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Unverified Commit 3c5717df authored by Illia Silin's avatar Illia Silin Committed by GitHub
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Merge branch 'develop' into gemm_elementwise_gemm

parents 171b9030 d9f1ead3
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Showing with 982 additions and 607 deletions
example/ck_tile/01_fmha/fmha_bwd.hpp
View file @ 3c5717df
......@@ -14,11 +14,19 @@
#include <utility>
#include <variant>
struct FmhaBwdFp16
{
};
struct FmhaBwdBf16
{
};
template <typename DataType>
struct FmhaBwdTypeConfig;
template <>
struct FmhaBwdTypeConfig<ck_tile::half_t>
struct FmhaBwdTypeConfig<FmhaBwdFp16>
{
using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t;
......@@ -38,7 +46,7 @@ struct FmhaBwdTypeConfig<ck_tile::half_t>
};
template <>
struct FmhaBwdTypeConfig<ck_tile::bf16_t>
struct FmhaBwdTypeConfig<FmhaBwdBf16>
{
using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t;
......@@ -150,113 +158,113 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
// create group mode kernel arguments
if constexpr(FmhaBwdDQDKDVKernel::kIsGroupMode)
{
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dq_acc,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.drop_seed_offset);
return FmhaBwdDQDKDVKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dq_acc,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.drop_seed_offset);
}
else
{ // create batch mode kernel arguments
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dq_acc,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_do,
args.batch_stride_lsed,
args.batch_stride_dq_acc,
args.batch_stride_dk,
args.batch_stride_dv,
args.batch_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.drop_seed_offset);
return FmhaBwdDQDKDVKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dq_acc,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_do,
args.batch_stride_lsed,
args.batch_stride_dq_acc,
args.batch_stride_dk,
args.batch_stride_dv,
args.batch_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.drop_seed_offset);
}
}();
......
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ 3c5717df
......@@ -3,6 +3,7 @@
#include "fmha_fwd.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/ref/naive_attention.hpp"
#include "mask.hpp"
#include "rotary.hpp"
#include "utils.hpp"
......@@ -41,7 +42,7 @@ std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("v", "1", "weather do CPU validation or not")
arg_parser.insert("v", "1", "0:no validation, 2:cpu validation, 2:gpu validation(experimental)")
.insert("mode", "0", "kernel mode. 0:batch, 1:group")
.insert("b", "2", "batch size")
.insert("h", "8", "num of head, for q")
......@@ -62,7 +63,7 @@ auto create_args(int argc, char* argv[])
"-1 to choose s_knew in [1, s] randomly.")
.insert("s_kpad",
"-1",
"seqlen_k stride between 2 tokens, currently used in group-mode only\n"
"seqlen_k stride between 2 batches, currently used in group-mode only\n"
"for kv-cache case, each batch [1,s,h,d]/[1,h,s,d] can have a stride\n"
"along seqlen, instead of packed. same as xformer kv_padding")
.insert("d", "128", "head dim for q, k")
......@@ -142,7 +143,7 @@ auto create_args(int argc, char* argv[])
}
// different threshold for different dtype
template <typename DataType>
template <typename DataTypeConfig>
auto get_elimit(std::string /*init_method*/)
{
double rtol = 1e-3;
......@@ -151,7 +152,7 @@ auto get_elimit(std::string /*init_method*/)
}
template <>
auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/)
auto get_elimit<FmhaFwdBf16>(std::string /*init_method*/)
{
double rtol = 1e-2;
double atol = 1e-2;
......@@ -159,7 +160,7 @@ auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/)
}
template <>
auto get_elimit<ck_tile::fp8_t>(std::string init_method)
auto get_elimit<FmhaFwdFp8>(std::string init_method)
{
if(init_method == "ui" || init_method == "ni")
{
......@@ -261,7 +262,7 @@ int override_num_splits_if_necessary(
return num_splits;
}
template <typename DataType>
template <typename DataTypeConfig>
bool run(const ck_tile::ArgParser& arg_parser)
{
std::string data_type = arg_parser.get_str("prec");
......@@ -294,7 +295,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
#if !CK_TILE_FMHA_FWD_APPENDKV_API
if(seqlen_knew != 0)
{
std::cerr << "kvcache is not supported. ignoring the 's_knew' option" << std::endl;
std::cerr << "fmha_fwd_appendkv() is not enabled. ignoring the 's_knew' option"
<< std::endl;
seqlen_knew = 0;
}
#endif
......@@ -304,8 +306,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
ck_tile::index_t rotary_dim = arg_parser.get_int("rotary_dim");
if constexpr(!(std::is_same_v<DataType, ck_tile::fp16_t> ||
std::is_same_v<DataType, ck_tile::bf16_t>))
if constexpr(!(std::is_same_v<DataTypeConfig, FmhaFwdFp16> ||
std::is_same_v<DataTypeConfig, FmhaFwdBf16>))
{
if(0 < rotary_dim)
{
......@@ -321,6 +323,13 @@ bool run(const ck_tile::ArgParser& arg_parser)
rotary_dim = 0;
}
#endif
// to use fmha_fwd_appendkv(), make sure it's in batch mode
const bool need_append_kvcache = (0 < seqlen_knew || 0 < rotary_dim);
if(need_append_kvcache && mode == mode_enum::group)
{
std::cerr << "fmha_fwd_appendkv() will be invoked. ignoring the 'mode' option" << std::endl;
mode = mode_enum::batch;
}
if(!(rotary_dim <= hdim_q))
{
std::cerr << "rotary_dim should be less than or equal to head dim for q" << std::endl;
......@@ -356,22 +365,26 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< std::endl;
use_cache_batch_idx = false;
}
#endif
if(0 < page_block_size && use_cache_batch_idx)
#else
if(use_cache_batch_idx)
{
std::cerr << "paged-kvcache does not support cache_batch_idx. ignoring the "
"'cache_batch_idx' option"
<< std::endl;
use_cache_batch_idx = false;
if(0 < page_block_size)
{
std::cerr << "paged-kvcache does not support cache_batch_idx. ignoring the "
"'cache_batch_idx' option"
<< std::endl;
use_cache_batch_idx = false;
}
else if(mode == mode_enum::group)
{
std::cerr << "group mode will not use cache_batch_idx. ignoring the "
"'cache_batch_idx' option"
<< std::endl;
use_cache_batch_idx = false;
}
}
// the input tensor layout for kvcache is same as batch mode
const bool need_append_kvcache = (0 < seqlen_knew || 0 < rotary_dim);
#endif
const bool use_kvcache = (need_append_kvcache || use_cache_batch_idx || 0 < page_block_size);
if(use_kvcache && mode != mode_enum::batch)
{
std::cerr << "kvcache enabled. ignoring the 'mode' option" << std::endl;
mode = mode_enum::batch;
}
auto [seqlen_qs, seqlen_ks, seqlen_kpads] =
decode_seqlen(mode,
......@@ -380,7 +393,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
arg_parser.get_str("s_k"),
arg_parser.get_str("s_kpad"),
/*seqlen_k_min=*/0 < seqlen_knew ? seqlen_knew : 0,
use_kvcache);
need_append_kvcache);
// compute kvcache seqlen_k (before appending knew/vnew)
auto cache_seqlen_ks = seqlen_ks;
std::transform(cache_seqlen_ks.begin(),
......@@ -416,25 +429,6 @@ bool run(const ck_tile::ArgParser& arg_parser)
return atoi(squant_str.c_str()) != 0 ? true : false;
}();
float range_q = arg_parser.get_float("range_q");
float range_k = arg_parser.get_float("range_k");
float range_v = arg_parser.get_float("range_v");
float range_p = arg_parser.get_float("range_p");
float range_o = arg_parser.get_float("range_o");
float dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<DataType>::max());
float scale_p = 1.f;
float scale_o = 1.f;
if(squant)
{
scale_s = scale_s * (range_q / dtype_max) * (range_k / dtype_max);
scale_p = dtype_max / range_p;
// scale_p = [max(fp8_t)/range_o] * [range_p/max(fp8_t)] * [range_v/max(fp8_t)]
scale_o = range_p * range_v / range_o / dtype_max;
}
std::string vlayout = arg_parser.get_str("vlayout");
bool lse = arg_parser.get_bool("lse");
......@@ -454,7 +448,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
bool s_randval = false;
if(p_drop > 0.0f && do_validation)
if(p_drop > 0.0f && do_validation != 0)
{
s_randval = true;
}
......@@ -487,7 +481,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
const auto seqstart_k_host = to_seqstarts(seqlen_ks);
const auto seqstart_k_with_padding_host = to_seqstarts(seqlen_kpads);
using TypeConfig = FmhaFwdTypeConfig<DataType>;
using TypeConfig = FmhaFwdTypeConfig<DataTypeConfig>;
using QDataType = typename TypeConfig::QDataType;
using KDataType = typename TypeConfig::KDataType;
......@@ -501,6 +495,28 @@ bool run(const ck_tile::ArgParser& arg_parser)
using OaccDataType = typename TypeConfig::OaccDataType;
using ODataType = typename TypeConfig::ODataType;
float range_q = arg_parser.get_float("range_q");
float range_k = arg_parser.get_float("range_k");
float range_v = arg_parser.get_float("range_v");
float range_p = arg_parser.get_float("range_p");
float range_o = arg_parser.get_float("range_o");
float q_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<QDataType>::max());
float k_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<KDataType>::max());
float v_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<VDataType>::max());
float p_dtype_max = v_dtype_max; // assume p and v is the same type
float o_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<ODataType>::max());
float scale_p = 1.f;
float scale_o = 1.f;
if(squant)
{
scale_s = scale_s * (range_q / q_dtype_max) * (range_k / k_dtype_max);
scale_p = p_dtype_max / range_p;
scale_o = (o_dtype_max / range_o) * (range_p / p_dtype_max) * (range_v / v_dtype_max);
}
// accumulation numbers for performance evaluation
std::size_t flop = 0, num_byte = 0;
auto max_seqlen_q =
......@@ -697,14 +713,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
else if(init_method == "ufq" || init_method == "uf:q" ||
init_method == "3") // suitable for fp8 quantization
{
ck_tile::FillUniformDistribution<QDataType>{-dtype_max, dtype_max, seed}(q_host);
ck_tile::FillUniformDistribution<KDataType>{-dtype_max, dtype_max, seed}(k_host);
ck_tile::FillUniformDistribution<KDataType>{-dtype_max, dtype_max, seed}(knew_host);
ck_tile::FillUniformDistribution<VDataType>{-dtype_max, dtype_max, seed}(v_host);
ck_tile::FillUniformDistribution<VDataType>{-dtype_max, dtype_max, seed}(vnew_host);
ck_tile::FillUniformDistribution<QDataType>{-q_dtype_max, q_dtype_max, seed}(q_host);
ck_tile::FillUniformDistribution<KDataType>{-k_dtype_max, k_dtype_max, seed}(k_host);
ck_tile::FillUniformDistribution<KDataType>{-k_dtype_max, k_dtype_max, seed}(knew_host);
ck_tile::FillUniformDistribution<VDataType>{-v_dtype_max, v_dtype_max, seed}(v_host);
ck_tile::FillUniformDistribution<VDataType>{-v_dtype_max, v_dtype_max, seed}(vnew_host);
// bias_fp8 = qscale_bias * bias_fp32
float qscale_bias = (dtype_max / range_q) * (dtype_max / range_k);
float qscale_bias = (q_dtype_max / range_q) * (k_dtype_max / range_k);
// Assume bias is in [-1.f, 1.f] in original fp32
ck_tile::FillUniformDistribution<BiasDataType>{-qscale_bias, qscale_bias, seed}(bias_host);
}
......@@ -741,8 +757,10 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
ck_tile::DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t));
ck_tile::DeviceMem seqlen_k_buf(
use_kvcache || 0 <= seqlen_kpads[0] ? seqlen_ks.size() * sizeof(int32_t) : 0);
ck_tile::DeviceMem seqlen_k_buf((mode == mode_enum::batch && use_kvcache) ||
0 <= seqlen_kpads[0]
? seqlen_ks.size() * sizeof(int32_t)
: 0);
ck_tile::DeviceMem cache_seqlen_k_buf(
need_append_kvcache ? cache_seqlen_ks.size() * sizeof(int32_t) : 0);
ck_tile::DeviceMem rotary_cos_buf(rotary_cos_host.get_element_space_size_in_bytes());
......@@ -763,7 +781,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
seqstart_q.ToDevice(seqstart_q_host.data());
seqstart_k.ToDevice(seqlen_kpads[0] < 0 ? seqstart_k_host.data()
: seqstart_k_with_padding_host.data());
seqlen_k_buf.ToDevice(use_kvcache || 0 <= seqlen_kpads[0] ? seqlen_ks.data() : nullptr);
seqlen_k_buf.ToDevice((mode == mode_enum::batch && use_kvcache) || 0 <= seqlen_kpads[0]
? seqlen_ks.data()
: nullptr);
cache_seqlen_k_buf.ToDevice(need_append_kvcache ? cache_seqlen_ks.data() : nullptr);
rotary_cos_buf.ToDevice(rotary_cos_host.data());
rotary_sin_buf.ToDevice(rotary_sin_host.data());
......@@ -976,8 +996,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
(mode == mode_enum::group ? seqstart_q.GetDeviceBuffer() : nullptr);
args.seqstart_k_ptr =
(mode == mode_enum::group ? seqstart_k.GetDeviceBuffer() : nullptr);
args.seqlen_k_ptr =
(use_kvcache || 0 <= k_paddings_[0] ? seqlen_k_buf.GetDeviceBuffer() : nullptr);
args.seqlen_k_ptr = ((mode == mode_enum::batch && use_kvcache) || 0 <= k_paddings_[0]
? seqlen_k_buf.GetDeviceBuffer()
: nullptr);
args.seqlen_k = shape_seqlen_k; // unused in group mode (or kvcache enabled)
args.max_seqlen_q = max_seqlen_q;
......@@ -1029,6 +1050,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
(0 < page_block_size ? block_table_buf.GetDeviceBuffer() : nullptr);
args.batch_stride_block_table = batch_stride_block_table;
args.page_block_size = page_block_size;
args.is_gappy = false; // use 'false' for flash-attention integration
args.cache_batch_idx =
(use_cache_batch_idx ? cache_batch_idx_buf.GetDeviceBuffer() : nullptr);
......@@ -1100,25 +1122,76 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< std::setprecision(2) << tflops << " TFlops, " << std::setprecision(2) << gb_per_sec
<< " GB/s" << std::flush;
if(!do_validation)
if(do_validation == 0)
{
std::cout << std::flush << std::endl;
return true;
}
if(do_validation == 2)
{
// NOTE: use gpu to do validation
ck_tile::naive_attention_fwd_traits naive_t;
naive_t.q_type = data_type;
naive_t.k_type = data_type;
naive_t.v_type = data_type;
naive_t.o_type = data_type;
naive_t.q_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.k_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.v_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.o_layout = o_perm == 1 ? "bhsd" : "bshd";
naive_t.variation = 0; // TODO?
naive_t.quant_algo = 0;
ck_tile::DeviceMem o_naive_buf(o_host.get_element_space_size_in_bytes());
ck_tile::naive_attention_fwd_args naive_a;
naive_a.q_ptr = q_buf.GetDeviceBuffer();
naive_a.k_ptr = k_buf.GetDeviceBuffer();
naive_a.v_ptr = v_buf.GetDeviceBuffer();
naive_a.o_ptr = o_naive_buf.GetDeviceBuffer();
naive_a.scale_s = scale_s;
naive_a.context_len_ptr = nullptr; // used when seqlen kv come from a pointer
naive_a.page_table_ptr =
nullptr; // [batch, num_blocks] seqlen_kv is in different block(paged attn)
naive_a.hdim = hdim_q;
naive_a.hdim_v = hdim_v; // could be cross-attn, where V and Q/K hdim are different
naive_a.batch_q = batch;
naive_a.batch_kv = batch;
naive_a.batch_ratio_kv = 1; // batch_q / batch_kv
naive_a.seqlen_q = seqlen_qs[0];
naive_a.seqlen_kv = seqlen_ks[0]; // if context_len_ptr is not nullptr, ignore this field
naive_a.nhead_q = nhead;
naive_a.nhead_kv = nhead_k;
naive_a.nhead_ratio_kv = naive_a.nhead_q / naive_a.nhead_kv; // nhead_q / nhead_kv
naive_a.page_size = 0; // if paged, the seqlen-kv for each block
ck_tile::stream_config naive_s{};
naive_attention_fwd(naive_t, naive_a, naive_s);
auto o_naive_ref = o_naive_buf.ToHost<ODataType>();
o_buf.FromDevice(o_host.data()); // TODO: ugly
auto [rtol_, atol_] = get_elimit<DataTypeConfig>(init_method);
bool pass_ = ck_tile::check_err(
o_host, o_naive_ref, std::string("OUT Error: Incorrect results!"), rtol_, atol_);
std::cout << ", valid:" << (pass_ ? "y" : "n") << std::flush << std::endl;
return pass_;
}
o_buf.FromDevice(o_host.data());
lse_buf.FromDevice(lse_host.data());
randval_buf.FromDevice(randval_host.data());
auto p_compute_element_func = [&]() {
if constexpr(std::is_same_v<DataType, ck_tile::fp8_t>)
if constexpr(std::is_same_v<DataTypeConfig, ck_tile::fp8_t>)
return ck_tile::scales{scale_p};
else
return ck_tile::identity{};
}();
auto oacc_element_func = [&]() {
if constexpr(std::is_same_v<DataType, ck_tile::fp8_t>)
if constexpr(std::is_same_v<DataTypeConfig, ck_tile::fp8_t>)
return ck_tile::composes(ck_tile::saturates<ck_tile::fp8_t>{},
ck_tile::scales{scale_o});
else
......@@ -1168,7 +1241,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
decltype(q_host_ref) q_host_ref_ro(q_host_ref.get_lengths());
auto [rotary_cos_slice, rotary_sin_slice] =
auto [rotary_cos_slice, rotary_sin_slice] =
slice_rotary_cos_sin(rotary_cos_host, rotary_sin_host, cache_seqlen_ks[wb], real_seqlen_q);
ck_tile::reference_batched_rotary_position_embedding(
......@@ -1184,13 +1257,13 @@ bool run(const ck_tile::ArgParser& arg_parser)
k_host_ref.ForEach([&](auto& self, auto i) {
self(i) = k_host(block_table_host(wb, i[1] / page_block_size), i[0] / nr, i[1] % page_block_size, i[2]);
});
} else {
} else {
k_host_ref.ForEach([&](auto& self, auto i) {
self(i) = k_host(block_table_host(wb, i[1] / page_block_size), i[1] % page_block_size, i[0] / nr, i[2]);
});
}
} else
#endif
#endif
{
if(i_perm) k_host_ref.ForEach([&](auto& self, auto i) { self(i) = k_host(cache_b_idx, i[0] / nr, i[1] + key_offset, i[2]); });
else k_host_ref.ForEach([&](auto& self, auto i) { self(i) = k_host(cache_b_idx, i[1] + key_offset, i[0] / nr, i[2]); });
......@@ -1211,7 +1284,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
knew_host_ref_ro.emplace(knew_host_ref.get_lengths());
auto [rotary_cos_slice, rotary_sin_slice] =
auto [rotary_cos_slice, rotary_sin_slice] =
slice_rotary_cos_sin(rotary_cos_host, rotary_sin_host, cache_seqlen_ks[wb], seqlen_knew);
ck_tile::reference_batched_rotary_position_embedding(
......@@ -1233,19 +1306,19 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(0 < page_block_size) {
if(is_v_rowmajor) {
if(i_perm) {
v_host_ref.ForEach([&](auto& self, auto i) {
self(i) = v_host(block_table_host(wb, i[2] / page_block_size), i[0] / nr, i[2] % page_block_size, i[1]);
v_host_ref.ForEach([&](auto& self, auto i) {
self(i) = v_host(block_table_host(wb, i[2] / page_block_size), i[0] / nr, i[2] % page_block_size, i[1]);
});
} else {
v_host_ref.ForEach([&](auto& self, auto i) {
v_host_ref.ForEach([&](auto& self, auto i) {
self(i) = v_host(block_table_host(wb, i[2] / page_block_size), i[2] % page_block_size, i[0] / nr, i[1]);
});
}
}
else
else
{
if(i_perm) {
v_host_ref.ForEach([&](auto& self, auto i) {
if(i_perm) {
v_host_ref.ForEach([&](auto& self, auto i) {
self(i) = v_host(block_table_host(wb, i[2] / page_block_size), i[0] / nr, i[1], i[2] % page_block_size);
});
} else {
......@@ -1440,7 +1513,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
else o_host_result.ForEach([&](auto& self, auto idx) { self(idx) = o_host(b_idx, idx[1] + query_offset, idx[0], idx[2]); });
// clang-format on
auto [rtol, atol] = get_elimit<DataType>(init_method);
auto [rtol, atol] = get_elimit<DataTypeConfig>(init_method);
bool cur_pass = ck_tile::check_err(
o_host_result, o_host_ref, std::string("OUT Error: Incorrect results!"), rtol, atol);
pass &= cur_pass;
......@@ -1497,15 +1570,15 @@ int main(int argc, char* argv[])
const std::string data_type = arg_parser.get_str("prec");
if(data_type == "fp16")
{
return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
return run<FmhaFwdFp16>(arg_parser) ? 0 : -2;
}
else if(data_type == "bf16")
{
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
return run<FmhaFwdBf16>(arg_parser) ? 0 : -2;
}
else if(data_type == "fp8")
{
return run<ck_tile::fp8_t>(arg_parser) ? 0 : -2;
return run<FmhaFwdFp8>(arg_parser) ? 0 : -2;
}
return -3;
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ 3c5717df
......@@ -16,11 +16,35 @@
#include <utility>
#include <variant>
struct FmhaFwdFp16
{
};
struct FmhaFwdBf16
{
};
struct FmhaFwdFp8
{
};
struct FmhaFwdBf8
{
};
struct FmhaFwdFp8Fp16
{
};
struct FmhaFwdFp8Bf16
{
};
template <typename DataType>
struct FmhaFwdTypeConfig;
template <>
struct FmhaFwdTypeConfig<ck_tile::half_t>
struct FmhaFwdTypeConfig<FmhaFwdFp16>
{
using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t;
......@@ -36,7 +60,7 @@ struct FmhaFwdTypeConfig<ck_tile::half_t>
};
template <>
struct FmhaFwdTypeConfig<ck_tile::bf16_t>
struct FmhaFwdTypeConfig<FmhaFwdBf16>
{
using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t;
......@@ -52,7 +76,7 @@ struct FmhaFwdTypeConfig<ck_tile::bf16_t>
};
template <>
struct FmhaFwdTypeConfig<ck_tile::fp8_t>
struct FmhaFwdTypeConfig<FmhaFwdFp8>
{
using QDataType = ck_tile::fp8_t;
using KDataType = ck_tile::fp8_t;
......@@ -68,7 +92,7 @@ struct FmhaFwdTypeConfig<ck_tile::fp8_t>
};
template <>
struct FmhaFwdTypeConfig<ck_tile::bf8_t>
struct FmhaFwdTypeConfig<FmhaFwdBf8>
{
using QDataType = ck_tile::bf8_t;
using KDataType = ck_tile::bf8_t;
......@@ -165,6 +189,8 @@ struct fmha_fwd_splitkv_args
void* block_table_ptr;
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
bool is_gappy; // differentiate seqstart_k_ptr usage. only used if 'block_table_ptr' is not
// nullptr.
const void* cache_batch_idx;
......@@ -173,9 +199,21 @@ struct fmha_fwd_splitkv_args
// seqlen_k = kargs.seqlen_k
// group mode: seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// kvcache mode (use same kernel as batch mode):
// or kargs.seqlen_k_ptr[b]
//
// batch mode (kvcache):
// seqlen_q = kargs.seqlen_q
// seqlen_k = kargs.seqlen_k_ptr[b]
// group mode (kvcache):
// seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
//
// when is_gappy=true:
// seqlen_k = kargs.seqlen_k_ptr[b]
// seqstart_k_ptr[b] now store local offset of each batch
//
// when is_gappy=false:
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// or kargs.seqlen_k_ptr[b]
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void* seqlen_k_ptr;
......@@ -251,7 +289,7 @@ struct fmha_fwd_appendkv_args
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
const void* cache_batch_idx;
const void* cache_batch_idx; // only used if block_table_ptr is nullptr -> batch mode (kvcache)
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
......@@ -278,92 +316,102 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
// create group mode kernel arguments
if constexpr(FmhaKernel::kIsGroupMode)
{
return FmhaKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
return FmhaKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
else
{ // create batch mode kernel arguments
return FmhaKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_lse,
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
return FmhaKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_lse,
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
}();
dim3 grids = FmhaKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v);
return ck_tile::make_tuple(kargs, grids);
if constexpr(FmhaKernel::kIsGroupMode)
{
dim3 grids = FmhaKernel::GridSize(
args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.seqlen_k_ptr != nullptr);
return ck_tile::make_tuple(kargs, grids);
}
else
{
dim3 grids =
FmhaKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, false);
return ck_tile::make_tuple(kargs, grids);
}
}
template <typename Kernel>
......@@ -389,6 +437,10 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.nhead_q,
args.nhead_q / args.nhead_k,
args.num_splits,
args.block_table_ptr,
args.batch_stride_block_table,
args.page_block_size,
args.is_gappy,
args.scale_s,
args.scale_p,
args.stride_q,
......@@ -458,8 +510,8 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
}
}();
dim3 grids =
Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.num_splits);
dim3 grids = Kernel::GridSize(
args.batch, args.nhead_q, args.nhead_k, args.max_seqlen_q, args.hdim_v, args.num_splits);
return ck_tile::make_tuple(kargs, grids);
}
......@@ -667,7 +719,6 @@ std::string fmha_fwd_splitkv_get_name_();
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::index_t kM0_,
ck_tile::index_t kN1_,
bool kStoreLse_,
bool kDoFp8StaticQuant_,
......@@ -678,7 +729,6 @@ struct fmha_fwd_splitkv_combine_traits_
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr ck_tile::index_t kM0 = kM0_;
static constexpr ck_tile::index_t kN1 = kN1_;
static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
......
example/ck_tile/01_fmha/utils.hpp
View file @ 3c5717df
......@@ -145,7 +145,7 @@ decode_seqlen(mode_enum mode,
std::string k_val,
std::string k_pad_val,
ck_tile::index_t seqlen_k_min = 0,
bool use_kvcache = false,
bool need_append_kvcache = false,
std::optional<unsigned> seed = std::nullopt)
{
#define _S2I_(str_) static_cast<ck_tile::index_t>(std::atoi((str_).c_str()))
......@@ -159,7 +159,7 @@ decode_seqlen(mode_enum mode,
const ck_tile::index_t seqlen_k_max = (k < 0 ? q : k);
std::vector<ck_tile::index_t> seqlen_ks(batch, seqlen_k_max);
if(1 < batch && use_kvcache)
if(1 < batch && need_append_kvcache)
{
// to keep the original s_k value, we always use seqlen_k_max in first batch
randints(std::next(seqlen_ks.begin()),
......
example/ck_tile/02_layernorm2d/CMakeLists.txt
View file @ 3c5717df
......@@ -33,7 +33,7 @@ target_sources(${EXAMPLE_LAYERNORM2D_FWD} PRIVATE ${LAYERNORM2D_FWD_GEN_BLOBS})
set(EXAMPLE_LAYERNORM2D_FWD_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
list(APPEND EXAMPLE_LAYERNORM2D_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal)
list(APPEND EXAMPLE_LAYERNORM2D_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal --offload-compress)
target_compile_options(${EXAMPLE_LAYERNORM2D_FWD} PRIVATE ${EXAMPLE_LAYERNORM2D_FWD_COMPILE_OPTIONS})
......
example/ck_tile/02_layernorm2d/README.md
View file @ 3c5717df
......@@ -59,7 +59,7 @@ args:
-kname print kernel name or not (default:1)
-prec_i input precision (default:fp16)
-prec_o output precision, set auto will be the same as input (default:auto)
-prec_sx output quant scale type, set auto will be the same as input. used when fquant=1 (default:auto)
-prec_sm output quant scale type, set auto will be the same as input. used when fquant=1 (default:auto)
-prec_sy output quant scale type, set auto will be the same as input. used when fquant=1 or 2 (default:auto)
-fadd fused-add, 0:no fused add, 1:preadd+store, 2:preadd only (default:0)
-fquant fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant (default:0)
......@@ -69,7 +69,7 @@ args:
```
## limitations
Note that `fquant=2`, `fadd=2`, `prec_sx/prec_sy` other than `fp32` are not by default generated. Though our kernel template suppor this. (TBD: add some flag in generate.py) to generate those instance on demand. Beside, `N>8192` case will by default using two-pass pipeline, and `-fquant=1/2` are not supported yet. If need suport `N>8192` and `fused+residual+store`, you can use this example together with `12_smoothquant`, to construct layernorm+residual, and smoothquant, 2 kernels for this purpose.
Note that `fquant=2`, `fadd=2`, `prec_sm/prec_sy` other than `fp32` are not by default generated. Though our kernel template suppor this. (TBD: add some flag in generate.py) to generate those instance on demand. Beside, `N>8192` case will by default using two-pass pipeline, and `-fquant=1/2` are not supported yet. If need suport `N>8192` and `fused+residual+store`, you can use this example together with `12_smoothquant`, to construct layernorm+residual, and smoothquant, 2 kernels for this purpose.
```
# some case
......
example/ck_tile/02_layernorm2d/generate.py
View file @ 3c5717df
# SPDX-License-Identifier: MIT
# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
# Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
# generate kernel instances to speed up compilation
import argparse
......@@ -23,6 +23,10 @@ def get_if_str(idx, total, lase_else = True):
else:
return 'else if'
XBIAS_ENUM_STR_MAP = [
'no',
'xbias'] # pre-norm add bias
FUSED_ADD_ENUM_STR_MAP = [
'no',
'pras', # pre-norm
......@@ -35,7 +39,8 @@ FUSED_FUSED_SWEEP_STR_MAP = [
DATA_TYPE_MAP = {'fp32' : 'float',
'fp16' : 'ck_tile::fp16_t',
'bf16' : 'ck_tile::bf16_t',
'int8' : 'ck_tile::int8_t'}
'int8' : 'ck_tile::int8_t',
'fp8' : 'ck_tile::fp8_t'}
def BOOL_MAP(b_) -> str:
if b_:
......@@ -48,7 +53,7 @@ class layernorm_fwd_codegen:
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <typename XDataType_,
typename YDataType_,
typename XScaleDataType_,
typename SmoothScaleDataType_,
typename YScaleDataType_,
ck_tile::index_t Repeat_M_, // each thread repeat along M
ck_tile::index_t Repeat_N_, // each thread repeat along N
......@@ -58,14 +63,16 @@ template <typename XDataType_,
bool kPadN_,
bool kSaveMeanInvStd_,
bool kFastFDiv_,
bool kWelford_,
bool kTwoPass_,
ck_tile::index_t kXbias_ = 0,
ck_tile::index_t kFusedAdd_ = 0,
ck_tile::index_t kFusedQuant_ = 0>
struct layernorm2d_fwd_traits_
{
using XDataType = ck_tile::remove_cvref_t<XDataType_>;
using YDataType = ck_tile::remove_cvref_t<YDataType_>;
using XScaleDataType = ck_tile::remove_cvref_t<XScaleDataType_>;
using SmoothScaleDataType = ck_tile::remove_cvref_t<SmoothScaleDataType_>;
using YScaleDataType = ck_tile::remove_cvref_t<YScaleDataType_>;
static constexpr bool is_warp_per_row = ThreadPerBlock_N_ <= warpSize;
......@@ -120,14 +127,16 @@ struct layernorm2d_fwd_traits_
static constexpr bool kPadN = kPadN_;
static constexpr bool kSaveMeanInvStd = kSaveMeanInvStd_;
static constexpr bool kFastFDiv = kFastFDiv_;
static constexpr bool kWelford = kWelford_;
static constexpr bool kTwoPass = kTwoPass_;
static constexpr ck_tile::index_t kXbias = kXbias_;
static constexpr ck_tile::index_t kFusedAdd = kFusedAdd_;
static constexpr ck_tile::index_t kFusedQuant = kFusedQuant_;
};
template <typename XDataType_,
typename YDataType_,
typename XScaleDataType_,
typename SmoothScaleDataType_,
typename YScaleDataType_,
ck_tile::index_t Repeat_M_, // each thread repeat along M
ck_tile::index_t Repeat_N_, // each thread repeat along N
......@@ -137,12 +146,14 @@ template <typename XDataType_,
bool kPadN_,
bool kSaveMeanInvStd_,
bool kFastFDiv_,
bool kWelford_,
bool kTwoPass_,
int kXbias_,
int kFusedAdd_,
int kFusedQuant_>
using traits_ = layernorm2d_fwd_traits_<XDataType_,
YDataType_,
XScaleDataType_,
SmoothScaleDataType_,
YScaleDataType_,
Repeat_M_,
Repeat_N_,
......@@ -152,13 +163,15 @@ using traits_ = layernorm2d_fwd_traits_<XDataType_,
kPadN_,
kSaveMeanInvStd_,
kFastFDiv_,
kWelford_,
kTwoPass_,
kXbias_,
kFusedAdd_,
kFusedQuant_>;
"""
API_COMMON_HEADER = """
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include <ck_tile/core.hpp>
#include "layernorm2d_fwd.hpp"
......@@ -177,26 +190,29 @@ float layernorm2d_fwd_(const S& s, A a)
{{
using XDataType = typename Traits_::XDataType;
using YDataType = typename Traits_::YDataType;
using XScaleDataType = typename Traits_::XScaleDataType;
using SmoothScaleDataType = typename Traits_::SmoothScaleDataType;
using YScaleDataType = typename Traits_::YScaleDataType;
using ComputeDataType = typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::ComputeDataType;
using ComputeDataType = typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::ComputeDataType;
using PipelineTraits = ck_tile::Layernorm2dFwdTraits<Traits_::kPadN,
Traits_::kSaveMeanInvStd,
Traits_::kFastFDiv,
Traits_::kWelford,
Traits_::kTwoPass,
static_cast<ck_tile::Layernorm2dXBiasEnum>(Traits_::kXbias),
static_cast<ck_tile::Layernorm2dFusedAddEnum>(Traits_::kFusedAdd),
static_cast<ck_tile::Layernorm2dFusedQuantEnum>(Traits_::kFusedQuant)>;
using PipelineProblem = ck_tile::Layernorm2dFwdPipelineProblem<
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::XDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::GammaDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::BetaDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::ComputeDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::YDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::MeanDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::InvStdDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::XScaleDataType,
typename LayerNormTypeConfig<XDataType, YDataType, XScaleDataType, YScaleDataType>::YScaleDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::XDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::XBiasDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::GammaDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::BetaDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::ComputeDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::YDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::MeanDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::InvStdDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::SmoothScaleDataType,
typename LayerNormTypeConfig<XDataType, YDataType, SmoothScaleDataType, YScaleDataType>::YScaleDataType,
typename Traits_::Shape,
PipelineTraits>;
......@@ -204,12 +220,13 @@ float layernorm2d_fwd_(const S& s, A a)
using TwoPassPipeline = ck_tile::Layernorm2dFwdPipelineTwoPass<PipelineProblem>;
using Pipeline = std::conditional_t<Traits_::kTwoPass, TwoPassPipeline, OnePassPipeline>;
using Default2DEpilogueProblem = ck_tile::Default2DEpilogueProblem<ComputeDataType, YDataType, false, Traits_::kPadN, false>;
using Default2DEpilogueProblem = ck_tile::Default2DEpilogueProblem<ComputeDataType, YDataType, false, Traits_::kPadN, true>;
using Default2DEpilogue = ck_tile::Default2DEpilogue<Default2DEpilogueProblem>;
static constexpr bool UseSmoothInputScale = Traits_::kFusedQuant == 1;
using DynamicQuantEpilogueProblem = ck_tile::DynamicQuantEpilogueProblem<ComputeDataType, XScaleDataType, YScaleDataType, YDataType, typename Traits_::Shape,
ck_tile::DynamicQuantEpilogueTraits<false, Traits_::kPadN, UseSmoothInputScale, false, true/*max3*/>>;
static constexpr bool UseRawStore = sizeof(YDataType) == 4;
using DynamicQuantEpilogueProblem = ck_tile::DynamicQuantEpilogueProblem<ComputeDataType, SmoothScaleDataType, YScaleDataType, YDataType, typename Traits_::Shape,
ck_tile::DynamicQuantEpilogueTraits<false, Traits_::kPadN, UseSmoothInputScale, UseRawStore, true/*max3*/>>;
using DynamicQuantEpilogue = ck_tile::DynamicQuantEpilogue<DynamicQuantEpilogueProblem>;
......@@ -233,7 +250,7 @@ float layernorm2d_fwd_(const S& s, A a)
API_BASE = """
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include <ck_tile/core.hpp>
#include "layernorm2d_fwd.hpp"
......@@ -269,12 +286,12 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
INSTANCE_BASE = """
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include "layernorm2d_fwd_api_common.hpp"
// clang-format off
// prec_i prec_o prec_sy rm rn tm tn vn pd mv rpcf 2p add sweep
// prec_i prec_o prec_sy rm rn tm tn vn pd mv rpcf welford 2p xbias add sweep
{F_instance_def}
// clang-format on
......@@ -284,6 +301,10 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
self.working_path = working_path
self.kernel_filter = kernel_filter
class k_xbias_enum(IntEnum):
F_NO_XBIAS = 0
F_ADD_XBIAS = 1
class k_fuesd_add_enum(IntEnum):
F_NO_ADD = 0
F_PRE_ADD = 1
......@@ -299,6 +320,7 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
F_kPadN : bool
F_kSaveMeanInvStd : bool
F_kTwoPass : bool
F_kXbias : Any #: layernorm_fwd_codegen.k_bias_enum
F_kFusedAdd : Any #: layernorm_fwd_codegen.k_fuesd_add_enum
F_kFusedQuant : Any #: layernorm_fwd_codegen.k_fused_sweep_enum
......@@ -315,6 +337,7 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
@dataclass
class k_problem:
F_XDataType : str
F_XBiasDataType : str
F_GammaDataType : str
F_BetaDataType : str
F_ComputeDataType : str
......@@ -352,7 +375,7 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
class h_traits:
F_XDataType : str
F_YDataType : str
F_XScaleDataType : str
F_SmoothScaleDataType : str
F_YScaleDataType : str
F_Repeat_M : int
F_Repeat_N : int
......@@ -362,15 +385,17 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
F_kPadN : bool
F_kSaveMeanInvStd_ : bool
F_kFastFDiv_ : bool
F_kWelford_ : bool
F_kTwoPass_ : bool
F_kXbias_ : int
F_kFusedAdd : int
F_kFusedQuant : int
@property
def trait_name(self) ->str:
t_ = f'{DATA_TYPE_MAP[self.F_XDataType]}, {DATA_TYPE_MAP[self.F_YDataType]}, {DATA_TYPE_MAP[self.F_XScaleDataType]}, {DATA_TYPE_MAP[self.F_YScaleDataType]}, {self.F_Repeat_M:2}, {self.F_Repeat_N:2}, {self.F_ThreadPerBlock_M:2}, {self.F_ThreadPerBlock_N:4}'
t_ += f', {self.F_Vector_N:2}, {BOOL_MAP(self.F_kPadN):5}, {BOOL_MAP(self.F_kSaveMeanInvStd_):5}, {BOOL_MAP(self.F_kFastFDiv_):5}'
t_ += f', {BOOL_MAP(self.F_kTwoPass_):5}, {self.F_kFusedAdd:4}, {self.F_kFusedQuant:4}'
t_ = f'{DATA_TYPE_MAP[self.F_XDataType]}, {DATA_TYPE_MAP[self.F_YDataType]}, {DATA_TYPE_MAP[self.F_SmoothScaleDataType]}, {DATA_TYPE_MAP[self.F_YScaleDataType]}, {self.F_Repeat_M:2}, {self.F_Repeat_N:2}, {self.F_ThreadPerBlock_M:2}, {self.F_ThreadPerBlock_N:4}'
t_ += f', {self.F_Vector_N:2}, {BOOL_MAP(self.F_kPadN):5}, {BOOL_MAP(self.F_kSaveMeanInvStd_):5}, {BOOL_MAP(self.F_kFastFDiv_):5}, {BOOL_MAP(self.F_kWelford_):5}'
t_ += f', {BOOL_MAP(self.F_kTwoPass_):5}, {self.F_kXbias:4}, {self.F_kFusedAdd:4}, {self.F_kFusedQuant:4}'
return t_
# string when calling this kernel
......@@ -388,6 +413,7 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
class h_instance:
F_DataTypePair : str
F_N : str
F_xbias : int
F_add : int
F_sweep : int
instance_list : List[Any] # List[h_traits]
......@@ -397,6 +423,8 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
prec_i, prec_o = self.F_DataTypePair.split(',')
dtype_str = f'{prec_i}' if prec_i == prec_o else f'{prec_i}_{prec_o}'
nnn = f'layernorm2d_fwd_{dtype_str}_n{self.F_N}'
if self.F_xbias != 0:
nnn = nnn + '_' + XBIAS_ENUM_STR_MAP[self.F_xbias]
if self.F_add != 0:
nnn = nnn + '_' + FUSED_ADD_ENUM_STR_MAP[self.F_add]
if self.F_sweep != 0:
......@@ -422,11 +450,10 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
def name_common_header(self) -> str:
return 'layernorm2d_fwd_api_common'
@property
def content_api(self) -> str:
def content_api(self, args) -> str:
# 1 sort based on dtype
t_dtype_dict = dict()
blobs = self.get_blobs()
blobs = self.get_blobs(args)
for blob in blobs:
if blob.F_DataTypePair not in t_dtype_dict:
t_dtype_dict[blob.F_DataTypePair] = {}
......@@ -451,19 +478,19 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
if ins.F_kFusedQuant == 0:
_sweep_cond = 't.fused_quant == {f_fused_sweep}'.format(f_fused_sweep = ins.F_kFusedQuant)
elif ins.F_kFusedQuant == 1:
_sweep_cond = 't.fused_quant == {f_fused_sweep} && (t.prec_sx == \"{f_sx_type}\" && t.prec_sy == \"{f_sy_type}\")'.format(
f_fused_sweep = ins.F_kFusedQuant, f_sx_type=ins.F_XScaleDataType, f_sy_type=ins.F_YScaleDataType)
_sweep_cond = 't.fused_quant == {f_fused_sweep} && (t.prec_sm == \"{f_sx_type}\" && t.prec_sy == \"{f_sy_type}\")'.format(
f_fused_sweep = ins.F_kFusedQuant, f_sx_type=ins.F_SmoothScaleDataType, f_sy_type=ins.F_YScaleDataType)
elif ins.F_kFusedQuant == 2:
_sweep_cond = 't.fused_quant == {f_fused_sweep} && (t.prec_sy == \"{f_sy_type}\")'.format(
f_fused_sweep = ins.F_kFusedQuant, f_sy_type=ins.F_YScaleDataType)
_cond = '((a.n % {f_vec_n} == 0) && (t.fused_add == {f_fused_add}) && ({f_sweep_cond}))'.format(
f_vec_n = ins.F_Vector_N, f_fused_add = ins.F_kFusedAdd,
_cond = '((a.n % {f_vec_n} == 0) && (t.xbias == {f_xbias}) && (t.fused_add == {f_fused_add}) && ({f_sweep_cond}))'.format(
f_vec_n = ins.F_Vector_N, f_xbias = ins.F_kXbias, f_fused_add = ins.F_kFusedAdd,
f_sweep_cond = _sweep_cond)
inner_str += self.API_INNER_CASE.format(F_if = get_if_str(idx_in_n, len_in_n, False),
F_VEC_COND = _cond, F_instance_func=ins.call_name)
#inner_str = inner_str + vec_str
n_cnd = f'(a.n <= {n_})' if (i_n < len(blob_per_t) - 1) else ''
n_str += self.API_PER_N_CASE.format(F_if = get_if_str(i_n, len(blob_per_t)), F_N_COND=n_cnd, F_inner_dispatch=inner_str)
n_cnd = f'(a.n <= {n_})' if isinstance(n_, int) else ''
n_str += self.API_PER_N_CASE.format(F_if = get_if_str(i_n, len(blob_per_t), not isinstance(n_, int)), F_N_COND=n_cnd, F_inner_dispatch=inner_str)
prec_i, prec_o = dtype_.split(',')
d_str += self.API_PER_DTYPE.format(F_if = get_if_str(i_d, len(t_dtype_dict), False), F_i_type=prec_i, F_o_type=prec_o, F_per_n_case=n_str)
......@@ -474,77 +501,80 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
def content_common_header(self) -> str:
return self.API_COMMON_HEADER.format(F_traits_define=self.API_TRAITS_DEFINE)
def get_blobs(self):
def get_blobs(self, args):
h_traits = layernorm_fwd_codegen.h_traits
h_instance = layernorm_fwd_codegen.h_instance
dynamic_quant_out_dtype = ['int8']
dynamic_quant_out_dtype = ['int8', 'fp8']
# some predefined support range
# (prec_i,prec_o) for simplicity this string will be used as key for dict
scale_list = [('fp32,fp32')]
dtype_list = [('fp16,fp16'), ('bf16,bf16'),
('fp16,int8'), ('bf16,int8')] # NOTE: only fused-dynamic-quant use int8 out
('fp16,int8'), ('bf16,int8'),
('fp16,fp8'), ('bf16,fp8')] # NOTE: only fused-dynamic-quant use int8 or fp8 out
types_8bit = ('int8', 'fp8')
types_16bit = ('int16', 'fp16', 'bf16')
#fused_add_list = [0, 1, 2]
#fused_sweep_list = [0, 1, 2] # NOTE: only single pass can use fused dynamic quant
xbias_list = [0, 1]
fused_add_list = [0, 1]
fused_sweep_list = [0, 1] # NOTE: only single pass can use fused dynamic quant
# rm rn tm tn vn pd mv fdiv 2p add sweep
h_trait_dict = {'64' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 8, 8, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 16, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 1, True, False, True, False, 0, 0)],
'128' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 16, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 4, 64, 1, True, False, True, False, 0, 0)],
'256' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 4, 64, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 4, 64, 1, True, False, True, False, 0, 0)],
'512' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 4, 64, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 4, 64, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 4, 64, 1, True, False, True, False, 0, 0)],
'768' : [ h_traits('x', 'y', 'xs', 'ys', 1, 3, 4, 64, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 4, 64, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 12, 4, 64, 1, True, False, True, False, 0, 0)],
'1024' :[ h_traits('x', 'y', 'xs', 'ys', 1, 1, 2, 128, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 2, 128, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 2, 128, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 1, True, False, True, False, 0, 0)],
'1536' :[ h_traits('x', 'y', 'xs', 'ys', 1, 3, 4, 64, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 2, 128, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 256, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 1, 256, 1, True, False, True, False, 0, 0)],
'2048' :[ h_traits('x', 'y', 'xs', 'ys', 1, 1, 1, 256, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 1, 256, 1, True, False, True, False, 0, 0)],
'3072' :[ h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 128, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 256, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 1, 256, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1,1024, 1, True, False, True, False, 0, 0)],
'4096' :[ h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1,1024, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 1, True, False, True, False, 0, 0)],
'6144' :[ h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 256, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 512, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1,1024, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 1,1024, 1, True, False, True, False, 0, 0)],
'8192' :[ h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 8, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 512, 4, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 2, True, False, True, False, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 1,1024, 1, True, False, True, False, 0, 0)],
'big' :[ h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 8, True, False, True, True, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 4, True, False, True, True, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1,1024, 2, True, False, True, True, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 1, True, False, True, True, 0, 0)]}
# rm rn tm tn vn pd mv fdiv welford 2p xbias add sweep
h_trait_dict = {'64' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 8, 8, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 16, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 1, True, False, True, True, False, 0, 0, 0)],
'128' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 16, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 4, 64, 1, True, False, True, True, False, 0, 0, 0)],
'256' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 4, 64, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 4, 64, 1, True, False, True, True, False, 0, 0, 0)],
'512' : [ h_traits('x', 'y', 'xs', 'ys', 1, 1, 4, 64, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 4, 64, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 4, 64, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 4, 64, 1, True, False, True, True, False, 0, 0, 0)],
'768' : [ h_traits('x', 'y', 'xs', 'ys', 1, 3, 4, 64, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 4, 64, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 12, 4, 64, 1, True, False, True, True, False, 0, 0, 0)],
'1024' :[ h_traits('x', 'y', 'xs', 'ys', 1, 1, 2, 128, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 2, 128, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 2, 128, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 1, True, False, True, True, False, 0, 0, 0)],
'1536' :[ h_traits('x', 'y', 'xs', 'ys', 1, 3, 4, 64, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 2, 128, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 256, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 1, 256, 1, True, False, True, True, False, 0, 0, 0)],
'2048' :[ h_traits('x', 'y', 'xs', 'ys', 1, 1, 1, 256, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 1, 256, 1, True, False, True, True, False, 0, 0, 0)],
'3072' :[ h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 128, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 256, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 1, 256, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1,1024, 1, True, False, True, True, False, 0, 0, 0)],
'4096' :[ h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1,1024, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 1, True, False, True, True, False, 0, 0, 0)],
'6144' :[ h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 256, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1, 512, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 3, 1,1024, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 6, 1,1024, 1, True, False, True, True, False, 0, 0, 0)],
'8192' :[ h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 8, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 512, 4, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 2, True, False, True, True, False, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 8, 1,1024, 1, True, False, True, True, False, 0, 0, 0)],
'big' :[ h_traits('x', 'y', 'xs', 'ys', 1, 2, 1, 256, 8, True, False, True, True, True, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1, 256, 4, True, False, True, True, True, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 2, 1,1024, 2, True, False, True, True, True, 0, 0, 0),
h_traits('x', 'y', 'xs', 'ys', 1, 4, 1,1024, 1, True, False, True, True, True, 0, 0, 0)]}
total_blob = list()
for hs_key in h_trait_dict:
hs = h_trait_dict[hs_key]
current_n = hs[0].F_Repeat_N * hs[0].F_ThreadPerBlock_N * hs[0].F_Vector_N
for dtype, scale_type, fused_add, fused_quant in itertools.product(dtype_list, scale_list, fused_add_list, fused_sweep_list):
for dtype, scale_type, xbias, fused_add, fused_quant in itertools.product(dtype_list, scale_list, xbias_list, fused_add_list, fused_sweep_list):
prec_i, prec_o = dtype.split(',')
scale_x, scale_y = scale_type.split(',')
scale_sm, scale_y = scale_type.split(',')
if prec_o in dynamic_quant_out_dtype and fused_quant != 1:
continue # skip non dynamic quant case
if fused_quant == 1 and hs_key == 'big':
......@@ -554,20 +584,32 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
h_ = copy.copy(chs_) # copy the base instance out
h_.F_XDataType = prec_i
h_.F_YDataType = prec_o
h_.F_XScaleDataType = scale_y
h_.F_YScaleDataType = scale_x
h_.F_SmoothScaleDataType = scale_sm
h_.F_YScaleDataType = scale_y
h_.F_kXbias = xbias
h_.F_kFusedAdd = fused_add
h_.F_kFusedQuant = fused_quant
# disable welford update for 8bit and 16 bit smallN
if not h_.F_kTwoPass_:
#disable 16 bit when set args disable_16b_welford
if args.disable_16b_welford and prec_i in types_16bit:
h_.F_kWelford_ = False
#disable 8bit by default
elif prec_i in types_8bit or prec_o in types_8bit:
h_.F_kWelford_ = False
#disable 16bit small N
elif prec_i in types_16bit and hs_key == '64':
h_.F_kWelford_ = False
current_hs.append(h_) # + "\n"
#f.write(str(f.parent / GEN_DIR / (blobs.api_common_header_
current_n_str = 'big' if hs_key == 'big' else current_n
total_blob.append(h_instance(dtype, current_n_str, fused_add, fused_quant, current_hs))
total_blob.append(h_instance(dtype, current_n_str, xbias, fused_add, fused_quant, current_hs))
return total_blob
def list_blobs(self) -> None:
def list_blobs(self, args) -> None:
w_p = Path(self.working_path)
list_p = w_p / 'layernorm2d_fwd_blobs.txt'
blobs = self.get_blobs()
blobs = self.get_blobs(args)
with list_p.open('w') as list_f:
# api related file
list_f.write(str(w_p / (self.name_api + ".cpp")) + "\n")
......@@ -576,11 +618,12 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
for b in blobs:
list_f.write(str(w_p / (b.name + ".cpp")) + "\n")
def gen_blobs(self) -> None:
def gen_blobs(self, args) -> None:
w_p = Path(self.working_path)
(w_p / (self.name_api + ".cpp")).write_text(self.content_api)
w_str = self.content_api(args)
(w_p / (self.name_api + ".cpp")).write_text(w_str)
(w_p / (self.name_common_header + ".hpp")).write_text(self.content_common_header)
blobs = self.get_blobs()
blobs = self.get_blobs(args)
for b in blobs:
(w_p / (b.name + ".cpp")).write_text(b.content)
......@@ -588,14 +631,14 @@ def list_blobs(args):
api_list = args.api.split(',')
for api in api_list:
if api == 'fwd':
layernorm_fwd_codegen(args.working_path, args.filter).list_blobs()
layernorm_fwd_codegen(args.working_path, args.filter).list_blobs(args)
def gen_blobs(args):
api_list = args.api.split(',')
for api in api_list:
if api == 'fwd':
layernorm_fwd_codegen(args.working_path, args.filter).gen_blobs()
layernorm_fwd_codegen(args.working_path, args.filter).gen_blobs(args)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
......@@ -663,6 +706,13 @@ if __name__ == "__main__":
help="codegen receipt."
)
parser.add_argument(
"--disable_16b_welford",
default=False,
required=False,
help="enable/disable welford for 16bit datatype n > 64"
)
args = parser.parse_args()
# print(f'{args.list_blobs}-{args.gen_blobs}')
......
example/ck_tile/02_layernorm2d/layernorm2d_fwd.cpp
View file @ 3c5717df
......@@ -20,6 +20,14 @@ auto get_elimit<ck_tile::bf16_t>()
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<ck_tile::int8_t>()
{
double rtol = 1e-2;
double atol = 1.0;
return ck_tile::make_tuple(rtol, atol);
}
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
......@@ -35,12 +43,13 @@ auto create_args(int argc, char* argv[])
.insert("kname", "1", "print kernel name or not")
.insert("prec_i", "fp16", "input precision")
.insert("prec_o", "auto", "output precision, set auto will be the same as input")
.insert("prec_sx",
.insert("prec_sm",
"auto",
"output quant scale type, set auto will use fp32. used when fquant=1")
.insert("prec_sy",
"auto",
"output quant scale type, set auto will use fp32. used when fquant=1 or 2")
.insert("xbias", "0", "add bias, 0:no add, 1:add bias before fadd")
.insert("fadd", "0", "fused-add, 0:no fused add, 1:preadd+store, 2:preadd only")
.insert("fquant", "0", "fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant")
.insert("warmup", "5", "cold iter")
......@@ -52,7 +61,7 @@ auto create_args(int argc, char* argv[])
template <typename InDataType,
typename OutDataType,
typename XScaleDataType,
typename SmoothScaleDataType,
typename YScaleDataType,
bool SaveMeanVar>
bool run(const ck_tile::ArgParser& arg_parser)
......@@ -74,15 +83,15 @@ bool run(const ck_tile::ArgParser& arg_parser)
float epsilon = arg_parser.get_float("e");
std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_sx = arg_parser.get_str("prec_sx");
std::string prec_sm = arg_parser.get_str("prec_sm");
std::string prec_sy = arg_parser.get_str("prec_sy");
if(prec_o == "auto")
{
prec_o = prec_i;
}
if(prec_sx == "auto")
if(prec_sm == "auto")
{
prec_sx = "fp32";
prec_sm = "fp32";
}
if(prec_sy == "auto")
{
......@@ -93,20 +102,25 @@ bool run(const ck_tile::ArgParser& arg_parser)
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat");
int xbias = arg_parser.get_int("xbias");
int fused_add = arg_parser.get_int("fadd");
int fused_quant = arg_parser.get_int("fquant");
if(fused_quant == 1 && prec_o != "int8")
if(fused_quant == 1 && prec_o != "int8" && prec_o != "fp8")
{
std::cout << "if fused_quant is 1, only support \"-prec_o=int8\" case" << std::endl;
std::cout
<< "if fused_quant is 1 or 2, only support \"-prec_o=int8\" or \"-prec_o=fp8\" cases."
<< std::endl;
return false;
}
assert(x_stride >= n);
using TypeConfig = LayerNormTypeConfig<InDataType, OutDataType, XScaleDataType, YScaleDataType>;
using TypeConfig =
LayerNormTypeConfig<InDataType, OutDataType, SmoothScaleDataType, YScaleDataType>;
using XDataType = typename TypeConfig::XDataType;
using YDataType = typename TypeConfig::YDataType;
using XBiasDataType = typename TypeConfig::XBiasDataType;
using GammaDataType = typename TypeConfig::GammaDataType;
using BetaDataType = typename TypeConfig::BetaDataType;
using XResidualDataType = XDataType;
......@@ -121,6 +135,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
// host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
ck_tile::HostTensor<XBiasDataType> x_bias_host({n});
ck_tile::HostTensor<GammaDataType> gamma_host({n});
ck_tile::HostTensor<BetaDataType> beta_host({n});
......@@ -135,30 +150,33 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::HostTensor<YScaleDataType> y_scale_host_ref({m});
ck_tile::HostTensor<YScaleDataType> y_scale_host_dev({m});
ck_tile::HostTensor<XScaleDataType> x_scale_host({n});
ck_tile::HostTensor<XScaleDataType> x_scale_host_dev({n});
ck_tile::HostTensor<SmoothScaleDataType> sm_scale_host({n});
ck_tile::HostTensor<SmoothScaleDataType> sm_scale_host_dev({n});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<XResidualDataType>{-.5f, .5f}(x_residual_host);
ck_tile::FillUniformDistribution<XScaleDataType>{-1.f, 1.f}(x_scale_host);
ck_tile::FillUniformDistribution<SmoothScaleDataType>{-1.f, 1.f}(sm_scale_host);
ck_tile::FillUniformDistribution<XBiasDataType>{-.5f, .5f}(x_bias_host);
ck_tile::FillUniformDistribution<GammaDataType>{-.5f, .5f}(gamma_host);
ck_tile::FillUniformDistribution<BetaDataType>{-.5f, .5f}(beta_host);
ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem x_bias_buf(x_bias_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem gamma_buf(gamma_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem beta_buf(beta_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_buf(y_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_scale_buf(y_scale_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem x_scale_buf(x_scale_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem sm_scale_buf(sm_scale_host_dev.get_element_space_size_in_bytes());
ck_tile::DeviceMem x_residual_buf(x_residual_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem y_residual_buf(y_residual_host.get_element_space_size_in_bytes());
x_buf.ToDevice(x_host.data());
x_bias_buf.ToDevice(x_bias_host.data());
gamma_buf.ToDevice(gamma_host.data());
beta_buf.ToDevice(beta_host.data());
x_residual_buf.ToDevice(x_residual_host.data());
x_scale_buf.ToDevice(x_scale_host.data());
sm_scale_buf.ToDevice(sm_scale_host.data());
auto prec_str = [&]() {
auto base_str = prec_i;
......@@ -179,11 +197,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< ", yr_stride:" << yr_stride << std::flush;
layernorm2d_fwd_traits traits{
prec_i, prec_o, prec_sx, prec_sy, SaveMeanVar, fused_add, fused_quant};
prec_i, prec_o, prec_sm, prec_sy, SaveMeanVar, xbias, fused_add, fused_quant};
layernorm2d_fwd_args args{x_buf.GetDeviceBuffer(),
fused_add != 0 ? x_residual_buf.GetDeviceBuffer() : nullptr,
fused_quant == 1 ? x_scale_buf.GetDeviceBuffer() : nullptr,
fused_quant == 1 ? sm_scale_buf.GetDeviceBuffer() : nullptr,
x_bias_buf.GetDeviceBuffer(),
gamma_buf.GetDeviceBuffer(),
beta_buf.GetDeviceBuffer(),
......@@ -210,8 +229,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
return false;
}
std::size_t num_byte = sizeof(XDataType) * m * n + sizeof(GammaDataType) * n +
sizeof(BetaDataType) * n + sizeof(YDataType) * m * n;
std::size_t num_byte = sizeof(XDataType) * m * n + sizeof(XBiasDataType) * n +
sizeof(GammaDataType) * n + sizeof(BetaDataType) * n +
sizeof(YDataType) * m * n;
float gb_per_sec = num_byte / 1.E6 / ave_time;
std::cout << ", " << ave_time * 1.E3 << " us, " << gb_per_sec << " GB/s" << std::flush;
......@@ -221,6 +241,22 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation)
{
// reference
if(xbias != 0)
{
// add bias before fadd
int M = x_host.mDesc.get_lengths()[0];
int N = x_host.mDesc.get_lengths()[1];
for(int idx_m = 0; idx_m < M; ++idx_m)
{
for(int idx_n = 0; idx_n < N; ++idx_n)
{
x_host(idx_m, idx_n) = ck_tile::type_convert<XDataType>(
ck_tile::type_convert<ComputeDataType>(x_host(idx_m, idx_n)) +
ck_tile::type_convert<ComputeDataType>(x_bias_host(idx_n)));
}
}
}
if(fused_add != 0)
{
// fused pre_add/pre_add_store
......@@ -254,8 +290,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
for(int n_ = 0; n_ < N_; n_++)
{
// input smooth outlier
acc_(m_, n_) =
acc_(m_, n_) * ck_tile::type_convert<ComputeDataType>(x_scale_host(n_));
acc_(m_, n_) = acc_(m_, n_) *
ck_tile::type_convert<ComputeDataType>(sm_scale_host(n_));
}
}
ComputeDataType absmax = static_cast<ComputeDataType>(0);
......@@ -265,7 +301,11 @@ bool run(const ck_tile::ArgParser& arg_parser)
absmax = a > absmax ? a : absmax;
}
// printf("cpu:absmax:%f\n", absmax);
ComputeDataType y_scale = absmax / static_cast<ComputeDataType>(127.0);
constexpr ComputeDataType kMaxY =
std::is_same<YDataType, ck_tile::fp8_t>::value ? 240.0
: std::is_same<YDataType, ck_tile::int8_t>::value ? 127.0
: 0.0;
ComputeDataType y_scale = absmax / kMaxY;
y_scale_host_ref(m_) = ck_tile::type_convert<YScaleDataType>(y_scale);
for(int n_ = 0; n_ < N_; n_++)
{
......@@ -308,7 +348,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
y_residual_buf.FromDevice(y_residual_host_dev.data());
}
auto [rtol, atol] = get_elimit<InDataType>();
auto [rtol, atol] = get_elimit<OutDataType>();
if(x_stride == n)
{
......@@ -377,16 +417,16 @@ int main(int argc, char* argv[])
std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_sx = arg_parser.get_str("prec_sx");
std::string prec_sm = arg_parser.get_str("prec_sm");
std::string prec_sy = arg_parser.get_str("prec_sy");
if(prec_o == "auto")
{
prec_o = prec_i;
}
if(prec_sx == "auto")
if(prec_sm == "auto")
{
prec_sx = "fp32";
prec_sm = "fp32";
}
if(prec_sy == "auto")
{
......@@ -395,37 +435,47 @@ int main(int argc, char* argv[])
int save_mv = arg_parser.get_int("save_mv");
// no dynamic quant case
if(prec_i == "fp16" && prec_o == "fp16" && prec_sx == "fp32" && prec_sy == "fp32" && save_mv)
if(prec_i == "fp16" && prec_o == "fp16" && prec_sm == "fp32" && prec_sy == "fp32" && save_mv)
{
return run<ck_tile::half_t, ck_tile::half_t, float, float, true>(arg_parser) ? 0 : -2;
}
else if(prec_i == "fp16" && prec_o == "fp16" && prec_sx == "fp32" && prec_sy == "fp32" &&
else if(prec_i == "fp16" && prec_o == "fp16" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv)
{
return run<ck_tile::half_t, ck_tile::half_t, float, float, false>(arg_parser) ? 0 : -2;
}
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sx == "fp32" && prec_sy == "fp32" &&
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sm == "fp32" && prec_sy == "fp32" &&
save_mv)
{
return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, true>(arg_parser) ? 0 : -2;
}
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sx == "fp32" && prec_sy == "fp32" &&
else if(prec_i == "bf16" && prec_o == "bf16" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv)
{
return run<ck_tile::bf16_t, ck_tile::bf16_t, float, float, true>(arg_parser) ? 0 : -2;
}
// dynamic quant case, only in inference
else if(prec_i == "fp16" && prec_o == "int8" && prec_sx == "fp32" && prec_sy == "fp32" &&
else if(prec_i == "fp16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv)
{
return run<ck_tile::half_t, ck_tile::int8_t, float, float, false>(arg_parser) ? 0 : -2;
}
else if(prec_i == "bf16" && prec_o == "int8" && prec_sx == "fp32" && prec_sy == "fp32" &&
else if(prec_i == "bf16" && prec_o == "int8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv)
{
return run<ck_tile::bf16_t, ck_tile::int8_t, float, float, false>(arg_parser) ? 0 : -2;
}
else if(prec_i == "fp16" && prec_o == "fp8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv)
{
return run<ck_tile::half_t, ck_tile::fp8_t, float, float, false>(arg_parser) ? 0 : -2;
}
else if(prec_i == "bf16" && prec_o == "fp8" && prec_sm == "fp32" && prec_sy == "fp32" &&
!save_mv)
{
return run<ck_tile::bf16_t, ck_tile::fp8_t, float, float, false>(arg_parser) ? 0 : -2;
}
return -3;
}
example/ck_tile/02_layernorm2d/layernorm2d_fwd.hpp
View file @ 3c5717df
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -8,35 +8,40 @@
#include "ck_tile/ops/layernorm2d.hpp"
#include <string>
template <typename InType, typename OutType, typename XScaleDataType_, typename YScaleDataType_>
template <typename InType,
typename OutType,
typename SmoothSScaleDataType_,
typename YScaleDataType_>
struct LayerNormTypeConfig;
template <typename OutType, typename XScaleDataType_, typename YScaleDataType_>
struct LayerNormTypeConfig<ck_tile::half_t, OutType, XScaleDataType_, YScaleDataType_>
template <typename OutType, typename SmoothScaleDataType_, typename YScaleDataType_>
struct LayerNormTypeConfig<ck_tile::half_t, OutType, SmoothScaleDataType_, YScaleDataType_>
{
using XDataType = ck_tile::half_t;
using YDataType = OutType;
using GammaDataType = ck_tile::half_t;
using BetaDataType = ck_tile::half_t;
using MeanDataType = ck_tile::half_t;
using InvStdDataType = ck_tile::half_t;
using ComputeDataType = float;
using XScaleDataType = XScaleDataType_;
using YScaleDataType = YScaleDataType_;
using XDataType = ck_tile::half_t;
using YDataType = OutType;
using XBiasDataType = ck_tile::half_t;
using GammaDataType = ck_tile::half_t;
using BetaDataType = ck_tile::half_t;
using MeanDataType = ck_tile::half_t;
using InvStdDataType = ck_tile::half_t;
using ComputeDataType = float;
using SmoothScaleDataType = SmoothScaleDataType_;
using YScaleDataType = YScaleDataType_;
};
template <typename OutType, typename XScaleDataType_, typename YScaleDataType_>
struct LayerNormTypeConfig<ck_tile::bf16_t, OutType, XScaleDataType_, YScaleDataType_>
template <typename OutType, typename SmoothScaleDataType_, typename YScaleDataType_>
struct LayerNormTypeConfig<ck_tile::bf16_t, OutType, SmoothScaleDataType_, YScaleDataType_>
{
using XDataType = ck_tile::bf16_t;
using YDataType = OutType;
using GammaDataType = ck_tile::bf16_t;
using BetaDataType = ck_tile::bf16_t;
using MeanDataType = ck_tile::bf16_t;
using InvStdDataType = ck_tile::bf16_t;
using ComputeDataType = float;
using XScaleDataType = XScaleDataType_;
using YScaleDataType = YScaleDataType_;
using XDataType = ck_tile::bf16_t;
using YDataType = OutType;
using XBiasDataType = ck_tile::bf16_t;
using GammaDataType = ck_tile::bf16_t;
using BetaDataType = ck_tile::bf16_t;
using MeanDataType = ck_tile::bf16_t;
using InvStdDataType = ck_tile::bf16_t;
using ComputeDataType = float;
using SmoothScaleDataType = SmoothScaleDataType_;
using YScaleDataType = YScaleDataType_;
};
// runtime args
......@@ -50,13 +55,14 @@ struct layernorm2d_fwd_traits
std::string prec_i; // input precision
std::string prec_o; // output precision
// if fused_quant == 1, need set prec_sx/prec_sy to proper string, otherwise can set
// if fused_quant == 1, need set prec_sm/prec_sy to proper string, otherwise can set
// arbitrary(will skip check) if fused_quant == 2, need set prec_sy to proper string, otherwise
// can set arbitrary(will skip check)
std::string prec_sx; // x-scale, used for [1*N] input smooth quant
std::string prec_sm; // x-scale, used for [1*N] input smooth quant
std::string prec_sy; // y-scale, used for [M*1] output for next layer
bool save_mean_var; //
int xbias; // 0:no-bias, 1:add bias
int fused_add; // 0:no-add, 1:pre-add-store, 2:pre-add
int fused_quant; // 0:no-sweep, 1:smooth-dynamic-quant, 2:dynamic-quant
};
......
example/ck_tile/02_layernorm2d/script/smoke_test.sh
View file @ 3c5717df
#!/bin/sh
EXE="$(find . -name tile_example_layernorm2d_fwd -type f | head -n 1)"
for fquant in "" "-fquant=1 -prec_o=int8"; do
for fquant in "" "-fquant=1 -prec_o=int8" "-fquant=1 -prec_o=fp8"; do
for pr_i in "fp16" "bf16" ; do
for fadd in "0" "1"; do
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=99 -n=13
......@@ -27,7 +27,8 @@ $EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=7 -n=2734
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=1 -n=3182
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=9 -n=4096
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=8192
#$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=1 -n=10547
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=9120
$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=1 -n=10547
#$EXE -prec_i=$pr_i -fadd=$fadd $fquant -m=3 -n=17134
done
done
......
example/ck_tile/03_gemm/CMakeLists.txt
View file @ 3c5717df
add_executable(tile_example_gemm_basic EXCLUDE_FROM_ALL gemm_basic.cpp)
add_executable(tile_example_gemm_mem_pipeline EXCLUDE_FROM_ALL gemm_mem_pipeline.cpp)
add_executable(tile_example_gemm_universal EXCLUDE_FROM_ALL universal_gemm.cpp)
example/ck_tile/03_gemm/README.md
View file @ 3c5717df
......@@ -11,9 +11,9 @@ sh ../script/cmake-ck-dev.sh ../ <arch>
# The basic pipeline method on the gemm calculation
make tile_example_gemm_basic -j
# The memory bound pipeline on the gemm calculation
make tile_example_gemm_mem_pipeline -j
make tile_example_gemm_universal -j
```
This will result in an executable `build/bin/tile_example_gemm_basic`
This will result in an executable `build/bin/tile_example_gemm_basic` & `build/bin/tile_example_gemm_universal`
## example
```
......@@ -22,6 +22,9 @@ args:
-m m dimension (default:1024)
-n n dimension (default:2048)
-k k dimension (default:64)
-a_layout Tensor A data layout (default: R)
-b_layout Tensor B data layout (default: R)
-c_layout Tensor C data layout (default: R)
-stride_a Tensor A stride (default:0)
-stride_b Tensor B stride (default:0)
-stride_c Tensor C stride (default:0)
......
example/ck_tile/03_gemm/gemm_basic.cpp
View file @ 3c5717df
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#include <hip/hip_runtime.h>
......@@ -9,29 +9,29 @@
#include <string>
#include <tuple>
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/host.hpp"
#include "gemm_basic.hpp"
template <typename ALayout, typename BLayout, typename CLayout>
float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CDataType,
typename ALayout,
typename BLayout,
typename CLayout>
float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
{
// The kPadM, kPadN, kPadK & kBlockPerCu should also come from the Codegen part.
constexpr bool kPadM = false;
constexpr bool kPadN = false;
constexpr bool kPadK = false;
constexpr bool kTilePermute = false;
// The rank and permutation will also be generate out by the CodeGen part.
constexpr ck_tile::index_t kOutputRank = 2;
constexpr int kBlockPerCu = 1;
// This part comes from the Codegen
constexpr ck_tile::index_t M_Tile = 128;
constexpr ck_tile::index_t N_Tile = 128;
constexpr ck_tile::index_t K_Tile = 32;
constexpr ck_tile::index_t K_Tile = 64;
constexpr ck_tile::index_t M_Warp = 2;
constexpr ck_tile::index_t N_Warp = 2;
......@@ -39,59 +39,47 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
constexpr ck_tile::index_t M_Warp_Tile = 32;
constexpr ck_tile::index_t N_Warp_Tile = 32;
constexpr ck_tile::index_t K_Warp_Tile = 8;
// Whether doing the CShuffle (transpose before the global memory), depending on the output
// layout.
constexpr bool CShuffleEpilogue =
std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::ColumnMajor>;
constexpr ck_tile::index_t K_Warp_Tile = 16;
using CodegenGemmShape =
ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTilePartitioner<CodegenGemmShape>;
using GemmEpilogue = std::conditional_t<
CShuffleEpilogue,
ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<AccDataType,
CDataType,
kPadM,
kPadN,
kTilePermute,
kOutputRank,
1,
0,
TilePartitioner::kM,
TilePartitioner::kN>>,
ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>>;
using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
using CodegenGemmTraits =
ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using CodegenPipelineProblem = ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem, CodegenGemmPolicy>;
using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<AccDataType,
CDataType,
CLayout,
CodegenPipelineProblem::kBlockSize,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock,
M_Warp,
N_Warp,
M_Warp_Tile,
N_Warp_Tile,
K_Warp_Tile,
CodegenPipelineProblem::TransposeC>>;
// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
using Kernel = ck_tile::GemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args.p_a,
args.p_b,
args.p_c,
args.M,
args.N,
args.K,
args.stride_A,
args.stride_B,
args.stride_C);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.kbatch);
auto kargs = Kernel::MakeKernelArgs(args);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
constexpr dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
}
if(s.log_level_ > 0)
{
std::cout << "Launching kernel with args:"
......@@ -108,4 +96,46 @@ float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
#include "run_gemm_example.inc"
int run_gemm_example(int argc, char* argv[])
{
auto [result, arg_parser] = create_args(argc, argv);
if(!result)
return -1;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
std::string data_type = arg_parser.get_str("prec");
std::string a_layout = arg_parser.get_str("a_layout");
std::string b_layout = arg_parser.get_str("b_layout");
if(a_layout == "R" && b_layout == "C")
{
if(data_type == "fp16")
{
return run_gemm_example_with_layouts<ck_tile::half_t>(argc, argv, Row{}, Col{}, Row{});
}
else if(data_type == "bf16")
{
return run_gemm_example_with_layouts<ck_tile::bf16_t>(argc, argv, Row{}, Col{}, Row{});
}
else if(data_type == "fp8")
{
return run_gemm_example_with_layouts<ck_tile::fp8_t>(argc, argv, Row{}, Col{}, Row{});
}
else if(data_type == "bf8")
{
return run_gemm_example_with_layouts<ck_tile::bf8_t>(argc, argv, Row{}, Col{}, Row{});
}
else
{
throw std::runtime_error("Unsupported data_type!");
}
}
else
{
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
}
}
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
example/ck_tile/03_gemm/gemm_basic.hpp
View file @ 3c5717df
......@@ -8,6 +8,27 @@
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#define CK_TILE_PIPELINE_COMPUTE 1
#define CK_TILE_PIPELINE_MEMORY 2
#ifndef CK_TILE_PIPELINE_DEFAULT
#define CK_TILE_PIPELINE_DEFAULT CK_TILE_PIPELINE_COMPUTE
#endif
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE)
#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrMem
#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrMem
#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Interwave
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE)
#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrCompV3
#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrCompV3
#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Intrawave
#else
#error "unsupported CK_TILE_PIPELINE_DEFAULT value"
#endif
template <typename DataType>
struct GemmBasicTypeConfig;
......@@ -22,6 +43,33 @@ struct GemmBasicTypeConfig<ck_tile::half_t>
// ToDo: Add more bias config to support different categories of GEMM.
};
template <>
struct GemmBasicTypeConfig<ck_tile::bf16_t>
{
using ADataType = ck_tile::bf16_t;
using BDataType = ck_tile::bf16_t;
using AccDataType = float;
using CDataType = ck_tile::bf16_t;
};
template <>
struct GemmBasicTypeConfig<ck_tile::fp8_t>
{
using ADataType = ck_tile::fp8_t;
using BDataType = ck_tile::fp8_t;
using AccDataType = float;
using CDataType = ck_tile::half_t;
};
template <>
struct GemmBasicTypeConfig<ck_tile::bf8_t>
{
using ADataType = ck_tile::bf8_t;
using BDataType = ck_tile::bf8_t;
using AccDataType = float;
using CDataType = ck_tile::half_t;
};
template <typename T>
struct DataTypeTraits;
......@@ -43,37 +91,32 @@ struct DataTypeTraits<ck_tile::half_t>
static constexpr const char* name = "fp16";
};
using Types = GemmBasicTypeConfig<ck_tile::half_t>;
template <>
struct DataTypeTraits<ck_tile::bf16_t>
{
static constexpr const char* name = "bf16";
};
// Specific type aliases for easy access
using ADataType = Types::ADataType;
using BDataType = Types::BDataType;
using AccDataType = Types::AccDataType;
using CDataType = Types::CDataType;
template <>
struct DataTypeTraits<ck_tile::fp8_t>
{
static constexpr const char* name = "fp8";
};
struct gemm_basic_args
template <>
struct DataTypeTraits<ck_tile::bf8_t>
{
const void* p_a;
const void* p_b;
void* p_c;
ck_tile::index_t kbatch;
ck_tile::index_t M;
ck_tile::index_t N;
ck_tile::index_t K;
ck_tile::index_t stride_A;
ck_tile::index_t stride_B;
ck_tile::index_t stride_C;
static constexpr const char* name = "bf8";
};
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("b", "1", "batch size")
.insert("m", "3840", "m dimension")
arg_parser.insert("m", "3840", "m dimension")
.insert("n", "4096", "n dimension")
.insert("k", "2048", "k dimension")
.insert("a_layout", "R", "A tensor data layout - Row by default")
.insert("b_layout", "R", "B tensor data layout - Row by default")
.insert("b_layout", "C", "B tensor data layout - Column by default")
.insert("c_layout", "R", "C tensor data layout - Row by default")
.insert("stride_a", "0", "Tensor A stride")
.insert("stride_b", "0", "Tensor B stride")
......@@ -82,11 +125,12 @@ auto create_args(int argc, char* argv[])
.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
.insert("warmup", "50", "number of iterations before benchmark the kernel")
.insert("repeat", "100", "number of iterations to benchmark the kernel")
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer");
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
.insert("split_k", "1", "splitK value");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
// host API
float gemm_calc(gemm_basic_args args, const ck_tile::stream_config& s);
float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s);
example/ck_tile/03_gemm/run_gemm_example.inc
View file @ 3c5717df
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
template <typename ALayout, typename BLayout, typename CLayout>
template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
ck_tile::tensor_layout::gemm::RowMajor>>{};
}
template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
{
using ComputeType =
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
// Calculate thresholds
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(K, kbatch));
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
// Calculate error due to split_k accumulation
const auto rtol_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
max_accumulated_value, kbatch);
// Use higher threshold
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}
template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType,
typename ALayout, typename BLayout, typename CLayout>
float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
ck_tile::DeviceMem& b_k_n_dev_buf,
ck_tile::DeviceMem& c_m_n_dev_buf,
......@@ -16,11 +45,11 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
int n_warmup,
int n_repeat)
{
gemm_basic_args args;
args.p_a = a_m_k_dev_buf.GetDeviceBuffer();
args.p_b = b_k_n_dev_buf.GetDeviceBuffer();
args.p_c = c_m_n_dev_buf.GetDeviceBuffer();
args.kbatch = kbatch;
ck_tile::GemmHostArgs args;
args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
args.k_batch = kbatch;
args.M = M;
args.N = N;
args.K = K;
......@@ -28,26 +57,31 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
args.stride_B = stride_B;
args.stride_C = stride_C;
float ave_time = gemm_calc<ALayout, BLayout, CLayout>(
float ave_time = gemm_calc<ADataType, BDataType, AccDataType, CDataType,
ALayout, BLayout, CLayout>(
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
std::string op_name{"Gemm{MemBoundPipeline}"};
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_byte =
sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_byte / 1.E6 / ave_time;
std::cout << "Run " << op_name << "kernel with M =" << M << " N =" << N << " K =" << K
std::cout << "Run Gemm kernel with M =" << M << " N =" << N << " K =" << K
<< " StrideA =" << stride_A << " StrideB =" << stride_B << " StrideC =" << stride_C
<< " A_Layout =" << ALayout::name
<< " B_Layout =" << BLayout::name
<< " C_Layout =" << CLayout::name
<< " A Type = " << DataTypeTraits<ADataType>::name
<< " B Type = " << DataTypeTraits<BDataType>::name
<< " C Type = " << DataTypeTraits<CDataType>::name
<< " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< std::endl;
return ave_time;
}
template <typename ALayout, typename BLayout, typename CLayout>
template <typename PrecType, typename ALayout, typename BLayout, typename CLayout>
int run_gemm_example_with_layouts(int argc,
char* argv[],
const ALayout a_layout = ALayout{},
......@@ -58,6 +92,11 @@ int run_gemm_example_with_layouts(int argc,
if(!result)
return -1;
using ADataType = typename GemmBasicTypeConfig<PrecType>::ADataType;
using BDataType = typename GemmBasicTypeConfig<PrecType>::BDataType;
using CDataType = typename GemmBasicTypeConfig<PrecType>::CDataType;
using AccDataType = typename GemmBasicTypeConfig<PrecType>::AccDataType;
ck_tile::index_t M = arg_parser.get_int("m");
ck_tile::index_t N = arg_parser.get_int("n");
ck_tile::index_t K = arg_parser.get_int("k");
......@@ -66,55 +105,22 @@ int run_gemm_example_with_layouts(int argc,
ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
ck_tile::index_t batch_size = arg_parser.get_int("b");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
using namespace ck_tile::literals;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
auto f_get_default_stride = [](std::size_t row,
std::size_t col,
std::size_t stride,
auto layout) {
if(stride == 0)
{
// give a chance if stride is zero, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
};
stride_A = f_get_default_stride(M, K, stride_A, a_layout);
stride_B = f_get_default_stride(K, N, stride_B, b_layout);
stride_C = f_get_default_stride(M, N, stride_C, CLayout{});
ck_tile::HostTensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, a_layout));
ck_tile::HostTensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, b_layout));
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
ck_tile::HostTensor<ADataType> a_m_k(
ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
ck_tile::HostTensor<BDataType> b_k_n(
ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
ck_tile::HostTensor<CDataType> c_m_n_dev_result(
f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
// TODO: add different init types
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n);
......@@ -127,7 +133,8 @@ int run_gemm_example_with_layouts(int argc,
c_m_n_dev_buf.SetZero();
c_m_n_dev_result.SetZero();
invoke_gemm<ALayout, BLayout, CLayout>(a_m_k_dev_buf,
invoke_gemm<ADataType, BDataType, AccDataType, CDataType,
ALayout, BLayout, CLayout>(a_m_k_dev_buf,
b_k_n_dev_buf,
c_m_n_dev_buf,
M,
......@@ -136,7 +143,7 @@ int run_gemm_example_with_layouts(int argc,
stride_A,
stride_B,
stride_C,
batch_size,
kbatch,
n_warmup,
n_repeat);
......@@ -146,72 +153,84 @@ int run_gemm_example_with_layouts(int argc,
if(arg_parser.get_int("v") == 1)
{
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
c_m_n_host_ref.SetZero();
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
a_m_k, b_k_n, c_m_n_host_ref);
pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_host_ref);
const float max_accumulated_value =
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>
(K, kbatch, max_accumulated_value);
pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_host_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
}
else if(arg_parser.get_int("v") == 2)
{
ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(
f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_gpu_ref.get_element_space_size_in_bytes());
c_m_n_gpu_ref.SetZero();
c_m_n_gpu_buf_ref.SetZero();
ADataType* d_A;
BDataType* d_B;
CDataType* d_C;
ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));
ck_tile::hip_check_error(hipMemcpy(d_A,
a_m_k_dev_buf.GetDeviceBuffer(),
M * K * sizeof(ADataType),
hipMemcpyHostToDevice));
ck_tile::hip_check_error(hipMemcpy(d_B,
b_k_n_dev_buf.GetDeviceBuffer(),
N * K * sizeof(BDataType),
hipMemcpyHostToDevice));
ck_tile::reference_gemm_gpu<ADataType,
BDataType,
AccDataType,
CDataType,
ALayout,
BLayout,
CLayout>(
a_m_k_dev_buf, b_k_n_dev_buf, c_m_n_gpu_buf_ref, M, N, K, stride_A, stride_B, stride_C);
CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_gpu_ref);
ck_tile::hip_check_error(hipMemcpy(c_m_n_gpu_buf_ref.GetDeviceBuffer(),
d_C,
M * N * sizeof(CDataType),
hipMemcpyDeviceToHost));
ck_tile::hip_check_error(hipFree(d_A));
ck_tile::hip_check_error(hipFree(d_B));
ck_tile::hip_check_error(hipFree(d_C));
c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
const float max_accumulated_value =
*std::max_element(c_m_n_gpu_ref.mData.begin(), c_m_n_gpu_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>
(K, kbatch, max_accumulated_value);
pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_gpu_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The GPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
}
return pass;
}
int run_gemm_example(int argc, char* argv[])
{
auto [result, arg_parser] = create_args(argc, argv);
if(!result)
return -1;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
std::string a_layout = arg_parser.get_str("a_layout");
std::string b_layout = arg_parser.get_str("b_layout");
if(a_layout == "R" && b_layout == "R")
{
return run_gemm_example_with_layouts(argc, argv, Row{}, Row{}, Row{});
}
else if(a_layout == "R" && b_layout == "C")
{
return run_gemm_example_with_layouts(argc, argv, Row{}, Col{}, Row{});
}
else if(a_layout == "C" && b_layout == "C")
{
return run_gemm_example_with_layouts(argc, argv, Col{}, Col{}, Row{});
}
else if(a_layout == "C" && b_layout == "R")
{
return run_gemm_example_with_layouts(argc, argv, Col{}, Row{}, Row{});
}
else
{
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
}
}
example/ck_tile/03_gemm/script/benchmark_basic.sh 0 → 100755
View file @ 3c5717df
#!/bin/sh
EXE="$(find . -name tile_example_gemm_basic -type f | head -n 1)"
VALID=1
for b_matrix_layout in "C"; do
for m in "64" "512" "1024" "2048"; do
for n in "512" "1024" "2048"; do
for k in "64" "512" "1024" "2048"; do
$EXE -prec=fp16 -m=$m -n=$n -k=$k -a_layout="R" -b_layout="$b_matrix_layout" -c_layout="R" -v=$VALID
done
done
done
done
example/ck_tile/03_gemm/script/benchmark_basic_fp8.sh 0 → 100644
View file @ 3c5717df
#!/bin/sh
EXE="$(find . -name tile_example_gemm_basic -type f | head -n 1)"
VALID=1
for b_matrix_layout in "C"; do
for m in "64" "512" "1024" "2048"; do
for n in "512" "1024" "2048"; do
for k in "64" "512" "1024" "2048"; do
$EXE -prec=fp8 -m=$m -n=$n -k=$k -a_layout="R" -b_layout="$b_matrix_layout" -c_layout="R" -v=$VALID
done
done
done
done
\ No newline at end of file
example/ck_tile/03_gemm/script/benchmark_mem_pipeline.sh 0 → 100755
View file @ 3c5717df
#!/bin/sh
EXE="$(find . -name tile_example_gemm_universal -type f | head -n 1)"
VALID=1
for b_matrix_layout in "C"; do
for m in "512" "1024" "2048" "4096"; do
for n in "512" "1024" "2048"; do
for k in "512" "1024" "2048"; do
$EXE -prec=fp16 -m=$m -n=$n -k=$k -a_layout="R" -b_layout="$b_matrix_layout" -c_layout="R" -v=$VALID
done
done
done
done
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