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"official/vision/evaluation/coco_utils.py" did not exist on "cb136c62f388e815241e925794e83e725b2d42bf"
Commit 572865a6 authored by carlushuang's avatar carlushuang
Browse files

update first gemm ok

parent 9ec4e3f7
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Showing with 371 additions and 214 deletions
example/ck_tile/15_fused_moe/main.cpp
View file @ 572865a6
......@@ -207,8 +207,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
{(max_num_tokens_padded + block_m - 1) / block_m});
ck_tile::HostTensor<IndexDataType> num_sorted_tiles_host({1});
#if 1
#if 1
#if 0
# if 1
ck_tile::FillStepRange<ADataType>{-.5f, .5f, 0.01f}(a_host);
ck_tile::FillStepRange<GDataType>{-.5f, .5f, 0.01f}(g_host);
ck_tile::FillStepRange<DDataType, false>{.5f, -.5f, -0.01f}(d_host);
......@@ -217,7 +217,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::FillStepRange<DScaleDataType>{0.f, 1.f, 0.01f}(sd_host);
ck_tile::FillStepRange<YSmoothScaleDataType>{0.f, 1.f, 0.01f}(sy_host);
ck_tile::FillStepRange<TopkWeightDataType>{-.5f, .5f, 0.01f}(topk_weight_host);
#else
# else
ck_tile::FillUniformDistribution<ADataType>{-.5f, .5f}(a_host);
ck_tile::FillUniformDistribution<GDataType>{-.5f, .5f}(g_host);
ck_tile::FillUniformDistribution<DDataType>{-.5f, .5f}(d_host);
......@@ -226,7 +226,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::FillUniformDistribution<DScaleDataType>{-.5f, .5f}(sd_host);
ck_tile::FillUniformDistribution<YSmoothScaleDataType>{-.5f, .5f}(sy_host);
ck_tile::FillUniformDistribution<TopkWeightDataType>{-.5f, .5f}(topk_weight_host);
#endif
# endif
// permute weight
ck_tile::HostTensor<GDataType> g_perm_host = shuffle_moe_weight(g_host, prec_w, 1);
......@@ -266,6 +266,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::HostTensor<DDataType> d_perm_host = shuffle_moe_weight(d_host, prec_w, 1);
std::cout << "------- @@@ " << __LINE__ << std::flush << std::endl;
# if 0
ck_tile::reference_moe_sorting<TopkWeightDataType, IndexDataType>(
topk_ids_host,
topk_weight_host,
......@@ -318,8 +319,10 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
return 1;
#endif
# endif
#endif
(void)balance;
ck_tile::reference_moe_sorting<TopkWeightDataType, IndexDataType>(
topk_ids_host,
topk_weight_host,
......
include/ck_tile/core/tensor/tile_window_linear.hpp
View file @ 572865a6
......@@ -432,7 +432,18 @@ struct tile_window_linear
CK_TILE_DEVICE static constexpr index_t get_bottom_linear_offset(number<i_access>)
{
constexpr auto linear_coord = get_bottom_linear_coordinate(number<i_access>{});
// since this is linear offset, we assum bottom X tensor is always linear
constexpr auto is_pure_linear_tensor = reduce_on_sequence(LinearBottomDims{}, multiplies{}, number<1>{});
if constexpr (is_pure_linear_tensor) {
// this case usually is a LDS window, everything is build time know.
// we directly use BottomTensorView to compute the offset, in case there is any padding
auto bottom_tensor_coord = make_tensor_coordinate(
BottomTensorView{}.get_tensor_descriptor(), linear_coord);
return bottom_tensor_coord.get_offset();
} else {
// this case usually is a global window, where last dim can be linear
// we hack here, that use the original TileDstr to compute the linear offset
// ... hoping that there is no extra padding between other dims, which make sense
// since that sould introduce runtime length
constexpr index_t linear_offset = [&]() {
constexpr auto x_idx_ = linear_coord;
constexpr auto x_len_ = TileDstr{}.get_lengths();
......@@ -447,9 +458,9 @@ struct tile_window_linear
});
return cu_offset_;
}();
return linear_offset;
}
}
CK_TILE_DEVICE constexpr auto get_num_of_access() const { return traits::NumAccess; }
......
include/ck_tile/host/reference/reference_fused_moe.hpp
View file @ 572865a6
......@@ -122,6 +122,7 @@ void reference_fused_moe(
type_convert<AccDataType>(g_host(i_expert, i_n, i_k));
}
acc_0(0, i_n) = acc;
// printf("ie:%2d, it:%3d, in:%d, %f\n", i_expert, i_token, i_n, acc);
}
ck_tile::HostTensor<AccDataType> y({1, intermediate_size_1});
......@@ -134,6 +135,7 @@ void reference_fused_moe(
for(ck_tile::index_t i_n = 0; i_n < intermediate_size_1; i_n++)
{
Activation{}(y(0, i_n), acc_0(0, i_n));
printf("ie:%2d, it:%3d, in:%d, %f\n", i_expert, i_token, i_n, y(0, i_n));
}
}
else
......@@ -168,7 +170,8 @@ void reference_fused_moe(
}
};
make_ParallelTensorFunctor(f, max_num_tokens_padded)(std::thread::hardware_concurrency());
// make_ParallelTensorFunctor(f, max_num_tokens_padded)(std::thread::hardware_concurrency());
make_ParallelTensorFunctor(f, max_num_tokens_padded)(1);
// reduce
auto r = [&](auto i_token) {
......
include/ck_tile/ops/elementwise/unary_element_wise_operation.hpp
View file @ 572865a6
......@@ -596,9 +596,9 @@ struct FastGeluAsm
CK_TILE_DEVICE void operator()<float, float>(float& y, const float& x) const
{
// const float u = 2.f * x * (0.035677f * x * x + 0.797885f);
const float c1 = 0xbd92220c; // -2.0 * 0.035677f;
const uint32_t c1 = 0xbd92220c; // -2.0 * 0.035677f;
const float c2 = -2.0 * 0.797885f;
const float log2e_ = 0x3fb8aa3b; // log2e_v<float>;
const uint32_t log2e_ = 0x3fb8aa3b; // log2e_v<float>;
float tmp;
asm volatile("v_mul_f32 %[v_tmp], %[v_x], %[v_x] ; x*x\n"
......@@ -606,13 +606,63 @@ struct FastGeluAsm
"v_mul_f32 %[v_tmp], %[v_tmp], %[v_x] ; x*(c1*x*x+c2)\n"
"v_mul_f32 %[v_tmp], %[v_tmp], %[s_log2e] ; log2e*x*(c1*x*x+c2)\n"
"v_exp_f32 %[v_tmp], %[v_tmp] ; emu = exp2(log2e*x*(c1*x*x+c2))\n"
"s_nop 0 ; hazard for exp\n"
"v_add_f32 %[v_tmp], %[v_tmp], 1.0 ; emu+1.0f\n"
"v_rcp_f32 %[v_tmp], %[v_tmp] ; 1/(emu+1.0f)\n"
"s_nop 0 ; hazard for rcp \n"
"v_mul_f32 %[v_y], %[v_tmp], %[v_x] ; x * 1/(emu+1f)\n"
: [v_y] "=v"(y), [v_tmp] "+v"(tmp)
: [v_x] "v"(x), [s_c1] "s"(c1), [v_c2] "v"(c2), [s_log2e] "s"(log2e_)
:);
}
template <>
CK_TILE_HOST void operator()<fp32x2_t, fp32x2_t>(fp32x2_t& y, const fp32x2_t& x) const
{
// const float u = -2.f * x * (0.035677f * x * x + 0.797885f);
const float c1 = -2.0 * 0.035677f;
const float c2 = -2.0 * 0.797885f;
const float u0 = x.x * (c1 * x.x * x.x + c2);
const float emu0 = exp(u0);
y.x = x.x / (1.f + emu0);
const float u1 = x.y * (c1 * x.y * x.y + c2);
const float emu1 = exp(u1);
y.y = x.y / (1.f + emu1);
}
// this is packed verion to remove data hazard for trans
template <>
CK_TILE_DEVICE void operator()<fp32x2_t, fp32x2_t>(fp32x2_t& y, const fp32x2_t& x) const
{
// const float u = 2.f * x * (0.035677f * x * x + 0.797885f);
const uint32_t c1 = 0xbd92220c; // -2.0 * 0.035677f;
const float c2 = -2.0 * 0.797885f;
const uint32_t log2e_ = 0x3fb8aa3b; // log2e_v<float>;
float tmp0, tmp1;
float y0, y1;
asm volatile("v_mul_f32 %[v_tmp0], %[v_x0], %[v_x0] ; x*x\n"
"v_mul_f32 %[v_tmp1], %[v_x1], %[v_x1] ; x*x\n"
"v_fma_f32 %[v_tmp0], %[v_tmp0], %[s_c1], %[v_c2] ; c1*x*x+c2\n"
"v_fma_f32 %[v_tmp1], %[v_tmp1], %[s_c1], %[v_c2] ; c1*x*x+c2\n"
"v_mul_f32 %[v_tmp0], %[v_tmp0], %[v_x0] ; x*(c1*x*x+c2)\n"
"v_mul_f32 %[v_tmp1], %[v_tmp1], %[v_x1] ; x*(c1*x*x+c2)\n"
"v_mul_f32 %[v_tmp0], %[v_tmp0], %[s_log2e] ; log2e*x*(c1*x*x+c2)\n"
"v_mul_f32 %[v_tmp1], %[v_tmp1], %[s_log2e] ; log2e*x*(c1*x*x+c2)\n"
"v_exp_f32 %[v_tmp0], %[v_tmp0] ; emu = exp2(log2e*x*(c1*x*x+c2))\n"
"v_exp_f32 %[v_tmp1], %[v_tmp1] ; emu = exp2(log2e*x*(c1*x*x+c2))\n"
"v_add_f32 %[v_tmp0], %[v_tmp0], 1.0 ; emu+1.0f\n"
"v_add_f32 %[v_tmp1], %[v_tmp1], 1.0 ; emu+1.0f\n"
"v_rcp_f32 %[v_tmp0], %[v_tmp0] ; 1/(emu+1.0f)\n"
"v_rcp_f32 %[v_tmp1], %[v_tmp1] ; 1/(emu+1.0f)\n"
"v_mul_f32 %[v_y0], %[v_tmp0], %[v_x0] ; x * 1/(emu+1f)\n"
"v_mul_f32 %[v_y1], %[v_tmp1], %[v_x1] ; x * 1/(emu+1f)\n"
: [v_y0] "=v"(y0), [v_y1] "=v"(y1), [v_tmp0] "+v"(tmp0), [v_tmp1] "+v"(tmp1)
: [v_x0] "v"(x.x), [v_x1] "v"(x.y), [s_c1] "s"(c1), [v_c2] "v"(c2), [s_log2e] "s"(log2e_)
:);
y.x = y0;
y.y = y1;
}
};
// https://paperswithcode.com/method/gelu
......
include/ck_tile/ops/flatmm/pipeline/uk/flatmm_uk_gfx9_32x512x128_1x4x1_16x16x16.hpp
View file @ 572865a6
......@@ -13,6 +13,26 @@ namespace ck_tile {
// require 4 wave, occupancy=1c
// agpr useage:256
// vgpr usage:64(A local) + 64(acc) + 8(os_a) + 8(os_b) = 144 (rem:112)
//
// for this gemm, 4 16x16x16 transposed layout
// input A vpgpr layout
// v0-v15: [ 0:15](gemm_m)x128(gemm_k)
// v16-v31: [16:31](gemm_m)x128(gemm_k)
// input B vpgpr layout
// v0-v15: [ 0: 15](gemm_n)x128(gemm_k)
// v16-v31: [ 64: 79](gemm_n)x128(gemm_k)
// ......................
// v111-v127: [448:463](gemm_n)x128(gemm_k)
// output C vpgpr layout
// v0-v3 : [ 0:15](gemm_m)x[ 0: 15](gemm_n)
// v4-v7 : [16:31](gemm_m)x[ 0: 15](gemm_n)
// v8-v11: [ 0:15](gemm_m)x[64: 79](gemm_n)
// v12-v15: [16:31](gemm_m)x[64: 79](gemm_n)
// ......................
// v56-v59: [ 0:15](gemm_m)x[448:463](gemm_n)
// v60-v63: [16:31](gemm_m)x[448:463](gemm_n)
struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
{
static constexpr index_t Block_M = 32;
......@@ -42,7 +62,7 @@ struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
static constexpr index_t Repeat_N = Block_N / (Warp_N * WarpPerBlock_N); // 8
static constexpr index_t Repeat_K = Block_K / (Warp_K * WarpPerBlock_K); // 8/2=4
static CK_TILE_DEVICE constexpr auto MakeCBlockTile()
static CK_TILE_DEVICE constexpr auto MakeCBlockDist()
{
constexpr auto c_block_outer_dstr_encoding = tile_distribution_encoding<
sequence<>,
......@@ -53,11 +73,17 @@ struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
sequence<0, 0>>{};
using WG = WarpGemmMfmaF16F16F32M16N16K32TransposedCDistribution;
using CDataType = float;
constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
c_block_outer_dstr_encoding, typename WG::CWarpDstrEncoding{});
constexpr auto c_block_dstr = make_static_tile_distribution(c_block_dstr_encode);
return c_block_dstr;
}
static CK_TILE_DEVICE constexpr auto MakeCBlockTile()
{
using CDataType = float;
constexpr auto c_block_dstr = MakeCBlockDist();
auto c_block_tensor = make_static_distributed_tensor<CDataType>(c_block_dstr);
return c_block_tensor;
}
......@@ -153,21 +179,8 @@ struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
// template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeLdsLoadDesc_A()
{
// A async->LDS
// Note that, this descriptor is only to construct the layout inside LDS
// in real Gemm pipeline, ds_read may not follow this pattern
// (may follow that in tile_distribution)
// below code is almost the same as SmemStore dist, with difference:
// 1). modify the GuaranteedLastDimensionVectorLength of naive tensor desc
// 2). return discriptor is in NxK 2d layout
// constexpr index_t Block_M = Problem::BlockShape::Block_M0;
// constexpr index_t Block_K = Problem::BlockShape::Block_K0;
// constexpr index_t BlockSize = Problem::BlockShape::BlockSize;
constexpr index_t warpSize = ck_tile::get_warp_size();
// constexpr index_t NumWarps = Problem::BlockShape::NumWarps;
// load from LDS to register, every wave has same layout
constexpr index_t KPack_ = 8; // GetSmemKPack_A<Problem>(); // LDS
constexpr index_t KVector = 2; // GetAlignment_A<Problem>(); // async copy 1 dword
constexpr index_t KPad = KPack_; // pad between warps
constexpr index_t kAMLane = 16;
......@@ -176,29 +189,12 @@ struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
constexpr index_t kKIter = 2;
static_assert(KPack_ == (kABKPerLane * kKIter));
static_assert(Block_K % KVector == 0);
constexpr index_t LanesPerK = Block_K / KVector; // how many thread loading K
if constexpr(LanesPerK >= warpSize)
{
// need multiple waves to load K
static_assert(LanesPerK % warpSize == 0);
constexpr index_t wavesPerK = LanesPerK / warpSize;
if constexpr(wavesPerK >= NumWarps)
{
// TODO: need multiple issues along K to load all data
}
else
{
// TODO: every wave load the same data!
static_assert(Block_K % (kABKLane * KPack_) == 0);
constexpr index_t issue_along_k = Block_K / (kABKLane * KPack_); // 4
constexpr index_t issue_along_m = Block_M / (kAMLane); // 2
constexpr auto lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<issue_along_m>{}, // m0
number<kAMLane>{}, // m1
number<issue_along_k>{}, // k0
number<kABKLane>{}, // k1
number<KPack_>{}), // k2
make_tuple(number<Repeat_M>{}, // m0 y
number<kAMLane>{}, // m1 p
number<Repeat_K>{}, // k0 y
number<kABKLane>{}, // k1 p
number<KPack_>{}), // k2 y-vector
make_tuple(number<kAMLane*(Block_K + KPad)>{}, // m0
number<Block_K + KPad>{}, // m1
number<kABKLane * KPack_>{}, // k0
......@@ -210,19 +206,14 @@ struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
constexpr auto lds_desc_m_k = transform_tensor_descriptor(
lds_block_desc_0,
make_tuple(make_merge_transform(
make_tuple(number<issue_along_m>{}, number<kAMLane>{})),
make_tuple(number<Repeat_M>{}, number<kAMLane>{})),
make_merge_transform(make_tuple(
number<issue_along_k>{}, number<kABKLane>{}, number<KPack_>{}))),
number<Repeat_K>{}, number<kABKLane>{}, number<KPack_>{}))),
make_tuple(sequence<0, 1>{}, sequence<2, 3, 4>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return lds_desc_m_k;
}
}
else
{
}
}
static constexpr auto GetGemm_AWarpEnc()
{
......@@ -271,10 +262,10 @@ struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
auto a_sld = [&]() {
constexpr auto a_warp_enc_ = GetGemm_AWarpEnc();
constexpr auto a_outer_dstr_enc = tile_distribution_encoding<
sequence<>,
sequence<WarpPerBlock_N>,
tuple<sequence<Repeat_M, WarpPerBlock_M>, sequence<Repeat_K>>,
tuple<sequence<1>>,
tuple<sequence<1>>,
tuple<sequence<1, 0>>,
tuple<sequence<1, 0>>,
sequence<1, 2>,
sequence<0, 0>>{};
constexpr auto a_block_dstr_encode =
......@@ -300,6 +291,12 @@ struct FlatmmUK_GFX9_32x512x128_1x4x1_16x16x16_BF16
},
number<a_sld.get_num_of_access()>{});
printf("----- tid:%d, a_sld:%d\n", static_cast<index_t>(threadIdx.x),
static_cast<index_t>(a_sld.cached_coords_[number<0>{}].get_offset()));
index_t loop_cnt = k / Block_K;
// this is the acc thread buffer
......
include/ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_policy.hpp
View file @ 572865a6
......@@ -586,6 +586,47 @@ struct FusedMoeGemmPipelineFlatmmPolicy
return desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeBridgeLdsStoreForUKDesc()
{
constexpr index_t WarpPerBlock_N = Problem::BlockShape::WarpPerBlock_N0;
constexpr index_t Repeat_N = Problem::BlockShape::Repeat_N0;
constexpr index_t Repeat_M = Problem::BlockShape::Repeat_M0;
constexpr index_t kAMLane = 16;
constexpr index_t kABKLane = 4;
constexpr index_t kABKPerLane = 4;
constexpr index_t KPack = kABKPerLane;
constexpr auto lds_block_desc_0 = make_naive_tensor_descriptor(
make_tuple(number<Repeat_M>{}, // m
number<Repeat_N>{}, // n
number<WarpPerBlock_N>{}, // n
number<kABKLane>{}, // n
number<kAMLane>{}, // m
number<KPack>{}), // n
make_tuple(number<Repeat_N * WarpPerBlock_N * kABKLane * kAMLane * KPack>{}, // m
number<WarpPerBlock_N * kABKLane * kAMLane * KPack>{}, // n
number<kABKLane * kAMLane * KPack>{}, // n
number<kAMLane * KPack>{}, // n
number<KPack>{}, // m
number<1>{}), // n
number<KPack>{}, // lds store vector(actually no explicit store)
number<1>{});
constexpr auto desc = transform_tensor_descriptor(
lds_block_desc_0,
make_tuple(
make_merge_transform(make_tuple(number<Repeat_M>{}, number<kAMLane>{})),
make_merge_transform(make_tuple(number<Repeat_N>{}, number<WarpPerBlock_N>{}, number<kABKLane>{}, number<KPack>{}))
),
make_tuple(sequence<0, 4>{}, sequence<1, 2, 3, 5>{}),
make_tuple(sequence<0>{}, sequence<1>{}));
return desc;
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto GetWarpGemm0()
{
......
include/ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_uk.hpp
View file @ 572865a6
......@@ -340,12 +340,15 @@ struct FusedMoeGemmPipeline_FlatmmUk
number<row_ids_a.size()>{});
auto bridge_sst_win = [&]() {
return make_tile_window(
constexpr auto desc_ = Policy::template MakeBridgeLdsStoreForUKDesc<Problem>();
constexpr auto dist_ = Policy::template GetUK_0<Problem>().MakeCBlockDist();
return make_tile_window_linear(
make_tensor_view<address_space_enum::lds>(
reinterpret_cast<YDataType*>(smem),
Policy::template MakeBridgeLdsStoreDesc<Problem>()),
Policy::template MakeBridgeLdsStoreDesc<Problem>().get_lengths(),
{0, 0});
desc_),
desc_.get_lengths(),
{0, 0},
dist_);
}();
auto o_res =
make_wave_buffer_resource(reinterpret_cast<const ODataType*>(kargs.o_ptr),
......@@ -439,8 +442,56 @@ struct FusedMoeGemmPipeline_FlatmmUk
BlockShape::Block_W0); // tile offset for B matrix each unroll
// return ;
//sweep_tile(acc_0,
// [&](auto idx) { typename Problem::GateActivation{}(acc_0(idx), acc_0[idx]); });
sweep_tile(acc_0,
[&](auto idx) { typename Problem::GateActivation{}(acc_0(idx), acc_0[idx]); });
[&](auto idx0, auto idx1) {
fp32x2_t v_ {acc_0(idx0), acc_0(idx1)};
typename Problem::GateActivation{}(v_, v_);
acc_0(idx0) = v_.x;
acc_0(idx1) = v_.y;
},
sequence<1, 2>{});
#if 0
printf("bid:%d,%d, tid:%d, sorted_tile_id:%d(, intermediate_tile_id:%d, e:%d, "
"interm_idx_nr:%d, coords:a:%d,%d,%d, row_ids_a:%d,%d,%d, (%d)g_coords:%d.%d.%d, bridge_sst_win:%d"
"acc:%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f,%.1f\n",
static_cast<int>(blockIdx.x),
static_cast<int>(blockIdx.y),
static_cast<int>(threadIdx.x),
sorted_tile_id,
intermediate_tile_id,
expert_id,
interm_idx_nr,
row_coords_a[0],
row_coords_a[1],
row_coords_a[7],
row_ids_a[0],
row_ids_a[1],
row_ids_a[7],
kr_0 * BlockShape::Block_W0,
g_coords[number<0>{}],
g_coords[number<1>{}],
g_coords[number<7>{}],
bridge_sst_win.cached_coords_[number<0>{}].get_offset(),
acc_0.get_thread_buffer()[number<0>{}],
acc_0.get_thread_buffer()[number<1>{}],
acc_0.get_thread_buffer()[number<2>{}],
acc_0.get_thread_buffer()[number<3>{}],
acc_0.get_thread_buffer()[number<4>{}],
acc_0.get_thread_buffer()[number<5>{}],
acc_0.get_thread_buffer()[number<6>{}],
acc_0.get_thread_buffer()[number<7>{}],
acc_0.get_thread_buffer()[number<8 + 0>{}],
acc_0.get_thread_buffer()[number<8 + 1>{}],
acc_0.get_thread_buffer()[number<8 + 2>{}],
acc_0.get_thread_buffer()[number<8 + 3>{}],
acc_0.get_thread_buffer()[number<8 + 4>{}],
acc_0.get_thread_buffer()[number<8 + 5>{}],
acc_0.get_thread_buffer()[number<8 + 6>{}],
acc_0.get_thread_buffer()[number<8 + 7>{}]);
#endif
auto y_pre = cast_tile<YDataType>(acc_0);
store_tile(bridge_sst_win, y_pre);
......
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