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upgrade from clang-format-12 to clang-format-18 (#2568)

Browse Source 
* upgrade to clang-format-18

* update to clang-format-18 in pre-commit-config
This commit is contained in:
Illia Silin
2025-07-28 11:34:07 -07:00
committed by GitHub
parent 9786087010
commit 504b101da3
373 changed files with 3351 additions and 3760 deletions
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3
example/ck_tile/02_layernorm2d/layernorm2d_fwd.cpp
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@@ -191,8 +191,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
return base_str;
}();
std::cout << "[" << prec_str << "]"
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride
std::cout << "[" << prec_str << "]" << " m:" << m << ", n:" << n << ", x_stride:" << x_stride
<< ", xr_stride:" << xr_stride << ", y_stride:" << y_stride
<< ", yr_stride:" << yr_stride << std::flush;

14
example/ck_tile/06_permute/alternative_impl/matrix_core_swizzle_kernel.hpp
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@@ -333,12 +333,12 @@ struct matrix_core_swizzle_kernel
return tmp_1;
#else
// b_nr_kr_waveflatten = b_nr_kr_kw_nw_kv,
constexpr index_t kv = Alignment;
constexpr index_t nw = WarpGemm::WarpGemmAttribute::Impl::kAMLane;
constexpr index_t kw = WarpGemm::WarpGemmAttribute::Impl::kABKLane;
constexpr index_t kv = Alignment;
constexpr index_t nw = WarpGemm::WarpGemmAttribute::Impl::kAMLane;
constexpr index_t kw = WarpGemm::WarpGemmAttribute::Impl::kABKLane;
constexpr index_t waveflatten = kw * nw * kv;
const index_t kr = a_.k / (k1 * k2);
const index_t nr = a_.n / nw;
const index_t kr = a_.k / (k1 * k2);
const index_t nr = a_.n / nw;
auto tmp = make_naive_tensor_view_packed<address_space_enum::global>(
p_dst,
make_tuple(nr, kr, waveflatten),
@@ -387,8 +387,8 @@ struct matrix_core_swizzle_kernel
constexpr index_t nw = WarpGemm::WarpGemmAttribute::Impl::kAMLane;
constexpr index_t kw = WarpGemm::WarpGemmAttribute::Impl::kABKLane;
constexpr index_t waveflatten_tile = kw * nw * kv;
constexpr index_t nr_tile = NPerBlock / nw;
constexpr index_t kr_tile = KPerBlock / (kw * kv);
constexpr index_t nr_tile = NPerBlock / nw;
constexpr index_t kr_tile = KPerBlock / (kw * kv);
return make_tile_window(dst_view,
make_tuple(number<nr_tile>{},
number<kr_tile>{},

3
example/ck_tile/10_rmsnorm2d/example_rmsnorm2d_fwd.cpp
View File

@@ -183,8 +183,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
}
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride
std::cout << "[" << data_type << "]" << " m:" << m << ", n:" << n << ", stride:" << stride
<< ", s:" << USEModelSensitive << ", valid:" << (pass ? "y" : "n") << std::flush
<< std::endl;
}

3
example/ck_tile/10_rmsnorm2d/rmsnorm2d_fwd.cpp
View File

@@ -193,8 +193,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
return base_str;
}();
std::cout << "[" << prec_str << "]"
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride
std::cout << "[" << prec_str << "]" << " m:" << m << ", n:" << n << ", x_stride:" << x_stride
<< ", xr_stride:" << xr_stride << ", y_stride:" << y_stride
<< ", yr_stride:" << yr_stride << ", s:" << use_model_sensitive_rmsnorm << std::flush;

4
example/ck_tile/11_add_rmsnorm2d_rdquant/add_rmsnorm2d_rdquant_fwd.cpp
View File

@@ -105,8 +105,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
b_buf.ToDevice(b_host.data());
gamma_buf.ToDevice(gamma_host.data());
std::cout << "[" << input_data_type << ", " << quantized_data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride << std::flush;
std::cout << "[" << input_data_type << ", " << quantized_data_type << "]" << " m:" << m
<< ", n:" << n << ", stride:" << stride << std::flush;
add_rmsnorm2d_rdquant_fwd_traits traits{input_data_type, quantized_data_type, SaveX};

3
example/ck_tile/11_add_rmsnorm2d_rdquant/example_add_rmsnorm2d_rdquant_fwd.cpp
View File

@@ -256,8 +256,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
}
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride
std::cout << "[" << data_type << "]" << " m:" << m << ", n:" << n << ", stride:" << stride
<< ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
}

7
example/ck_tile/12_smoothquant/example_smoothquant.cpp
View File

@@ -216,10 +216,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
}
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride
<< ", y_stride:" << y_stride << ", valid:" << (pass ? "y" : "n") << std::flush
<< std::endl;
std::cout << "[" << data_type << "]" << " m:" << m << ", n:" << n
<< ", x_stride:" << x_stride << ", y_stride:" << y_stride
<< ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
}
return pass;

5
example/ck_tile/12_smoothquant/smoothquant.cpp
View File

@@ -93,9 +93,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
x_buf.ToDevice(x_host.data());
smscale_buf.ToDevice(smscale_host.data());
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride << ", y_stride:" << y_stride
<< std::flush;
std::cout << "[" << data_type << "]" << " m:" << m << ", n:" << n << ", x_stride:" << x_stride
<< ", y_stride:" << y_stride << std::flush;
smoothquant_traits traits{data_type};

30
example/ck_tile/13_moe_sorting/moe_sorting.cpp
View File

@@ -228,20 +228,26 @@ bool test_moe_sorting(ck_tile::ArgParser args)
moe_sorting_trait trait{
index_prec, weight_prec, local_expert_masking, clear_inside, dispatch_policy};
moe_sorting_args karg
{
topk_ids_dev.GetDeviceBuffer(), weights_dev.GetDeviceBuffer(),
local_expert_masking ? local_expert_masking_dev.GetDeviceBuffer() : nullptr,
is_local_token ? local_tokens_dev.GetDeviceBuffer() : nullptr,
sorted_ids_dev.GetDeviceBuffer(), sorted_weights_dev.GetDeviceBuffer(),
sorted_expert_ids_dev.GetDeviceBuffer(), sorted_id_cnt_dev.GetDeviceBuffer(),
moe_buf_bytes > 0 ? moe_buf_dev.GetDeviceBuffer() : nullptr,
workspace_size != 0 ? moe_sorting_ws.GetDeviceBuffer() : nullptr, tokens, unit_size,
num_experts, topk,
moe_sorting_args karg{topk_ids_dev.GetDeviceBuffer(),
weights_dev.GetDeviceBuffer(),
local_expert_masking ? local_expert_masking_dev.GetDeviceBuffer()
: nullptr,
is_local_token ? local_tokens_dev.GetDeviceBuffer() : nullptr,
sorted_ids_dev.GetDeviceBuffer(),
sorted_weights_dev.GetDeviceBuffer(),
sorted_expert_ids_dev.GetDeviceBuffer(),
sorted_id_cnt_dev.GetDeviceBuffer(),
moe_buf_bytes > 0 ? moe_buf_dev.GetDeviceBuffer() : nullptr,
workspace_size != 0 ? moe_sorting_ws.GetDeviceBuffer() : nullptr,
tokens,
unit_size,
num_experts,
topk,
#if MOE_SORTING_FMOE_2D_BUF
moe_buf_interm_dim, moe_buf_elem_bytes
moe_buf_interm_dim,
moe_buf_elem_bytes
#else
static_cast<ck_tile::long_index_t>(moe_buf_size * sizeof(float))
static_cast<ck_tile::long_index_t>(moe_buf_size * sizeof(float))
#endif
};

60
example/ck_tile/13_moe_sorting/moe_sorting_api.cpp
View File

@@ -40,11 +40,11 @@
constexpr bool local_expert_masking = local_expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemEx<index_t, \
ms_weight_type, \
sub_token_tile, \
sub_token_onshot, \
local_expert_masking, \
local_token>; \
ms_weight_type, \
sub_token_tile, \
sub_token_onshot, \
local_expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -200,11 +200,11 @@ float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_confi
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P0<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -218,11 +218,11 @@ float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_confi
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P1<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -236,11 +236,11 @@ float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_confi
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P2<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -254,11 +254,11 @@ float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_confi
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P3<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -273,11 +273,11 @@ float moe_sorting(moe_sorting_trait t, moe_sorting_args a, ck_tile::stream_confi
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P23<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \

6
example/ck_tile/14_moe_smoothquant/moe_smoothquant.cpp
View File

@@ -124,9 +124,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
smscale_buf.ToDevice(smscale_host.data());
topk_ids_buf.ToDevice(topk_ids_host.data());
std::cout << "[" << prec_i << "-" << prec_o << "]"
<< " tokens:" << tokens << ", hidden_size:" << hidden_size << ", stride:" << stride
<< ", experts:" << experts << ", topk:" << topk << std::flush;
std::cout << "[" << prec_i << "-" << prec_o << "]" << " tokens:" << tokens
<< ", hidden_size:" << hidden_size << ", stride:" << stride << ", experts:" << experts
<< ", topk:" << topk << std::flush;
moe_smoothquant_traits traits{prec_i, prec_o};

38
example/ck_tile/15_fused_moe/instances/fused_moe_api.cpp
View File

@@ -25,27 +25,27 @@ float fused_moe(fused_moe_traits t, fused_moe_args a, const ck_tile::stream_conf
}();
auto t0 = fused_moesorting_trait{"int32", "fp32", t.local_expert_masking};
auto a0 = fused_moesorting_args
{
a.topk_ids_ptr, // const void* p_topk_ids;
a.topk_weight_ptr, // const void* p_weights;
a.local_expert_mask_ptr, // const void* p_local_expert_mask;
a.local_tokens,
a.sorted_token_ids_ptr, // void* p_sorted_token_ids;
a.sorted_weight_ptr, // void* p_sorted_weights;
a.sorted_expert_ids_ptr, // void* p_sorted_expert_ids;
a.num_sorted_tiles_ptr, // void* p_total_tokens_post_pad;
a.o_ptr, // void* p_moe_buf;
a.ws_ptr, // void* p_ws;
a.num_tokens, // index_t tokens;
a.block_m, // index_t unit_size;
a.num_experts, // index_t num_experts;
a.topk, // index_t topk;
auto a0 = fused_moesorting_args{
a.topk_ids_ptr, // const void* p_topk_ids;
a.topk_weight_ptr, // const void* p_weights;
a.local_expert_mask_ptr, // const void* p_local_expert_mask;
a.local_tokens,
a.sorted_token_ids_ptr, // void* p_sorted_token_ids;
a.sorted_weight_ptr, // void* p_sorted_weights;
a.sorted_expert_ids_ptr, // void* p_sorted_expert_ids;
a.num_sorted_tiles_ptr, // void* p_total_tokens_post_pad;
a.o_ptr, // void* p_moe_buf;
a.ws_ptr, // void* p_ws;
a.num_tokens, // index_t tokens;
a.block_m, // index_t unit_size;
a.num_experts, // index_t num_experts;
a.topk, // index_t topk;
#if MOE_SORTING_FMOE_2D_BUF
a.stride_token, o_data_bytes,
a.stride_token,
o_data_bytes,
#else
static_cast<ck_tile::long_index_t>(a.num_tokens) *
a.stride_token* o_data_bytes // index_t moe_buf_bytes;
static_cast<ck_tile::long_index_t>(a.num_tokens) * a.stride_token *
o_data_bytes // index_t moe_buf_bytes;
#endif
};

10
example/ck_tile/15_fused_moe/instances/fused_moegemm_api_internal.hpp
View File

@@ -16,11 +16,11 @@ float fused_moegemm_(const ck_tile::stream_config& s, fused_moegemm_args a)
{
using f_traits = ck_tile::FusedMoeGemmTraits<Ts_::GateOnly, Ts_::FusedQuant == 1, 1 /*atomic*/>;
using f_shape = ck_tile::FusedMoeGemmShape<typename Ts_::BlockTile_0,
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0,
typename Ts_::BlockTile_1,
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0>;
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0,
typename Ts_::BlockTile_1,
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0>;
constexpr auto get_activation_ = []() {
if constexpr(Ts_::Activation == 0)

60
example/ck_tile/15_fused_moe/instances/fused_moesorting_api.cpp
View File

@@ -40,11 +40,11 @@
constexpr bool local_expert_masking = local_expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemEx<index_t, \
ms_weight_type, \
sub_token_tile, \
sub_token_onshot, \
local_expert_masking, \
local_token>; \
ms_weight_type, \
sub_token_tile, \
sub_token_onshot, \
local_expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -204,11 +204,11 @@ float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_til
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P0<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -222,11 +222,11 @@ float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_til
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P1<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -240,11 +240,11 @@ float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_til
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P2<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -258,11 +258,11 @@ float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_til
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P3<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
@@ -277,11 +277,11 @@ float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_til
constexpr bool expert_masking = expert_masking_; \
constexpr bool local_token = local_token_; \
using ms_problem = ck_tile::MoeSortingProblemMp<ms_index_t, \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
ms_weight_type, \
mesh_type_, \
unroll_num, \
expert_masking, \
local_token>; \
using kernel = ck_tile::MoeSortingMultiPhaseKernel_P23<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \

3
example/ck_tile/15_fused_moe/main.cpp
View File

@@ -218,8 +218,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
return std::string(", st:") + std::to_string(stride);
}();
std::cout << "[" << api_str << "|" << prec_str << "]"
<< " t:" << tokens;
std::cout << "[" << api_str << "|" << prec_str << "]" << " t:" << tokens;
if(is_local_token)
{

7
example/ck_tile/17_grouped_gemm/grouped_gemm.cpp
View File

@@ -173,10 +173,9 @@ float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
if(s.log_level_ > 0)
{
std::cout << "Launching kernel: " << Kernel::GetName() << " with args:"
<< " grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
<< std::endl;
std::cout << "Launching kernel: " << Kernel::GetName() << " with args:" << " grid: {"
<< grids.x << ", " << grids.y << ", " << grids.z << "}" << ", blocks: {"
<< blocks.x << ", " << blocks.y << ", " << blocks.z << "}" << std::endl;
}
ave_time =

7
example/ck_tile/17_grouped_gemm/grouped_gemm_tileloop.cpp
View File

@@ -138,10 +138,9 @@ float grouped_gemm_tileloop(const ck_tile::stream_config& s,
if(s.log_level_ > 0)
{
std::cout << "Launching kernel: " << Kernel::GetName() << " with args:"
<< " grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
<< std::endl;
std::cout << "Launching kernel: " << Kernel::GetName() << " with args:" << " grid: {"
<< grids.x << ", " << grids.y << ", " << grids.z << "}" << ", blocks: {"
<< blocks.x << ", " << blocks.y << ", " << blocks.z << "}" << std::endl;
}
ave_time =

6
example/ck_tile/17_grouped_gemm/run_grouped_gemm_example.inc
View File

@@ -216,9 +216,9 @@ int run_grouped_gemm_example_with_layouts(int argc,
c_m_n_tensors.push_back(ck_tile::HostTensor<CDataType>(
ck_tile::host_tensor_descriptor(M, N, stride_Cs[i], is_row_major(CLayout{}))));
std::cout << "gemm[" << i << "]"
<< " a_m_k: " << a_m_k_tensors[i].mDesc << " b_k_n: " << b_k_n_tensors[i].mDesc
<< " c_m_n: " << c_m_n_tensors[i].mDesc << std::endl;
std::cout << "gemm[" << i << "]" << " a_m_k: " << a_m_k_tensors[i].mDesc
<< " b_k_n: " << b_k_n_tensors[i].mDesc << " c_m_n: " << c_m_n_tensors[i].mDesc
<< std::endl;
ck_tile::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k_tensors[i]);
ck_tile::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n_tensors[i]);

7
example/ck_tile/19_gemm_multi_d/gemm_multi_d_fp16.cpp
View File

@@ -170,10 +170,9 @@ auto gemm_multi_d(const gemm_multi_d_kargs& args, const ck_tile::stream_config&
if(s.log_level_ > 0)
{
std::cout << "Launching kernel with args:"
<< " grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
<< "}" << std::endl;
std::cout << "Launching kernel with args:" << " grid: {" << grids.x << ", "
<< grids.y << ", " << grids.z << "}" << ", blocks: {" << blocks.x << ", "
<< blocks.y << ", " << blocks.z << "}" << std::endl;
}
ave_time = ck_tile::launch_kernel(

3
example/ck_tile/20_grouped_convolution/run_grouped_convolution_bwd_weight_example.inc
View File

@@ -161,8 +161,7 @@ int run_grouped_conv_bwd_weight_example_with_layouts(
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_);
const ck_tile::index_t GemmK =
weight.get_element_size() / (conv_param.G_ * conv_param.K_);
const ck_tile::index_t GemmK = weight.get_element_size() / (conv_param.G_ * conv_param.K_);
const float max_accumulated_value =
*std::max_element(weight_host_ref.mData.begin(), weight_host_ref.mData.end());
const auto rtol_atol =

48
example/ck_tile/38_block_scale_gemm/gemm_aquant_basic.cpp
View File

@@ -87,24 +87,24 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
tail_number_v>;
using CodegenGemmPipeline = ck_tile::AQuantGemmPipelineAgBgCrCompV3<CodegenPipelineProblem>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ck_tile::tuple<>,
CLayout,
ck_tile::element_wise::PassThrough,
CodegenPipelineProblem::kBlockSize,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
M_Warp,
N_Warp,
M_Warp_Tile,
N_Warp_Tile,
K_Warp_Tile,
transposed_warp_gemm,
ck_tile::memory_operation_enum::set>>;
ck_tile::CShuffleEpilogueProblem<ADataType,
BDataType,
ck_tile::tuple<>,
AccDataType,
CDataType,
ck_tile::tuple<>,
CLayout,
ck_tile::element_wise::PassThrough,
CodegenPipelineProblem::kBlockSize,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
M_Warp,
N_Warp,
M_Warp_Tile,
N_Warp_Tile,
K_Warp_Tile,
transposed_warp_gemm,
ck_tile::memory_operation_enum::set>>;
using Kernel =
ck_tile::AQuantGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
@@ -195,14 +195,18 @@ int run_gemm_example(int argc, char* argv[])
}
else if(data_type == "i4fp8")
{
using TypeConfig = decltype(
GemmQuantTypeConfig<ck_tile::pk_int4_t, ck_tile::fp8_t, float, ck_tile::fp8_t>{});
using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t,
ck_tile::fp8_t,
float,
ck_tile::fp8_t>{});
return run_gemm_example_prec_type<TypeConfig, 128>(a_layout, b_layout, argc, argv);
}
else if(data_type == "i4bf8")
{
using TypeConfig = decltype(
GemmQuantTypeConfig<ck_tile::pk_int4_t, ck_tile::bf8_t, float, ck_tile::bf8_t>{});
using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t,
ck_tile::bf8_t,
float,
ck_tile::bf8_t>{});
return run_gemm_example_prec_type<TypeConfig, 128>(a_layout, b_layout, argc, argv);
}
else if(data_type == "i4f32fp8")

2
example/ck_tile/remod.py
View File

@@ -13,7 +13,7 @@ for p in sorted(Path("./").rglob("*")):
# formatting
for x in all_files:
subprocess.Popen(f'dos2unix {str(x)}', shell=True)
cmd = f'clang-format-12 -style=file -i {str(x)}'
cmd = f'clang-format-18 -style=file -i {str(x)}'
#for xp in x.parents:
#print(get_file_base(x))
subprocess.Popen(cmd, shell=True)