsExtraM)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<1>{}, Number<KPerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
make_tuple(Number<KPerBlock>{} * Number<MPerBlock + 1>{} * K1,
|
||||
Number<MPerBlock + 1>{} * K1,
|
||||
K1,
|
||||
I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<1>{}, Number<KPerBlock>{}, Number<MPerBlock>{}, K1),
|
||||
max_lds_align);
|
||||
}
|
||||
}();
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_k0_n_k1_block_desc = [&]() {
|
||||
if constexpr(BBlockLdsExtraN)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
|
||||
}
|
||||
}();
|
||||
|
||||
constexpr auto b_b_k0_n_k1_block_desc = [&]() {
|
||||
if constexpr(BBlockLdsExtraN)
|
||||
{
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(Number<1>{}, Number<KPerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
make_tuple(Number<KPerBlock>{} * Number<NPerBlock + 1>{} * K1,
|
||||
Number<NPerBlock + 1>{} * K1,
|
||||
K1,
|
||||
I1));
|
||||
}
|
||||
else
|
||||
{
|
||||
return make_naive_tensor_descriptor_aligned(
|
||||
make_tuple(Number<1>{}, Number<KPerBlock>{}, Number<NPerBlock>{}, K1),
|
||||
max_lds_align);
|
||||
}
|
||||
}();
|
||||
// A matrix blockwise copy
|
||||
auto a_blockwise_copy =
|
||||
BlockwiseTensorSliceTransfer_v4<BlockSize,
|
||||
InMemoryDataOperationEnum_t::Set,
|
||||
Sequence<1, KPerBlock, MPerBlock, K1>,
|
||||
ABlockTransferThreadSliceLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(a_b_k0_m_k1_grid_desc),
|
||||
decltype(a_b_k0_m_k1_block_desc),
|
||||
ABlockTransferSrcAccessOrder,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
ABlockTransferSrcVectorDim,
|
||||
3,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
1,
|
||||
1,
|
||||
AThreadTransferSrcResetCoordinateAfterRun,
|
||||
true>(
|
||||
a_b_k0_m_k1_grid_desc,
|
||||
make_multi_index(k_batch_id, 0, m_block_data_idx_on_grid, 0),
|
||||
a_b_k0_m_k1_block_desc,
|
||||
make_multi_index(0, 0, 0, 0));
|
||||
|
||||
// B matrix blockwise copy
|
||||
auto b_blockwise_copy =
|
||||
BlockwiseTensorSliceTransfer_v4<BlockSize,
|
||||
InMemoryDataOperationEnum_t::Set,
|
||||
Sequence<1, KPerBlock, NPerBlock, K1>,
|
||||
BBlockTransferThreadSliceLengths_K0_N_K1,
|
||||
BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(b_b_k0_n_k1_grid_desc),
|
||||
decltype(b_b_k0_n_k1_block_desc),
|
||||
BBlockTransferSrcAccessOrder,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
BBlockTransferSrcVectorDim,
|
||||
3,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
1,
|
||||
1,
|
||||
BThreadTransferSrcResetCoordinateAfterRun,
|
||||
true>(
|
||||
b_b_k0_n_k1_grid_desc,
|
||||
make_multi_index(k_batch_id, 0, n_block_data_idx_on_grid, 0),
|
||||
b_b_k0_n_k1_block_desc,
|
||||
make_multi_index(0, 0, 0, 0));
|
||||
|
||||
// GEMM definition
|
||||
// c_mtx += transpose(a_mtx) * b_mtx
|
||||
// a_mtx[KPerBlock, MPerBlock] is in LDS
|
||||
// b_mtx[KPerBlock, NPerBlock] is in LDS
|
||||
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
|
||||
// register
|
||||
// sanity check
|
||||
|
||||
auto blockwise_gemm =
|
||||
BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
|
||||
FloatAB,
|
||||
FloatAcc,
|
||||
decltype(a_k0_m_k1_block_desc),
|
||||
decltype(b_k0_n_k1_block_desc),
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MRepeat,
|
||||
NRepeat,
|
||||
K1>{};
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_space_size =
|
||||
math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
FloatAB* p_a_block = p_shared_block;
|
||||
FloatAB* p_b_block = p_shared_block + a_block_space_size;
|
||||
|
||||
constexpr auto a_block_slice_copy_step = make_multi_index(0, KPerBlock, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(0, KPerBlock, 0, 0);
|
||||
|
||||
// hack to control index calculation when iterating over A and B matrix for threadwise copy
|
||||
constexpr auto a_k0_m_k1_grid_step_hacks = AGridStepHacks{};
|
||||
constexpr auto b_k0_n_k1_grid_step_hacks = BGridStepHacks{};
|
||||
|
||||
// hack to control index calculation when move slice window for A and B matrix for
|
||||
// threadwise copy
|
||||
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hack = AGridMoveSliceWindowStepHacks{};
|
||||
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hack = BGridMoveSliceWindowStepHacks{};
|
||||
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
|
||||
p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
|
||||
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
|
||||
p_b_block, b_k0_n_k1_block_desc.GetElementSpaceSize());
|
||||
|
||||
// preload data into LDS
|
||||
{
|
||||
a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf, a_k0_m_k1_grid_step_hacks);
|
||||
b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf, b_k0_n_k1_grid_step_hacks);
|
||||
|
||||
a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
|
||||
b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
|
||||
}
|
||||
|
||||
// main body
|
||||
index_t k_block_data_begin = 0;
|
||||
|
||||
do
|
||||
{
|
||||
a_blockwise_copy.MoveSrcSliceWindow(a_b_k0_m_k1_grid_desc,
|
||||
a_block_slice_copy_step,
|
||||
a_k0_m_k1_grid_move_slice_window_step_hack);
|
||||
b_blockwise_copy.MoveSrcSliceWindow(b_b_k0_n_k1_grid_desc,
|
||||
b_block_slice_copy_step,
|
||||
b_k0_n_k1_grid_move_slice_window_step_hack);
|
||||
|
||||
a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf, a_k0_m_k1_grid_step_hacks);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf, b_k0_n_k1_grid_step_hacks);
|
||||
|
||||
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
|
||||
|
||||
block_sync_lds();
|
||||
|
||||
a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
|
||||
b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
|
||||
|
||||
k_block_data_begin += KPerBlock;
|
||||
} while(k_block_data_begin < (K0 - KPerBlock));
|
||||
|
||||
// tail
|
||||
{
|
||||
block_sync_lds();
|
||||
|
||||
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
|
||||
}
|
||||
|
||||
// output: register to global memory
|
||||
{
|
||||
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc =
|
||||
blockwise_gemm.GetCM0N0M1N1M2M3M4N2BlockDescriptor();
|
||||
|
||||
constexpr auto M0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I0);
|
||||
constexpr auto N0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I1);
|
||||
constexpr auto M1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I2);
|
||||
constexpr auto N1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I3);
|
||||
constexpr auto M2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I4);
|
||||
constexpr auto M3 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I5);
|
||||
constexpr auto M4 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I6);
|
||||
constexpr auto N2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I7);
|
||||
|
||||
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(
|
||||
Number<M0>{}, Number<N0>{}, I1, I1, Number<M2>{}, I1, Number<M4>{}, I1));
|
||||
|
||||
// calculate origin of thread output tensor on global memory
|
||||
// blockwise GEMM c matrix starting index
|
||||
const auto c_thread_mtx_on_block =
|
||||
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
|
||||
|
||||
const index_t m_thread_data_on_grid =
|
||||
m_block_data_idx_on_grid + c_thread_mtx_on_block[I0];
|
||||
|
||||
const index_t n_thread_data_on_grid =
|
||||
n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
|
||||
|
||||
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks = CGridStepHacks{};
|
||||
|
||||
const auto m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto m_thread_data_on_grid_idx =
|
||||
m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_grid));
|
||||
|
||||
const auto n_thread_data_on_grid_to_n0_n1_n2_adaptor = make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto n_thread_data_on_grid_idx =
|
||||
n_thread_data_on_grid_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_grid));
|
||||
|
||||
auto c_thread_copy =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
|
||||
FloatC,
|
||||
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc),
|
||||
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc),
|
||||
Sequence<M0, N0, I1, I1, M2, I1, M4, I1>,
|
||||
CThreadTransferSrcDstAccessOrder,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector,
|
||||
CGlobalMemoryDataOperation,
|
||||
1,
|
||||
true>{
|
||||
|
||||
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
|
||||
make_multi_index(m_thread_data_on_grid_idx[I0],
|
||||
n_thread_data_on_grid_idx[I0],
|
||||
m_thread_data_on_grid_idx[I1],
|
||||
n_thread_data_on_grid_idx[I1],
|
||||
m_thread_data_on_grid_idx[I2],
|
||||
m_thread_data_on_grid_idx[I3],
|
||||
m_thread_data_on_grid_idx[I4],
|
||||
n_thread_data_on_grid_idx[I2])};
|
||||
|
||||
c_thread_copy.Run(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
|
||||
make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
|
||||
c_thread_buf,
|
||||
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
|
||||
c_grid_buf,
|
||||
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks);
|
||||
}
|
||||
}
|
||||
}; // namespace ck
|
||||
|
||||
} // namespace ck
|
||||
#endif
|
||||
Reference in New Issue
Block a user