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composable_kernel/example/ck_tile/01_fmha/fmha_fwd.cpp
carlushuang fcba889ef4 [CK_TILE] fix some rand number init (#1287) 
* add random norm

* normalized default to 0/3

* change squant->auto
2024-05-10 09:03:39 -07:00

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "fmha_fwd.hpp"
#include "ck_tile/host.hpp"
#include "mask.hpp"
#include "utils.hpp"
#include <array>
#include <cstring>
#include <functional>
#include <numeric>
#include <ostream>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
template <typename T>
std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
{
using size_type = typename std::vector<T>::size_type;
os << "[";
for(size_type idx = 0; idx < v.size(); ++idx)
{
if(0 < idx)
{
os << ", ";
}
os << v[idx];
}
return os << "]";
}
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("v", "1", "weather do CPU validation or not")
.insert("mode", "0", "kernel mode. 0:batch, 1:group")
.insert("b", "2", "batch size")
.insert("h", "8", "num of head, for q")
.insert("h_k",
"-1",
"num of head, for k/v, -1 means equal to h\n"
"if not equal to h, then this is GQA/MQA case")
.insert("s",
"3328",
"seqlen_q. if group-mode, means the average value of seqlen_q\n"
"total_seqlen_q = seqlen_q * batch, and seqlen_q per batch may vary")
.insert("s_k", "-1", "seqlen_k, -1 means equal to s")
.insert("d", "128", "head dim for q, k")
.insert("d_v", "-1", "head dim for v, -1 means equal to d")
.insert("scale_s",
"0",
"scale factor of S. 0 means equal to 1/sqrt(hdim).\n"
"note when squant=1, this value will be modified by range_q/k")
.insert("range_q", "16", "per-tensor quantization range of q. used if squant=1.")
.insert("range_k", "16", "per-tensor quantization range of k. used if squant=1.")
.insert("range_v", "16", "per-tensor quantization range of v. used if squant=1.")
.insert("range_p", "1", "per-tensor quantization range of p [e^(s-m)]. used if squant=1.")
.insert("range_o", "16", "per-tensor quantization range of o (p*v). used if squant=1.")
.insert("squant",
"auto",
"if using static quantization fusion or not. auto: fp8 will default use squant, "
"other will not\n"
"0: no static quant(not implemented) 1: apply scale_p and scale_o with respect to "
"P and O.\n"
"calculate scale_s, scale_p, scale_o according to range_q, range_k, range_v, "
"range_p, range_o")
.insert("iperm",
"1",
"permute input\n"
"if true, will be b*h*s*d, else b*s*h*d")
.insert("operm", "1", "permute output")
.insert("bias",
"n",
"n or 0, no bias\n"
"e(lementwise) or 1, elementwise bias with 1*1*s*s. e:1, 1*h*s*s. e:2, b*h*s*s\n"
"a(libi) or 2, alibi with 1*h. a:1, b*h")
.insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
.insert("mask",
"0",
"0: no mask, 1: top-left(same as 't'), 2:bottom-right(same as 'b')\n"
"'t', top-left causal mask, 'b', bottom-r causal mask\n"
"'t:l,r', top-left sliding window attn(swa) with FA style left right size\n"
"'b:l,r', bottom-r sliding window attn(swa) with FA style left right size\n"
"'xt:window_size', xformer style masking from top-left, window_size negative is "
"causal, positive is swa\n"
"'xb:window_size', xformer style masking from bottom-r, window_size negative is "
"causal, positive is swa\n"
"'g:y,x', generic attention mask coordinate with y/x size (only debug purpose for "
"now)")
.insert("vlayout", "r", "r for row-major(seqlen*hdim), c for col-major(hdim*seqlen)")
.insert("lse", "0", "0 not store lse, 1 store lse")
.insert("kname", "0", "if set to 1 will print kernel name")
.insert("init",
"uf",
"init method. ui, uniform random int, ni, normalized random int\n"
"uf, uniform random float, nf, normalized random float, tf, trig float, uf:q, "
"quantization")
.insert("seed",
"11939",
"random seed used for initializing input tensors. 0 for "
"non-deterministic seed")
.insert("warmup", "5", "number of iterations before benchmark the kernel")
.insert("repeat", "20", "number of iterations to benchmark the kernel");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
}
// different threshold for different dtype
template <typename DataType>
auto get_elimit(std::string /*init_method*/)
{
double rtol = 1e-3;
double atol = 1e-3;
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<ck_tile::bf16_t>(std::string init_method)
{
if(init_method == "ui" || init_method == "ni")
{
double rtol = 1e-2;
double atol = 1e-2;
return ck_tile::make_tuple(rtol, atol);
}
else if(init_method == "nf")
{
double rtol = 1e-2;
double atol = 1e-2;
return ck_tile::make_tuple(rtol, atol);
}
else
{
double rtol = 3e-3;
double atol = 3e-3;
return ck_tile::make_tuple(rtol, atol);
}
}
template <>
auto get_elimit<ck_tile::fp8_t>(std::string init_method)
{
if(init_method == "ui" || init_method == "ni")
{
unsigned max_rounding_point_distance = 0;
double atol = 2e-3;
return ck_tile::make_tuple(max_rounding_point_distance, atol);
}
else
{
unsigned max_rounding_point_distance = 1;
double atol = 0.0625;
return ck_tile::make_tuple(max_rounding_point_distance, atol);
}
}
template <typename DataType>
bool run(const ck_tile::ArgParser& arg_parser)
{
std::string data_type = arg_parser.get_str("prec");
int do_validation = arg_parser.get_int("v");
auto mode = static_cast<mode_enum>(arg_parser.get_uint32("mode"));
ck_tile::index_t batch = arg_parser.get_int("b");
ck_tile::index_t nhead = arg_parser.get_int("h");
ck_tile::index_t nhead_k = arg_parser.get_int("h_k");
if(nhead_k < 0)
nhead_k = nhead;
if(nhead % nhead_k != 0)
{
std::cerr << "nhead:" << nhead << " must be multiple of nhead_k:" << nhead_k << std::endl;
return false;
}
ck_tile::index_t seqlen_q = arg_parser.get_int("s");
ck_tile::index_t seqlen_k = arg_parser.get_int("s_k");
if(seqlen_k < 0)
seqlen_k = seqlen_q;
ck_tile::index_t hdim_q = arg_parser.get_int("d");
ck_tile::index_t hdim_v = arg_parser.get_int("d_v");
if(hdim_v < 0)
hdim_v = hdim_q;
bool i_perm = arg_parser.get_bool("iperm"); // if true, will be batch * nhead * seqlen * hdim
bool o_perm = arg_parser.get_bool("operm"); // if false, will be batch * seqlen * nhead * hdim
float scale_s = arg_parser.get_float("scale_s");
if(scale_s == .0f)
scale_s = 1.0 / ck_tile::sqrt(static_cast<float>(hdim_q)); // TODO: q ? v ?
std::string squant_str = arg_parser.get_str("squant");
bool squant = [&]() {
if(squant_str == "auto")
{
if(data_type == "fp8")
return true;
else
return false;
}
else
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");
bias_info bias = bias_info::decode(arg_parser.get_str("bias"));
mask_info mask = mask_info::decode(arg_parser.get_str("mask"), seqlen_q, seqlen_k);
std::string init_method = arg_parser.get_str("init");
std::optional<uint32_t> seed = arg_parser.get_uint32("seed");
if(*seed == 0)
{
seed.reset();
}
int stream_warmup = arg_parser.get_int("warmup");
int stream_repeat = arg_parser.get_int("repeat");
bool kname = arg_parser.get_bool("kname");
ck_tile::stream_config stream_config{
nullptr, true, /* log_level = */ (kname ? 1 : 0), stream_warmup, stream_repeat};
const auto seqstart_q_host = generate_seqstarts(mode, batch, seqlen_q);
const auto seqstart_k_host = generate_seqstarts(mode, batch, seqlen_k);
using TypeConfig = FmhaFwdTypeConfig<DataType>;
using QDataType = typename TypeConfig::QDataType;
using KDataType = typename TypeConfig::KDataType;
using VDataType = typename TypeConfig::VDataType;
using BiasDataType = typename TypeConfig::BiasDataType;
using LSEDataType = typename TypeConfig::LSEDataType;
using SaccDataType = typename TypeConfig::SaccDataType;
using SMPLComputeDataType = typename TypeConfig::SMPLComputeDataType;
using PDataType = typename TypeConfig::PDataType;
using OaccDataType = typename TypeConfig::OaccDataType;
using ODataType = typename TypeConfig::ODataType;
// accumulation numbers for performance evaluation
std::size_t flop = 0, num_byte = 0;
auto max_seqlen_q =
std::numeric_limits<int32_t>::min(); // we will use max seqlen to decide grid size
{
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
{
const int32_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
const int32_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
if(max_seqlen_q < real_seqlen_q)
{
max_seqlen_q = real_seqlen_q;
}
flop += nhead * (static_cast<std::size_t>(2) * real_seqlen_q * real_seqlen_k * hdim_q +
static_cast<std::size_t>(2) * real_seqlen_q * hdim_v * real_seqlen_k);
num_byte += nhead * (sizeof(QDataType) * real_seqlen_q * hdim_q +
sizeof(KDataType) * real_seqlen_k * hdim_q +
sizeof(VDataType) * hdim_v * real_seqlen_k +
sizeof(ODataType) * real_seqlen_q * hdim_v);
}
}
auto get_lengths = [&](bool permute,
ck_tile::index_t b /*batch*/,
ck_tile::index_t h /*nhead*/,
ck_tile::index_t s /*seqlen*/,
ck_tile::index_t d /*hdim*/) {
if(permute)
return std::array<ck_tile::index_t, 4>{b, h, s, d};
else
return std::array<ck_tile::index_t, 4>{b, s, h, d};
};
bool is_v_rowmajor = vlayout == std::string("r");
// host memory for storing all the tensor elements
const ck_tile::index_t shape_batch = (mode == mode_enum::batch ? batch : 1);
const ck_tile::index_t shape_seqlen_q =
(mode == mode_enum::batch ? seqlen_q : seqstart_q_host.back());
const ck_tile::index_t shape_seqlen_k =
(mode == mode_enum::batch ? seqlen_k : seqstart_k_host.back());
ck_tile::HostTensor<QDataType> q_host(
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
ck_tile::HostTensor<KDataType> k_host(
get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_q));
ck_tile::HostTensor<VDataType> v_host(
is_v_rowmajor ? get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_v)
: get_lengths(i_perm, shape_batch, nhead_k, hdim_v, shape_seqlen_k));
ck_tile::HostTensor<BiasDataType> bias_host(
bias.type == bias_enum::elementwise_bias
? get_lengths(i_perm, 1, 1, shape_seqlen_q, shape_seqlen_k)
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1} /* dummy shape for simplifying code */);
ck_tile::HostTensor<SaccDataType> alibi_slope_host(
bias.type == bias_enum::alibi
? (bias.rank_info == 0 ? std::array<ck_tile::index_t, 2>{1, nhead}
: std::array<ck_tile::index_t, 2>{batch, nhead})
: std::array<ck_tile::index_t, 2>{1, 1});
// self define lse data layout as [shape_batch, nhead, shape_seqlen_q]
ck_tile::HostTensor<LSEDataType> lse_host(
lse ? std::array<ck_tile::index_t, 3>{shape_batch, nhead, shape_seqlen_q}
: std::array<ck_tile::index_t, 3>{1, 1, 1} /* dummy shape for simplifying code */);
ck_tile::HostTensor<ODataType> o_host(
get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
if(init_method == "ui" || init_method == "0")
{
ck_tile::FillUniformDistributionIntegerValue<QDataType>{-3.f, 3.f, seed}(q_host);
ck_tile::FillUniformDistributionIntegerValue<KDataType>{-3.f, 3.f, seed}(k_host);
ck_tile::FillUniformDistributionIntegerValue<VDataType>{-3.f, 3.f, seed}(v_host);
ck_tile::FillUniformDistributionIntegerValue<BiasDataType>{-3.f, 3.f, seed}(bias_host);
}
else if(init_method == "ni")
{
ck_tile::FillNormalDistributionIntegerValue<QDataType>{-3.f, 3.f, seed}(q_host);
ck_tile::FillNormalDistributionIntegerValue<KDataType>{-3.f, 3.f, seed}(k_host);
ck_tile::FillNormalDistributionIntegerValue<VDataType>{-3.f, 3.f, seed}(v_host);
ck_tile::FillNormalDistributionIntegerValue<BiasDataType>{-3.f, 3.f, seed}(bias_host);
}
else if(init_method == "uf" || init_method == "1")
{
ck_tile::FillUniformDistribution<QDataType>{0.f, 1.f, seed}(q_host);
ck_tile::FillUniformDistribution<KDataType>{0.f, 1.f, seed}(k_host);
ck_tile::FillUniformDistribution<VDataType>{0.f, 1.f, seed}(v_host);
ck_tile::FillUniformDistribution<BiasDataType>{0.f, 1.f, seed}(bias_host);
}
else if(init_method == "nf")
{
ck_tile::FillNormalDistribution<QDataType>{0.f, 3.f, seed}(q_host);
ck_tile::FillNormalDistribution<KDataType>{0.f, 3.f, seed}(k_host);
ck_tile::FillNormalDistribution<VDataType>{0.f, 3.f, seed}(v_host);
ck_tile::FillNormalDistribution<BiasDataType>{0.f, 3.f, seed}(bias_host);
}
else if(init_method == "tf" || init_method == "2")
{
ck_tile::FillTrigValue<QDataType>{}(q_host);
ck_tile::FillTrigValue<KDataType>{}(k_host);
ck_tile::FillTrigValue<VDataType>{}(v_host);
ck_tile::FillTrigValue<BiasDataType>{}(bias_host);
}
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<VDataType>{-dtype_max, dtype_max, seed}(v_host);
// bias_fp8 = qscale_bias * bias_fp32
float qscale_bias = (dtype_max / range_q) * (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);
}
if(bias.type == bias_enum::alibi)
{
auto slopes = ck_tile::get_alibi_slopes<SaccDataType>(nhead);
assert(slopes.size() == nhead);
if(bias.rank_info == 0)
{
// alibi in 1*h
std::copy(slopes.begin(), slopes.end(), alibi_slope_host.begin());
}
else
{
// alibi in b*h
for(auto i_b = 0; i_b < batch; i_b++)
{
std::copy(slopes.begin(), slopes.end(), alibi_slope_host.begin() + i_b * nhead);
}
}
}
ck_tile::DeviceMem q_buf(q_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem k_buf(k_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem v_buf(v_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem bias_buf(bias_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem lse_buf(lse_host.get_element_space_size_in_bytes());
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 alibi_slope_buf(alibi_slope_host.get_element_space_size_in_bytes());
q_buf.ToDevice(q_host.data());
k_buf.ToDevice(k_host.data());
v_buf.ToDevice(v_host.data());
bias_buf.ToDevice(bias_host.data());
seqstart_q.ToDevice(seqstart_q_host.data());
seqstart_k.ToDevice(seqstart_k_host.data());
alibi_slope_buf.ToDevice(alibi_slope_host.data());
// clang-format off
auto layout_str = [&](bool permute){
if (permute) return std::string("bhsd");
else return std::string("bshd");
};
auto io_layout = [&](bool iperm_, bool operm_) {
if (iperm_ == operm_) return layout_str(iperm_);
else return layout_str(iperm_) + std::string("-") + layout_str(operm_);
};
// clang-format on
const std::string prec = arg_parser.get_str("prec");
std::cout << "[" << prec << "|" << mode << "|" << io_layout(i_perm, o_perm) << "] b:" << batch
<< ", h:" << nhead << "/" << nhead_k << ", s:" << seqlen_q << "/" << seqlen_k
<< ", d:" << hdim_q << "/" << hdim_v << ", scale_s:" << scale_s << ", bias:" << bias
<< ", lse:" << lse << ", squant:" << squant << ", mask:" << mask << ", v:" << vlayout
<< std::flush;
auto fmha_traits = fmha_fwd_traits{hdim_q,
hdim_v,
data_type,
mode == mode_enum::group,
is_v_rowmajor,
mask.type,
bias.type,
lse,
squant};
auto p_compute_element_func = [&]() {
if constexpr(std::is_same_v<DataType, 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>)
return ck_tile::composes(ck_tile::saturates<ck_tile::fp8_t>{},
ck_tile::scales{scale_o});
else
return ck_tile::identity{};
}();
auto fmha_args = [&]() {
assert(nhead % nhead_k == 0);
/// NOTE: we broadcast bias from [1, 1, seqlen_q, seqlen_k] to [batch, nhead, seqlen_q,
/// seqlen_k] in this example, hence both the 'batch_stride_bias' &
/// 'nhead_stride_bias' are 0.
// setup stride_* arguments
const ck_tile::index_t stride_q = (i_perm ? hdim_q : nhead * hdim_q);
const ck_tile::index_t stride_k = (i_perm ? hdim_q : nhead_k * hdim_q);
const ck_tile::index_t stride_v = [&]() {
if(is_v_rowmajor)
return i_perm ? hdim_v : nhead_k * hdim_v;
else
return i_perm ? shape_seqlen_k : nhead_k * shape_seqlen_k;
}();
const ck_tile::index_t stride_bias = (i_perm ? shape_seqlen_k : 1 * shape_seqlen_k);
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
// setup nhead_stride_* arguments
const ck_tile::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
const ck_tile::index_t nhead_stride_k = (i_perm ? shape_seqlen_k * hdim_q : hdim_q);
const ck_tile::index_t nhead_stride_v = [&]() {
if(is_v_rowmajor)
return i_perm ? shape_seqlen_k * hdim_v : hdim_v;
else
return i_perm ? hdim_v * shape_seqlen_k : shape_seqlen_k;
}();
const ck_tile::index_t nhead_stride_bias =
(i_perm ? 0 * shape_seqlen_q * shape_seqlen_k : 0 * shape_seqlen_k);
const ck_tile::index_t nhead_stride_lse = (shape_seqlen_q * 1);
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
// setup batch_stride_* arguments
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
const ck_tile::index_t batch_stride_k = (nhead_k * shape_seqlen_k * hdim_q);
const ck_tile::index_t batch_stride_v = (nhead_k * hdim_v * shape_seqlen_k);
const ck_tile::index_t batch_stride_bias = (0 * nhead * shape_seqlen_q * shape_seqlen_k);
const ck_tile::index_t batch_stride_lse = (nhead * shape_seqlen_q * 1);
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
return fmha_fwd_args{q_buf.GetDeviceBuffer(),
k_buf.GetDeviceBuffer(),
v_buf.GetDeviceBuffer(),
bias.type == bias_enum::alibi ? alibi_slope_buf.GetDeviceBuffer()
: bias_buf.GetDeviceBuffer(),
lse_buf.GetDeviceBuffer(),
o_buf.GetDeviceBuffer(),
seqstart_q.GetDeviceBuffer(),
seqstart_k.GetDeviceBuffer(),
nullptr,
shape_seqlen_q,
shape_seqlen_k,
batch,
max_seqlen_q,
hdim_q,
hdim_v,
nhead,
nhead_k,
scale_s,
scale_p,
scale_o,
stride_q,
stride_k,
stride_v,
bias.type == bias_enum::alibi ? (bias.rank_info == 0 ? 0 : nhead)
: stride_bias,
stride_o,
nhead_stride_q,
nhead_stride_k,
nhead_stride_v,
nhead_stride_bias,
nhead_stride_lse,
nhead_stride_o,
batch_stride_q,
batch_stride_k,
batch_stride_v,
batch_stride_bias,
batch_stride_lse,
batch_stride_o,
mask.left,
mask.right,
static_cast<ck_tile::index_t>(mask.type)};
}();
float ave_time = fmha_fwd(fmha_traits, fmha_args, stream_config);
if(ave_time < 0)
{
std::cout << ", not supported yet" << std::flush << std::endl;
return false;
}
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_byte / 1.E6 / ave_time;
std::cout << std::fixed << ", " << std::setprecision(3) << ave_time << " ms, "
<< std::setprecision(2) << tflops << " TFlops, " << std::setprecision(2) << gb_per_sec
<< " GB/s" << std::flush;
if(!do_validation)
{
std::cout << std::flush << std::endl;
return true;
}
o_buf.FromDevice(o_host.data());
lse_buf.FromDevice(lse_host.data());
bool pass = true;
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
{
const ck_tile::index_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
const ck_tile::index_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
// adjust matrix index according to the mode
const ck_tile::index_t b = (mode == mode_enum::batch ? wb : 0);
const ck_tile::index_t query_offset = (mode == mode_enum::batch ? 0 : seqstart_q_host[wb]);
const ck_tile::index_t key_offset = (mode == mode_enum::batch ? 0 : seqstart_k_host[wb]);
const auto v_host_ref_lengths =
std::array<ck_tile::index_t, 3>{nhead, hdim_v, real_seqlen_k};
const auto v_host_ref_strides =
is_v_rowmajor
? std::array<ck_tile::index_t, 3>{hdim_v * real_seqlen_k, 1, hdim_v}
: std::array<ck_tile::index_t, 3>{hdim_v * real_seqlen_k, real_seqlen_k, 1};
ck_tile::HostTensor<QDataType> q_host_ref({nhead, real_seqlen_q, hdim_q});
ck_tile::HostTensor<KDataType> k_host_ref({nhead, real_seqlen_k, hdim_q});
ck_tile::HostTensor<VDataType> v_host_ref(v_host_ref_lengths, v_host_ref_strides);
ck_tile::HostTensor<ODataType> o_host_ref({nhead, real_seqlen_q, hdim_v});
ck_tile::HostTensor<SMPLComputeDataType> s_host_ref({nhead, real_seqlen_q, real_seqlen_k});
ck_tile::HostTensor<PDataType> p_host_ref({nhead, real_seqlen_q, real_seqlen_k});
ck_tile::HostTensor<SMPLComputeDataType> lse_host_ref({nhead, real_seqlen_q});
ck_tile::index_t nr = nhead / nhead_k;
// clang-format off
// permute
if(i_perm) q_host_ref.ForEach([&](auto& self, auto i) { self(i) = q_host(b, i[0], i[1] + query_offset, i[2]); });
else q_host_ref.ForEach([&](auto& self, auto i) { self(i) = q_host(b, i[1] + query_offset, i[0], i[2]); });
if(i_perm) k_host_ref.ForEach([&](auto& self, auto i) { self(i) = k_host(b, i[0] / nr, i[1] + key_offset, i[2]); });
else k_host_ref.ForEach([&](auto& self, auto i) { self(i) = k_host(b, i[1] + key_offset, i[0] / nr, i[2]); });
if (is_v_rowmajor) {
// v_host_ref: [nhead, hdim, seq], v_host: [b, h_k, s, d]
if(i_perm) v_host_ref.ForEach([&](auto& self, auto i) { self(i) = v_host(b, i[0] / nr, i[2] + key_offset, i[1]); });
// v_host_ref: [nhead, hdim, seq], v_host: [b, s, h_k, d]
else v_host_ref.ForEach([&](auto& self, auto i) { self(i) = v_host(b, i[2] + key_offset, i[0] / nr, i[1]); });
}
else {
if(i_perm) v_host_ref.ForEach([&](auto& self, auto i) { self(i) = v_host(b, i[0] / nr, i[1], i[2] + key_offset); });
else v_host_ref.ForEach([&](auto& self, auto i) { self(i) = v_host(b, i[1], i[0] / nr, i[2] + key_offset); });
}
// clang-format on
// reference
ck_tile::reference_batched_gemm<QDataType, KDataType, SaccDataType, SMPLComputeDataType>(
q_host_ref,
k_host_ref,
s_host_ref,
ck_tile::identity{},
ck_tile::identity{},
ck_tile::scales(scale_s));
if(bias.type == bias_enum::elementwise_bias)
{
// elementwise bias
ck_tile::HostTensor<BiasDataType> bias_host_ref({1, real_seqlen_q, real_seqlen_k});
// clang-format off
if(i_perm)
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, 0, i[1] + query_offset, i[2] + key_offset); });
else
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, i[1] + query_offset, 0, i[2] + key_offset); });
// clang-format on
// broadcast from [1, real_seqlen_q, real_seqlen_k] to [nhead, real_seqlen_q,
// real_seqlen_k]
ck_tile::reference_batched_elementwise<SMPLComputeDataType,
BiasDataType,
SMPLComputeDataType,
SMPLComputeDataType>(
s_host_ref, bias_host_ref, s_host_ref);
}
else if(bias.type == bias_enum::alibi)
{
// alibi construct elementwise bias to verify
auto alibi_host = [&]() {
if(mask.type != mask_enum::no_mask)
{
return ck_tile::make_alibi_from_lr_mask<SaccDataType, true>(
0,
mask.left,
mask.right,
real_seqlen_q,
real_seqlen_k,
static_cast<ck_tile::GenericAttentionMaskEnum>(mask.type));
}
else
{
return ck_tile::Alibi<SaccDataType, true>{
0, real_seqlen_q, real_seqlen_k, ck_tile::AlibiMode::VERTICAL};
}
}();
ck_tile::HostTensor<SaccDataType> alibi_bias_host_ref(
{nhead, real_seqlen_q, real_seqlen_k});
auto i_b_slope = bias.rank_info == 0 ? 0 : wb;
for(auto i_h = 0; i_h < nhead; i_h++)
{
SaccDataType current_slope = alibi_slope_host(i_b_slope, i_h);
alibi_host.slope = current_slope;
for(auto i_r = 0; i_r < real_seqlen_q; i_r++)
{
for(auto i_c = 0; i_c < real_seqlen_k; i_c++)
{
SaccDataType pixel = 0;
alibi_host.update(pixel, i_r, i_c);
alibi_bias_host_ref(i_h, i_r, i_c) = pixel;
}
}
}
// [nhead, real_seqlen_q, real_seqlen_k]
ck_tile::reference_batched_elementwise<SMPLComputeDataType,
SaccDataType,
SMPLComputeDataType,
SMPLComputeDataType>(
s_host_ref, alibi_bias_host_ref, s_host_ref);
}
if(mask.type == mask_enum::no_mask)
{
ck_tile::reference_batched_masking<SaccDataType>(
s_host_ref, FmhaMasks::NoMask{real_seqlen_q, real_seqlen_k});
}
else if(mask.type == mask_enum::window_generic)
{
ck_tile::reference_batched_masking<SaccDataType>(
s_host_ref,
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
mask.left, mask.right, real_seqlen_q, real_seqlen_k));
}
else
{
// if left window size is negative, means causal
// else means generic (for current batch)
if(mask.left < 0)
ck_tile::reference_batched_masking<SaccDataType>(
s_host_ref,
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::CausalMask>(
mask.left,
mask.right,
real_seqlen_q,
real_seqlen_k,
mask.type == mask_enum::mask_top_left));
else
ck_tile::reference_batched_masking<SaccDataType>(
s_host_ref,
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
mask.left,
mask.right,
real_seqlen_q,
real_seqlen_k,
mask.type == mask_enum::mask_top_left));
}
if(lse)
{
ck_tile::reference_batched_softmax<SMPLComputeDataType, SMPLComputeDataType, PDataType>(
s_host_ref, p_host_ref, p_compute_element_func, lse_host_ref);
}
else
{
ck_tile::reference_batched_softmax<SMPLComputeDataType, SMPLComputeDataType, PDataType>(
s_host_ref, p_host_ref, p_compute_element_func);
}
ck_tile::reference_batched_gemm<PDataType, VDataType, OaccDataType, ODataType>(
p_host_ref,
v_host_ref,
o_host_ref,
ck_tile::identity{},
ck_tile::identity{},
oacc_element_func);
ck_tile::HostTensor<ODataType> o_host_result({nhead, real_seqlen_q, hdim_v});
// clang-format off
// permute
if(o_perm) o_host_result.ForEach([&](auto& self, auto idx) { self(idx) = o_host(b, idx[0], idx[1] + query_offset, idx[2]); });
else o_host_result.ForEach([&](auto& self, auto idx) { self(idx) = o_host(b, idx[1] + query_offset, idx[0], idx[2]); });
// clang-format on
auto [rtol, atol] = get_elimit<DataType>(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;
if(!cur_pass)
{
std::cerr << "OUT mismatch found at batch: " << wb << std::endl
<< "\tseqlen_q: " << real_seqlen_q << std::endl
<< "\tseqlen_k: " << real_seqlen_k << std::endl
<< "\tseqstart_q: " << seqstart_q_host << std::endl
<< "\tseqstart_k: " << seqstart_k_host << std::endl;
break;
}
if(lse)
{
ck_tile::HostTensor<SMPLComputeDataType> lse_host_result({nhead, real_seqlen_q});
lse_host_result.ForEach([&](auto& self, auto idx) {
self(idx) = lse_host(b, idx[0], idx[1] + query_offset);
});
bool lse_pass = ck_tile::check_err(lse_host_result,
lse_host_ref,
"LSE Error: Incorrect results!",
rtol,
atol,
/* allow_infinity_ref = */ true);
pass &= lse_pass;
if(!cur_pass)
{
std::cerr << "LSE mismatch found at batch: " << wb << std::endl
<< "\tseqlen_q: " << real_seqlen_q << std::endl
<< "\tseqlen_k: " << real_seqlen_k << std::endl
<< "\tseqstart_q: " << seqstart_q_host << std::endl
<< "\tseqstart_k: " << seqstart_k_host << std::endl;
break;
}
}
}
std::cout << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
return pass;
}
int main(int argc, char* argv[])
{
auto [result, arg_parser] = create_args(argc, argv);
if(!result)
return -1;
const std::string data_type = arg_parser.get_str("prec");
if(data_type == "fp16")
{
return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
}
else if(data_type == "bf16")
{
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
}
else if(data_type == "fp8")
{
return run<ck_tile::fp8_t>(arg_parser) ? 0 : -2;
}
return -3;
}
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