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Merge commit '2aec38f9ec67bfbdccbdb3a5c25913e5a9ba6136' into develop

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assistant-librarian[bot]
2025-09-19 07:12:19 +00:00
parent 6e7460a434
commit 2d48a99ddd
17 changed files with 287 additions and 162 deletions
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2
example/ck_tile/01_fmha/README.md
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@@ -131,4 +131,4 @@ TBD
## FP8 experimental support
As described in [this blog](https://blog.hippoml.com/8bit-hippoattention-up-to-3x-faster-compared-to-flashattentionv2-8f9def90b482), we have an experimental support for fp8 fmha kernels, you can evaluate the performance by setting the arg `-prec=fp8` to the `tile_example_fmha_fwd`, on a gfx942 machine and ROCm 6.0+.
Currently we only support `-vlayout=c`( `hdim*seqlen` for V matrix) and `-squant=1`(static quantization) with `hdim=128` for fp8 now. Full feature support will come later.
Currently we only support `-vlayout=r`( `seqlen*hdim` for V matrix) for fp8 and fp8bf16 now. Full feature support will come later.

3
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
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@@ -7,7 +7,8 @@ FWD_DTYPE_MAP = {
"bf16" : "FmhaFwdBf16",
"fp8" : "FmhaFwdFp8",
"fp8fp16": "FmhaFwdFp8Fp16",
"fp8bf16": "FmhaFwdFp8Bf16"
"fp8bf16": "FmhaFwdFp8Bf16",
"fp8fp32": "FmhaFwdFp8Fp32"
}
BWD_DTYPE_MAP = {

48
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
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@@ -163,7 +163,7 @@ float fmha_fwd(fmha_fwd_traits t, fmha_fwd_args a, const ck_tile::stream_config&
[[maybe_unused]] auto get_num_blocks = [&](unsigned kM0) {{
return get_num_thread_blocks(a.batch, a.nhead_q, a.max_seqlen_q, kM0);
}};
const bool has_load_tr = ck_tile::is_load_tr_supported();
{F_dispatch}
@@ -248,11 +248,11 @@ class FmhaFwdApiTrait:
if self.spad == 't' : return f'true /*a.seqlen_q % {self.bm0} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.seqlen_q % {self.bm0} == 0'
else: assert False
@property
def seqtune(self) -> str:
if self.bm0 == 128: return 'true/*fall back to largest tile*/' # group mode only generate spad/skpad == true
else:
else:
return f'a.seqlen_q <= {self.bm0}'
@property
@@ -351,7 +351,7 @@ class FmhaFwdPipeline:
if self.F_squant == 't' : n += '_squant'
else: n += '_nsquant'
if self.F_trload == 't' : n += '_trload'
else: n += '_ntrload'
@@ -378,7 +378,7 @@ class FmhaFwdApiPool:
"t": "has_load_tr",
"f": "true"
}
per_tr_load =str()
for tr_load in ["t", "f"]:
per_dtypes=str()
@@ -550,12 +550,16 @@ class KernelComponentFactory:
(192,192) : [FmhaFwdTileSize(128, 128, 32, 192, 32, 192, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, 1)],
(256,256) : [FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, 32, 32, 16, -1)],
}
elif dtype == 'fp8' or dtype == 'bf8':
elif dtype == 'fp8' or dtype == 'fp8bf16':
return {
(64,64 ) : [FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1)],
(128,128) : [FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1)],
(256,256) : [FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1)],
}
elif dtype == 'fp8fp32':
return {
(128,128) : [FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1)],
}
else:
return None
@@ -567,9 +571,9 @@ class KernelComponentFactory:
# TODO: the order of List matters! the later in this list will be also be checked later
# TODO: currently for qr pipeline, let 't' padding to appear later!!
# TODO: how to design this more generic?
squant = 't' if dtype == 'fp8' else 'f'
pipelines = []
if dtype in ['fp16', 'bf16']:
squant = 'f'
for logits, mask, bias, lse, dropout, skip in itertools.product(["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"], ["t", "f"]):
if hdim == 256 and hdim_v == 256:
pipelines.append(FmhaFwdPipeline('qr', 'row', 'f', 'f', 'f', 'f', logits, bias, lse, dropout, squant, mask, skip, 'f'))
@@ -589,11 +593,12 @@ class KernelComponentFactory:
pipelines.append(FmhaFwdPipeline('qr_async_trload', 'row', 'f', 'f', 't', 't', logits, bias, lse, dropout, squant, mask, skip, 't'))
if receipt == 1 and bias != "bias":
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 't', 't', logits, bias, lse, dropout, squant, mask, skip, 'f')) # TODO: cover arbitraty hdim
elif dtype in ['fp8', 'bf8']:
elif dtype in ['fp8', 'fp8bf16', 'fp8fp32']:
# no need lse/dropout kernels
for logits, mask, bias in itertools.product(["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', logits, bias, 'f', 'f', squant, mask, 'f', 'f'))
elif dtype in ['fp8fp16', 'fp8bf16']:
for logits, squant, mask, bias in itertools.product(["f"], ["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(FmhaFwdPipeline('qr', 'row', 'f', 'f', 'f', 'f', logits, bias, 'f', 'f', squant, mask, 'f', 'f'))
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 'f', 'f', logits, bias, 'f', 'f', squant, mask, 'f', 'f'))
elif dtype in ['fp8fp16', 'bf8']:
# TODO
None
else:
@@ -674,25 +679,34 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, optdim_list, mask_impl
continue
# Aiter(mha_fwd) integration
elif receipt == 100:
cond = dtype in ['fp16', 'bf16']
cond = dtype in ['fp16', 'bf16', 'fp8bf16']
cond &= mode == 'batch'
cond &= pipeline.F_vlayout == 'row'
cond &= pipeline.F_squant == 'f'
if dtype == 'fp8bf16':
cond &= hdim == 128
if not cond:
continue
# Aiter(mha_varlen_fwd) integration
elif receipt == 200:
cond = dtype in ['fp16', 'bf16']
cond = dtype in ['fp16', 'bf16', 'fp8bf16']
cond &= mode == 'group'
cond &= pipeline.F_vlayout == 'row'
cond &= pipeline.F_squant == 'f'
if dtype == 'fp8bf16':
cond &= hdim == 128
if not cond:
continue
# aiter::mha_fwd C++ api integration
elif receipt == 600:
cond = dtype in ['fp16', 'bf16']
cond = dtype in ['fp16', 'bf16', 'fp8bf16']
cond &= pipeline.F_vlayout == 'row'
cond &= pipeline.F_squant == 'f'
if dtype == 'fp8bf16':
cond &= hdim == 128
if not cond:
continue
elif receipt == 888:
cond = dtype in ['fp8', 'fp8bf16', 'fp8fp32']
cond &= pipeline.F_vlayout == 'row'
cond &= hdim == 128
if not cond:
continue

1
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
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@@ -645,7 +645,6 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, 32, 32, 32, -1),
}
else:
return None

10
example/ck_tile/01_fmha/codegen/ops/fmha_pagedkv_prefill.py
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@@ -465,14 +465,14 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, optdim_list, mask_impl
squant = 't' if dtype == 'fp8' else 'f'
pipelines = []
if dtype in ['fp16', 'bf16']:
for logits, mask, bias, pagedkv, skip in itertools.product(["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"]):
pipelines.append(FmhaFwdPipeline('qr_pagedkv', 'col', 't', 'f', 'f', 'f', logits, bias, 'f', pagedkv, squant, mask, skip))
pipelines.append(FmhaFwdPipeline('qr_pagedkv', 'col', 't', 't', 'f', 'f', logits, bias, 'f', pagedkv, squant, mask, skip))
for logits, mask, bias, pagedkv, skip in itertools.product(["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t"], ["f"]):
pipelines.append(FmhaFwdPipeline('qr_pagedkv', 'row', 't', 'f', 'f', 'f', logits, bias, 'f', pagedkv, squant, mask, skip))
pipelines.append(FmhaFwdPipeline('qr_pagedkv', 'row', 't', 't', 'f', 'f', logits, bias, 'f', pagedkv, squant, mask, skip))
elif dtype in ['fp8', 'bf8']:
# TODO
None
# no need lse/dropout kernels
for logits, mask, bias in itertools.product(["t", "f"], get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(FmhaFwdPipeline('qr_pagedkv', 'row', 'f', 'f', 'f', 'f', logits, bias, 'f', 't', squant, mask, 'f'))
pipelines.append(FmhaFwdPipeline('qr_pagedkv', 'row', 't', 't', 'f', 'f', logits, bias, 'f', 't', squant, mask, 'f'))
elif dtype in ['fp8fp16', 'fp8bf16']:
# TODO
None

30
example/ck_tile/01_fmha/example_fmha_fwd.cpp
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@@ -44,21 +44,15 @@ auto create_args(int argc, char* argv[])
.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")
"note when squant=1, this value will be modified")
.insert("logits_soft_cap", "0", "attention logits soft capping value.")
.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")
"calculate scale_s, scale_p, scale_o auto")
.insert("iperm",
"1",
"permute input\n"
@@ -89,7 +83,7 @@ auto create_args(int argc, char* argv[])
"uf",
"init method:\n ui or 0 - uniform random int\n ni - normalized random int"
"\n uf or 1 - uniform random float\n nf - normalized random float"
"\n tf or 2 - trig float\n uf:q or ufq or 3 - fp8 quantization")
"\n tf or 2 - trig float\n")
.insert("seed",
"11939",
"random seed used for initializing input tensors. 0 for "
@@ -148,11 +142,6 @@ auto run(const ck_tile::ArgParser& arg_parser)
uint64_t drop_offset = arg_parser.get_uint64("drop_offset");
bool drop_prefs = arg_parser.get_bool("drop_prefs");
std::string mask_str = arg_parser.get_str("mask");
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");
bool is_rotary_interleaved = arg_parser.get_bool("rotary_interleaved");
ck_tile::index_t num_splits = arg_parser.get_int("num_splits");
std::string init_method = arg_parser.get_str("init");
@@ -201,11 +190,6 @@ auto run(const ck_tile::ArgParser& arg_parser)
drop_offset,
drop_prefs,
mask_str,
range_q,
range_k,
range_v,
range_p,
range_o,
squant,
is_rotary_interleaved,
num_splits,
@@ -237,6 +221,14 @@ int main(int argc, char* argv[])
{
return run<FmhaFwdFp8>(arg_parser) == fwd_result::success ? 0 : -2;
}
else if(data_type == "fp8bf16")
{
return run<FmhaFwdFp8Bf16>(arg_parser) == fwd_result::success ? 0 : -2;
}
else if(data_type == "fp8fp32")
{
return run<FmhaFwdFp8Fp32>(arg_parser) == fwd_result::success ? 0 : -2;
}
std::cerr << "Unsupported precision: " << data_type << std::endl;
return -1;
}

36
example/ck_tile/01_fmha/fmha_fwd.hpp
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@@ -41,6 +41,10 @@ struct FmhaFwdFp8Bf16
{
};
struct FmhaFwdFp8Fp32
{
};
template <typename DataType>
struct FmhaFwdTypeConfig;
@@ -108,6 +112,38 @@ struct FmhaFwdTypeConfig<FmhaFwdBf8>
using ODataType = ck_tile::bf8_t;
};
template <>
struct FmhaFwdTypeConfig<FmhaFwdFp8Bf16>
{
using QDataType = ck_tile::fp8_t;
using KDataType = ck_tile::fp8_t;
using VDataType = ck_tile::fp8_t;
using BiasDataType = float;
using RandValOutputDataType = uint8_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SaccDataType = float; // data type for first gemm accumulation
using SMPLComputeDataType = float; // data type for reduction, softmax
using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm
using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::bf16_t;
};
template <>
struct FmhaFwdTypeConfig<FmhaFwdFp8Fp32>
{
using QDataType = ck_tile::fp8_t;
using KDataType = ck_tile::fp8_t;
using VDataType = ck_tile::fp8_t;
using BiasDataType = float;
using RandValOutputDataType = uint8_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SaccDataType = float; // data type for first gemm accumulation
using SMPLComputeDataType = float; // data type for reduction, softmax
using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm
using OaccDataType = float; // data type for second gemm accumulation
using ODataType = float;
};
struct FmhaMasks
{
using NoMask = ck_tile::GenericAttentionMask<false>;

170
example/ck_tile/01_fmha/fmha_fwd_runner.hpp
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@@ -50,20 +50,30 @@ auto get_elimit<FmhaFwdBf16>(std::string /*init_method*/)
}
template <>
auto get_elimit<FmhaFwdFp8>(std::string init_method)
auto get_elimit<FmhaFwdFp8>(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);
}
using TypeConfig = FmhaFwdTypeConfig<FmhaFwdFp8>;
using ODataType = typename TypeConfig::ODataType;
float o_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<ODataType>::max());
double rtol = 0;
double atol = 16 * (o_dtype_max > 240 ? 2 : 1);
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<FmhaFwdFp8Bf16>(std::string /*init_method*/)
{
double rtol = 1e-2;
double atol = 1.8e-1;
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<FmhaFwdFp8Fp32>(std::string /*init_method*/)
{
double rtol = 1e-2;
double atol = 1.8e-1;
return ck_tile::make_tuple(rtol, atol);
}
int num_splits_heuristic(int batch_nhead_mblocks, int num_SMs, int max_splits)
@@ -157,11 +167,6 @@ fwd_result fmha_fwd_run(mode_enum mode,
uint64_t drop_offset,
bool drop_prefs,
std::string mask_str,
float range_q,
float range_k,
float range_v,
float range_p,
float range_o,
bool squant,
bool is_rotary_interleaved,
ck_tile::index_t num_splits,
@@ -180,6 +185,10 @@ fwd_result fmha_fwd_run(mode_enum mode,
return "fp8";
else if constexpr(std::is_same_v<DataTypeConfig, FmhaFwdBf8>)
return "bf8";
else if constexpr(std::is_same_v<DataTypeConfig, FmhaFwdFp8Bf16>)
return "fp8bf16";
else if constexpr(std::is_same_v<DataTypeConfig, FmhaFwdFp8Fp32>)
return "fp8fp32";
else
static_assert(false);
}();
@@ -367,22 +376,6 @@ fwd_result fmha_fwd_run(mode_enum mode,
using OaccDataType = typename TypeConfig::OaccDataType;
using ODataType = typename TypeConfig::ODataType;
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 =
@@ -528,7 +521,7 @@ fwd_result fmha_fwd_run(mode_enum mode,
ck_tile::HostTensor<int32_t> cache_batch_idx_host(use_cache_batch_idx
? std::array<ck_tile::index_t, 1>{batch}
: std::array<ck_tile::index_t, 1>{1});
float max_o = 5.0;
if(init_method == "ui" || init_method == "0")
{
ck_tile::FillUniformDistributionIntegerValue<QDataType>{-3.f, 3.f, next_seed()}(q_host);
@@ -576,32 +569,6 @@ fwd_result fmha_fwd_run(mode_enum mode,
ck_tile::FillTrigValue<VDataType>{}(vnew_host);
ck_tile::FillTrigValue<BiasDataType>{}(bias_host);
}
else if(init_method == "ufq" || init_method == "uf:q" || init_method == "3")
{
// suitable for fp8 quantization
if(!squant)
{
std::cerr << "init method " << init_method << " can not be used without quantization"
<< std::endl;
return fwd_result::invalid_args;
}
ck_tile::FillUniformDistribution<QDataType>{0.f, q_dtype_max, next_seed()}(q_host);
ck_tile::FillUniformDistribution<KDataType>{0.f, k_dtype_max, next_seed()}(k_host);
ck_tile::FillUniformDistribution<KDataType>{0.f, k_dtype_max, next_seed()}(knew_host);
ck_tile::FillUniformDistribution<VDataType>{0.f, v_dtype_max, next_seed()}(v_host);
ck_tile::FillUniformDistribution<VDataType>{0.f, v_dtype_max, next_seed()}(vnew_host);
// bias_fp8 = qscale_bias * bias_fp32
float qscale_bias = (q_dtype_max / range_q) * (k_dtype_max / range_k);
// Assume bias is in [0.f, 1.f] in original fp32
ck_tile::FillUniformDistribution<BiasDataType>{0.f, qscale_bias, next_seed()}(bias_host);
}
else
{
std::cerr << "Unknown value for init argument: " << init_method << std::endl;
return fwd_result::invalid_args;
}
if(bias.type == bias_enum::alibi)
{
auto slopes = ck_tile::get_alibi_slopes<SaccDataType>(nhead);
@@ -625,8 +592,8 @@ fwd_result fmha_fwd_run(mode_enum mode,
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 knew_buf(knew_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem v_buf(v_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem knew_buf(knew_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem vnew_buf(vnew_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem bias_buf(bias_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem lse_acc_buf(lse_acc_host.get_element_space_size_in_bytes());
@@ -650,10 +617,79 @@ fwd_result fmha_fwd_run(mode_enum mode,
ck_tile::DeviceMem block_table_buf(block_table_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem cache_batch_idx_buf(cache_batch_idx_host.get_element_space_size_in_bytes());
float scale_p = 1.f;
float scale_o = 1.f;
if(squant)
{
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
// Q tensor
{
float max_value = ck_tile::type_convert<float>(ck_tile::numeric<QDataType>::min());
q_host.ForEach([&](auto& self, auto idx) {
float val = ck_tile::type_convert<float>(self(idx));
if(val > max_value)
max_value = val;
});
float scale = q_dtype_max / max_value;
q_host.ForEach([&](auto& self, auto idx) {
float val = ck_tile::type_convert<float>(self(idx));
self(idx) = ck_tile::type_convert<QDataType>(val * scale);
});
scale_s = scale_s / scale;
}
// K tensor
{
float max_value = ck_tile::type_convert<float>(ck_tile::numeric<KDataType>::min());
k_host.ForEach([&](auto& self, auto idx) {
float val = ck_tile::type_convert<float>(self(idx));
if(val > max_value)
max_value = val;
});
float scale = k_dtype_max / max_value;
k_host.ForEach([&](auto& self, auto idx) {
float val = ck_tile::type_convert<float>(self(idx));
self(idx) = ck_tile::type_convert<KDataType>(val * scale);
});
scale_s = scale_s / scale;
}
// V tensor
{
float max_value = ck_tile::type_convert<float>(ck_tile::numeric<VDataType>::min());
v_host.ForEach([&](auto& self, auto idx) {
float val = ck_tile::type_convert<float>(self(idx));
if(val > max_value)
max_value = val;
});
float scale = k_dtype_max / max_value;
v_host.ForEach([&](auto& self, auto idx) {
float val = ck_tile::type_convert<float>(self(idx));
self(idx) = ck_tile::type_convert<VDataType>(val * scale);
});
scale_o = (1.0 / p_dtype_max) / scale;
}
scale_p = p_dtype_max;
if constexpr(std::is_same_v<DataTypeConfig, FmhaFwdFp8>)
{
float o_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<ODataType>::max());
scale_o = scale_o * o_dtype_max / max_o;
}
}
q_buf.ToDevice(q_host.data());
k_buf.ToDevice(k_host.data());
knew_buf.ToDevice(knew_host.data());
v_buf.ToDevice(v_host.data());
knew_buf.ToDevice(knew_host.data());
vnew_buf.ToDevice(vnew_host.data());
bias_buf.ToDevice(bias_host.data());
seqstart_q.ToDevice(seqstart_q_host.data());
@@ -1103,7 +1139,9 @@ fwd_result fmha_fwd_run(mode_enum mode,
lse_buf.FromDevice(lse_host.data());
randval_buf.FromDevice(randval_host.data());
constexpr bool supports_squant = std::is_same_v<DataTypeConfig, FmhaFwdFp8>;
constexpr bool supports_squant = std::is_same_v<DataTypeConfig, FmhaFwdFp8> ||
std::is_same_v<DataTypeConfig, FmhaFwdFp8Bf16> ||
std::is_same_v<DataTypeConfig, FmhaFwdFp8Fp32>;
auto p_compute_element_func = [&]() {
if constexpr(supports_squant)
@@ -1113,9 +1151,11 @@ fwd_result fmha_fwd_run(mode_enum mode,
}();
auto oacc_element_func = [&]() {
if constexpr(supports_squant)
if constexpr(std::is_same_v<ODataType, ck_tile::fp8_t> && supports_squant)
return ck_tile::composes(ck_tile::saturates<ck_tile::fp8_t>{},
ck_tile::scales{scale_o});
else if constexpr(supports_squant)
return ck_tile::scales{scale_o};
else
return ck_tile::identity{};
}();

29
example/ck_tile/01_fmha/script/smoke_test_fwd.sh
View File

@@ -94,7 +94,30 @@ run_fp8_tests() {
for b in 1 2 ; do
for hdim in 64 128 256 ; do
run_exe -prec=fp8 -init=3 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=c -squant=1 -kname=$KNAME $COMMON_ARGS
$EXE -prec=fp8 -init=0 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=r -squant=1 -kname=$KNAME $COMMON_ARGS
done ; done ; done ; done
}
run_fp8bf16_tests() {
for perm in 0 1 ; do
for bias in "n" "e" "a" ; do
for b in 1 2 ; do
for hdim in 64 128 256 ; do
$EXE -prec=fp8bf16 -init=0 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=r -squant=1 -kname=$KNAME $COMMON_ARGS
done ; done ; done ; done
}
run_fp8fp32_tests() {
for perm in 0 1 ; do
for bias in "n" "e" "a" ; do
for b in 1 2 ; do
for hdim in 64 128 256 ; do
$EXE -prec=fp8fp32 -init=0 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=r -squant=1 -kname=$KNAME $COMMON_ARGS
done ; done ; done ; done
}
@@ -117,7 +140,9 @@ run_fp16_appendkv_tests() {
set -x
run_fp16_bf16_tests
# run_fp8_tests
run_fp8_tests
run_fp8bf16_tests
run_fp8fp32_tests
if [ $TEST_APPENDKV -eq 1 ] ; then
run_fp16_appendkv_tests