[CK_TILE] Add PagedAttention kernels (#1387)

* Use dictionary to config all the functions

* Add init codegen logic for fmha fwd appendkv

* Call HIP_CHECK_ERROR() macro to get real source info

* Setup meaningfull arguments

* Sync kernel name with the codegen

* Add knew/vnew tensors to the kernel argument

* Fix wrong K values after appending

* Fix vnew append errro

* Extract common logics

* Fix Vnew tile dstr for row major case

* Conditionally add fwd_splitkv API in fmha_fwd example

* Conditionally add call to fmha_fwd_splitkv()

* Remove "EXAMPLE_" prefix of cmake variables

* Regsiter API handlers automatically

* Early return if 0 < s_k_new is not supported

* Show message if we are ignoring option

* Unify CMakeLists.txt coding style

* Set num_splits=1 if split-kv is not supported

* Add length/stride getters for HostTensor

* Add RoPE example utilities

* Add reference_rotary_position_embedding() (not implemented)

* Finish reference_rotary_position_embedding() impl

* Fix typo of HostTensor<>::get_length()

* Fix compilation errors

* Fix wrong answer when interleaved=false

* Fix wrong answer when interleaved=true

* Append K/V in the host verification code

* Simplify K appending logics

* Simplify v_host_ref definition

* Reduce input/output dimensions

* Rename function: add "batched" prefix

* Apply RoPE on host side

* Rename RoPE utility function

* Fix wrong tensor size

* Avoid invoking deprecated method 'find_module'

* Pass RoPE kernel args

* Create Rotary Cos/Sin tile windows in kernel

* Add compute data type alias for RoPE

* Randomly generate seqlen_knew if needed

* Fix seqlen_knew enabling check logic

* Add minimum seqlen_k to generate compliance kvcache

* Fix compilation error in debug mode

* Fix wrong boundaries

* Fix wrong seqlen_k for kvcache

* Rename variables used in distributio encoding

* Fix rotary cos/sin tensor/tile size

* Add constraint to the rotary_dim option

* Remove unused inner namespace

* Add dram distribution for rotary_cos/rotary_sin (interleaved)

* Only apply interleaved RoPE on Knew for now

* Fix wrong thread starting offset

* Instantiate multiple kernels for RoPE approaches

* Clean-up pipeline

* Fix error in RoPE host reference

* Handle RoPE half-rotated logics

* Support 8x rotary_dim under half-rotated RoPE

* Add comment

* Apply elementwise function to the loaded tiles

* Unify parameter/variable naming style

* Remove constness from q_ptr

* Add code blocks for q_tile

* Apply RoPE to q_tile

* Remove debug print code in kernel

* Fix wrong knew/vnew appending positions

* Use better naming for tile indices

* Add make_tile_window() for adding distribution only

* Skip code if # of block is more than needed

* Move thread locating logics into policy

* Remove always true static_assert()

* Rename header

* Rename RotaryEmbeddingEnum

* Extract rotary embedding logic out

* Re-order parameters

* Align naming of some tile size constants

* Rename more tile size constants

* Fix wrong grid size

* Fix wrong shape of knew_host/vnew_host

* Fix wrong index into knew_host/vnew_host

* Fix wrong rotary_cos/rotary_sin memory size for Q

* Extract Q/Knew vector size to helper methods

* Use different rotary_cos/rotary_sin distr for Q/Knew

* Update host/device specifiers

* Fix wrong data type for Q rotary_cos/rotary_sin

* Remove RoPEComputeDataType type alias

* Shift rotary_cos/rotary_sin by cache_seqlen_k

* Add comment for why I just 't' for all padding flags

* Align commit message to the real comment

* Fix wrong pipeline

* Rename utility function

* Disable host verification if API not exist

* Fix wrong rope key for fp8 pipeline

* Allow only apply RoPE on Q (without append KV)

* Add append-kv smoke tests

* Remove debug statements

* Remove more debug statements

* Re-arrange the 'set +x' command

* Remove no-longer used method in pipeline

* Add missing init code

* Refine pipeline padding settings

* Enlarge rotary_dim limit (8 -> 16)

* Enlarge KPerThread for rotary_interleaved=false

* Update rotary_dim range in smoke_test_fwd.sh

* Add template argument 'kIsPagedKV' for splitkv kernels

* Launch splitkv kernel if given page_block_size

* Fix wrong kernel name

* Fix seqlen_k_min for pre-fill case (1 -> 0)

* Add copy_const<> type trait

* Add another make_tile_window()

* Introduce 'TileWindowNavigator' types

* Simplify TileWindowNavigator interfaces

* Fix tile window navigation bugs

* Disable calling fmha_fwd()

* Remove ununnecessary data members

* Simplify more make_tile_window() overloads

* Move V tile through TileWindowNavigator

* Fix uneven split checking logic

* Move code after decide seqlen_q/seqlen_k

* Make sure we always start reading complete tile

* Use 128 as minimus page_block_size

* Fix wrong origin for bias

* Add batch_stride_k/batch_stride_v in group mode

* Unify origin

* Add missing kernel arguments for group mode

* Add paged-kv codegen logic for appendkv kernels

* Add block_table kernel args for appendkv kernel

* Add tile navigators to the appendkv kernel

* Fix wrong tensor descriptor lengths

* Pass re-created tile window to pipeline

* Fix wrong strides for appendkv kernel

* Allow transit tile_window to another page-block

* Handle cross-page-block write

* Donot perform write again if already in last page-block

* Always add fmha_fwd() api

* Add missing group mode argument

* Remove debug macro usages

* Rename option s_k_new to s_knew

* Separate splitkv/non-splitkv args/traits

* Remove fmha_fwd_dispatch()

* Fix compilation errors

* Remove dropout code in splitkv kernel

* Allow problem types without define kHasDropout attr

* Use generic lambda to init traits objects

* Separate more non-splitkv & splitkv traits/args

* Display more info for specific kernels

* Show more detailed warning message

* Rename 'max_num_blocks' to 'max_num_page_blocks'

* Remove no-longer used pipeline files

* Wrap code by #if directives

* Move functors to the begining of validation code

* Use generic lambda to init all the api traits/args

* Fix wrong seqlen for kvcache

* Add missing comment

* Rename TileWindowNavigator to PageBlockNavigator

* Only expose necessary methods (not attributes)

* Re-order pipeline paremeters

* Refine smoke_test_fwd.sh

* Fix wrong arugment count

* Make tile window directly via PageBlockNavigator

* Remove unused template paremeter

* Remove group mode from appendkv kernel

* Fix skcheck logic

* Fix wrong syntax in skcheck expr

* Use meaningful options in smoke test

* Remove options

* Fix formatting

* Fix more format

* Re-organize bash functions

* Pass cache_batch_idx to kernels

* Support cache_batch_idx in example

* Fix compilation error

* Add more appendkv test

* Add more case for appendkv

* Fix unexisted attribute

* Remove 0 < seqlen_knew constraint

* Clarify the case in warning message

* Remove macro checking

* Force batch mode when invoking appendkv & splitkv apis

* Fix mode overriding logics

* Fix wrong parameter name

* Randomize seqlen_k if use kvcache

* Use randomized seqlen_k for kvcache

* Avoid using too small rotary_cos & rotary_sin

* Rename parameter

* Add seqlen_q & seqlen_k rules

* Add comment

* Add more comments

* Fix compilation errors

* Fix typo in comment

* Remove type argument

* Avoid seqlen_k=0 for kvcache

* Revert "Avoid seqlen_k=0 for kvcache"

This reverts commit 21c4df89e416182e8e9bc78e67bd4b98dbb6c88d.

* Fix wrong uneven split checking logics

* Only randomize kvcache seqlen_k if 1 < batch

* Return earlier if split is empty

* Revert "Only randomize kvcache seqlen_k if 1 < batch"

This reverts commit b9a4ab0d7e3c2beecc0fccafd2a13259dd06299c.

* Re-order seqlen_k_start adjustment logics

* Fix compilation errors

* Re-format script

* Find executable from folder automatically

* Fix kvcache seqlen_k generating logic

* Make comment more clear

* Fix wrong knew/vew appending logic on host

* Add s_barrier to sync threads

* Revert "Add s_barrier to sync threads"

This reverts commit d3f550f30c0a4d9df15c613015d5dff268d6746d.

* Support only using 1 row of rotary_cos/rotary_sin

* Rotate Q in different way

* Unify tensor view creation logics

* Fix wrong argument

* Add mask to switch how we use the rotary_cos/sin

* Move attr from traits to problem

* Move has_mask to fmha_fwd_appendkv_args

* Support use uint32_t as SAD operand in Alibi<>

* Use sad_u32() in splitkv kernels

* Store tensor views in PageBlockNavigator

* Use stored tensor view to update tile windows

* Enlarge tensor view size

* Remove debug code

* Fix wrong tensor view size

* Wrap tensor view into PageBlockNavigator

* Add DataType member to PageBlockNavigator

* Remove unnecessary member functions

* Refind macro use

* Fix typo

* Add blank line between directives and actual code

* Re-format files

* Remove type in comment

---------
Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: rocking <ChunYu.Lai@amd.com>

example/ck_tile/01_fmha/CMakeLists.txt

-# generate a list of kernels, but not actually emit files at config stage
+# validate user-specified fmha_fwd API list
+set(FMHA_FWD_KNOWN_APIS "fwd;fwd_splitkv;fwd_appendkv")
+set(FMHA_FWD_ENABLE_APIS "fwd" CACHE STRING
+    "semicolon-separated list of APIs to generate (${FMHA_FWD_KNOWN_APIS}) & link, or \"all\".")
+if(FMHA_FWD_ENABLE_APIS STREQUAL "all")
+  set(FMHA_FWD_ENABLE_APIS ${FMHA_FWD_KNOWN_APIS})
+endif()
+
+foreach(api ${FMHA_FWD_ENABLE_APIS})
+  if(NOT "${api}" IN_LIST FMHA_FWD_KNOWN_APIS)
+    message(FATAL_ERROR "${api} isn't a known api: ${FMHA_FWD_KNOWN_APIS}.")
+  endif()
+endforeach()
+
+# "fwd" is a must-have api for the fmha_fwd example, add it if not specified
+if(NOT "fwd" IN_LIST FMHA_FWD_ENABLE_APIS)
+  list(APPEND FMHA_FWD_ENABLE_APIS "fwd")
+endif()
+
+string(REPLACE ";" "," FMHA_FWD_APIS "${FMHA_FWD_ENABLE_APIS}")
+# generate a list of kernels, but not actually emit files at config sta
 execute_process(
   COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
-  --api fwd,fwd_splitkv --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
+  --api ${FMHA_FWD_APIS} --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
 )
 
 execute_process(
@@ -17,7 +37,7 @@ file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt FMHA_BWD_GEN_BLOBS)
 add_custom_command(
   OUTPUT ${FMHA_FWD_GEN_BLOBS}
   COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
-  --api fwd,fwd_splitkv --output_dir ${CMAKE_CURRENT_BINARY_DIR}
+  --api ${FMHA_FWD_APIS} --output_dir ${CMAKE_CURRENT_BINARY_DIR}
 )
 
 add_custom_command(
@@ -60,6 +80,20 @@ else()
 endif()
 list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -fgpu-flush-denormals-to-zero)
 
+# conditionally enable call to the fwd_splitkv API in fmha_fwd example
+if("fwd_splitkv" IN_LIST FMHA_FWD_ENABLE_APIS)
+  list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_SPLITKV_API=1)
+else()
+  list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_SPLITKV_API=0)
+endif()
+
+# conditionally enable call to the fwd_appendkv API in fmha_fwd example
+if("fwd_appendkv" IN_LIST FMHA_FWD_ENABLE_APIS)
+  list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_APPENDKV_API=1)
+else()
+  list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -DCK_TILE_FMHA_FWD_APPENDKV_API=0)
+endif()
+
 # Allow comparing floating points directly in order to check sentinel values
 list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-float-equal)
 list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-float-equal)

example/ck_tile/01_fmha/codegen/cpp_symbol_map.py

@@ -82,6 +82,18 @@ DROPOUT_CHECK_MAP = {
     "dropout_wg16_storerandval" : "t.has_dropout == true && t.is_store_randval == true",
 }
 
+ROPE_MAP = {
+    "no" : "ck_tile::RotaryEmbeddingEnum::NONE",
+    "inter"  : "ck_tile::RotaryEmbeddingEnum::INTERLEAVED",
+    "half" : "ck_tile::RotaryEmbeddingEnum::HALF_ROTATED"
+}
+
+ROPE_CHECK_MAP = {
+    "no"    : "rope_enum::none",
+    "inter" : "rope_enum::interleaved",
+    "half"  : "rope_enum::half_rotated"
+}
+
 MODE_MAP = {
     "batch" : "false",
     "group" : "true"
@@ -105,4 +117,4 @@ PIPELINE_ENUM_MAP = {
 BOOL_MAP = {
     "t" : "true",
     "f" : "false"
-}
+}
\ No newline at end of file

example/ck_tile/01_fmha/codegen/ops/fmha_fwd_appendkv.py

+# SPDX-License-Identifier: MIT
+# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+# generate kernel instances to speed up compilation
+
+import copy
+from dataclasses import dataclass
+import fnmatch
+import itertools
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+from codegen.cmake_config import *
+from codegen.cpp_symbol_map import *
+
+from codegen.ops.fmha_fwd import (
+    FmhaFwdApiTrait,
+    DTYPE_BITS,
+    FMHA_FWD_KERNEL_HEADER,
+    FMHA_FWD_API_PER_DTYPE,
+    FMHA_FWD_API_PER_HDIM_CASE,
+)
+
+
+FMHA_FWD_APPENDKV_KERNEL_BODY="""
+using fmha_dtype_{F_idx} = {F_dtype};
+
+using fmha_trait_{F_idx} = ck_tile::TileFmhaFwdAppendKVTraits<{F_spad},
+                                                    {F_skpad},
+                                                    {F_dpad},
+                                                    {F_dvpad},
+                                                    {F_occupancy}>;
+
+using fmha_pipeline_problem_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipelineProblem<
+    typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
+    typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::KDataType,
+    typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::VDataType,
+    {F_bs},
+    {F_bsk},
+    {F_bd},
+    {F_bdv},
+    {F_vlayout},
+    {F_rope},
+    {F_pagedkv},
+    fmha_trait_{F_idx}>;
+
+using fmha_pipeline_{F_idx} = ck_tile::BlockFmhaFwdAppendKVPipeline<
+    fmha_pipeline_problem_{F_idx}>;
+
+using fmha_kernel_{F_idx} =
+    ck_tile::FmhaFwdAppendKVKernel<ck_tile::FmhaFwdAppendKVTilePartitioner<{F_bs}, {F_bsk}, {F_bd}, {F_bdv}>,
+                  fmha_pipeline_{F_idx}>;
+
+using trait_{F_idx} = fmha_fwd_appendkv_traits_<{F_hdim}, {F_dtype}, {F_bs}, {F_bsk}, {F_bd}, {F_bdv}, {F_vlayout},
+                        {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_rope}, {F_pagedkv}>;
+
+#include <iostream>
+
+template<>
+float fmha_fwd_appendkv_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_appendkv_args a)
+{{
+    using k_ = fmha_kernel_{F_idx};
+    if(s.log_level_ > 0)
+        std::cout << ", " << k_::GetName() << std::flush;
+    auto [kargs, grids] = fmha_fwd_appendkv_create_kargs_and_grids<k_>(a);
+    constexpr dim3 blocks             = k_::BlockSize();
+    constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
+    return ck_tile::launch_kernel(s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs));
+}}
+"""
+
+FMHA_FWD_APPENDKV_API_FILENAME="fmha_fwd_appendkv_api.cpp"
+FMHA_FWD_APPENDKV_API="""
+float fmha_fwd_appendkv(fmha_fwd_appendkv_traits t, fmha_fwd_appendkv_args a, const ck_tile::stream_config& s){{
+    float r = -1;
+{F_dispatch}
+    return r;
+}}
+"""
+
+FMHA_FWD_APPENDKV_API_INNER_DISPATCH="""            {F_if}((t.is_v_rowmajor == {F_vlayout}) &&
+                        ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck}) && (t.rope_type == {F_rope_check}) &&
+                        ((a.block_table_ptr != nullptr) == {F_pagedkv})) {{
+                using trait_ = fmha_fwd_appendkv_traits_<{F_hdim}, {F_dtype}, {F_bs}, {F_bsk}, {F_bd}, {F_bdv}, {F_vlayout}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}, {F_rope}, {F_pagedkv}>;
+                return fmha_fwd_appendkv_<trait_>(s, a);
+            }}
+"""
+
+@dataclass
+class FmhaFwdAppendKVApiTrait:
+    # sync with fmha_fwd_traits<>, to generate fallback calls
+    hdim      : str
+    dtype     : str  # data type
+    bs        : int  # tile size along q seqlen
+    bsk       : int  # tile size along k seqlen
+    bd        : int  # tile size along qk gemm unroll
+    bdv       : int  # tile size along kv gemm unroll
+    vlayout   : str
+    spad      : str
+    skpad     : str
+    dpad      : str
+    dvpad     : str
+    rope      : str # key from ROPE_MAP
+    pagedkv   : str
+
+    @property
+    def name(self) -> str:
+        return f'{self.hdim}-{self.dtype}-{self.bs}-{self.bsk}-{self.bd}-{self.bdv}-{self.vlayout}-'+\
+               f'{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}-{self.rope}-{self.pagedkv}'
+
+    @property
+    def scheck(self) -> str:
+        if self.spad == 't' : return f'true /*a.seqlen_q % {self.bs} != 0*/'
+        else :                return f'a.seqlen_q % {self.bs} == 0'
+
+    @property
+    def skcheck(self) -> str:
+        # we do not check all the values in a.seqlen_k_ptr
+        return 'true'
+
+    @property
+    def dcheck(self) -> str:
+        if self.dpad == 't': return f'true /*a.hdim_q % {self.bd} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
+        else :               return f'a.hdim_q % {self.bd} == 0'
+
+    @property
+    def dvcheck(self) -> str:
+        if self.dvpad == 't': return f'true /*a.hdim_v % {self.bdv} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
+        else :                return f'a.hdim_v % {self.bdv} == 0'
+
+@dataclass
+class FmhaFwdAppendKVPipeline:
+    F_vlayout   : str  # row/col
+    F_spad      : str  # true/false
+    F_skpad     : str  #
+    F_dpad      : str  #
+    F_dvpad     : str  #
+    F_rope      : str  # key from ROPE_MAP
+    F_pagedkv   : str  # t/f
+
+    @property
+    def name(self) -> str:
+        def pad_name() -> str:
+            n = ''
+            if self.F_spad == 't': n += 's'
+            if self.F_skpad == 't' : n += 'sk'
+            if self.F_dpad == 't' : n += 'd'
+            if self.F_dvpad == 't' : n += 'dv'
+            if n != '' : n = 'p' + n
+            return n
+        pn = pad_name()
+        n = f'v{self.F_vlayout[0]}'
+        if pn != '' : n += f'_{pn}'
+        if self.F_rope != 'no': n += f'_{self.F_rope}'
+        if self.F_pagedkv == 't': n += '_pagedkv'
+        return n
+
+class FmhaFwdAppendKVApiPool:
+    def __init__(self, mask_impl):
+        self.pool = dict()
+        self.mask_impl = mask_impl
+
+    def register_traits(self, trait : FmhaFwdApiTrait) -> None:
+        # TODO: do we need to check duplication?
+        if trait.dtype not in self.pool.keys():
+            self.pool[trait.dtype] = dict()
+        if trait.hdim not in self.pool[trait.dtype].keys():
+            self.pool[trait.dtype][trait.hdim] = list()
+
+        self.pool[trait.dtype][trait.hdim].append(copy.copy(trait))
+
+    @property
+    def api(self) -> str:
+        per_dtypes=str()
+        for i, dtype in enumerate(self.pool.keys()):
+            per_hdim_case=str()
+            for j, hdim in enumerate(self.pool[dtype].keys()):
+                traits=self.pool[dtype][hdim]
+                inners=str()
+                for k, trait in enumerate(traits):
+                    if_k = 'if' if k == 0 else 'else if'
+                    inners = inners + FMHA_FWD_APPENDKV_API_INNER_DISPATCH.format(F_if=if_k, F_vlayout=LAYOUT_MAP[trait.vlayout],
+                                   F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_rope_check=ROPE_CHECK_MAP[trait.rope],
+                                   F_pagedkv=BOOL_MAP[trait.pagedkv], F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
+                                   F_rope=ROPE_MAP[trait.rope], F_bs=trait.bs, F_bsk=trait.bsk, F_bd=trait.bd, F_bdv=trait.bdv, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
+                if_j = 'if' if j == 0 else 'else if'
+                per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
+            if_i = 'if' if i == 0 else 'else if'
+            per_dtypes = per_dtypes + FMHA_FWD_API_PER_DTYPE.format(F_if=if_i, F_dtype=dtype, F_hdim_case=per_hdim_case)
+        return FMHA_FWD_KERNEL_HEADER + FMHA_FWD_APPENDKV_API.format(F_dispatch = per_dtypes)
+
+@dataclass
+class FmhaFwdAppendKVTileSize:
+    F_bs        : int  # tile size along q seqlen
+    F_bsk       : int  # tile size along k seqlen
+    F_bd        : int  # tile size along qk gemm unroll
+    F_bdv       : int  # tile size along kv gemm unroll
+    F_occupancy : int  # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy
+    @property
+    def name(self) -> str:
+        return f"b{self.F_bs}x{self.F_bsk}x{self.F_bd}x{self.F_bdv}" +\
+            ("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
+
+@dataclass
+class FmhaFwdAppendKVKernel:
+    F_idx           : int  # this is not a tunable, but a counter to differentiate symbol
+    F_hdim          : int  # hdim
+    F_dtype         : str  # data type
+    F_tile          : FmhaFwdAppendKVTileSize
+    F_pipeline      : FmhaFwdAppendKVPipeline
+    mask_impl       : str
+
+    @property
+    def template(self) -> str:
+        kernel_body = str()
+        return FMHA_FWD_KERNEL_HEADER + \
+            FMHA_FWD_APPENDKV_KERNEL_BODY.format(
+                F_idx           = self.F_idx,
+                F_hdim          = self.F_hdim,
+                F_dtype         = DTYPE_MAP[self.F_dtype],
+                F_bs            = self.F_tile.F_bs,
+                F_bsk           = self.F_tile.F_bsk,
+                F_bd            = self.F_tile.F_bd,
+                F_bdv           = self.F_tile.F_bdv,
+                F_vlayout       = LAYOUT_MAP[self.F_pipeline.F_vlayout],
+                F_spad          = BOOL_MAP[self.F_pipeline.F_spad],
+                F_skpad         = BOOL_MAP[self.F_pipeline.F_skpad],
+                F_dpad          = BOOL_MAP[self.F_pipeline.F_dpad],
+                F_dvpad         = BOOL_MAP[self.F_pipeline.F_dvpad],
+                F_rope          = ROPE_MAP[self.F_pipeline.F_rope],
+                F_pagedkv       = BOOL_MAP[self.F_pipeline.F_pagedkv],
+                F_occupancy     = self.F_tile.F_occupancy)
+
+    @property
+    def name(self) -> str:
+        # TODO: we don't encode idx here
+        return f"fmha_fwd_appendkv_d{self.F_hdim}_{self.F_dtype}_" + \
+                self.F_tile.name + '_' + self.F_pipeline.name
+
+    @property
+    def filename(self) -> str:
+        return self.name + ".cpp"
+
+    def api_trait(self) -> FmhaFwdAppendKVApiTrait:
+        return FmhaFwdAppendKVApiTrait(
+                hdim=str(self.F_hdim),
+                dtype=self.F_dtype,
+                bs=self.F_tile.F_bs,
+                bsk=self.F_tile.F_bsk,
+                bd=self.F_tile.F_bd,
+                bdv=self.F_tile.F_bdv,
+                vlayout=self.F_pipeline.F_vlayout,
+                spad=self.F_pipeline.F_spad,
+                skpad=self.F_pipeline.F_skpad,
+                dpad=self.F_pipeline.F_dpad,
+                dvpad=self.F_pipeline.F_dvpad,
+                rope=self.F_pipeline.F_rope,
+                pagedkv=self.F_pipeline.F_pagedkv)
+
+# TODO: design a more practical way to do it
+# this is current supported tile size per hdim
+def get_fmha_fwd_appendkv_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
+    if dtype == 'fp16' or dtype == 'bf16':
+        return {
+            '32'  : FmhaFwdAppendKVTileSize(64, 64,  32,  32, -1),
+            '64'  : FmhaFwdAppendKVTileSize(64, 64,  64,  64, -1),
+            '128' : FmhaFwdAppendKVTileSize(64, 64, 128, 128, -1),
+            '256' : FmhaFwdAppendKVTileSize(64, 64, 256, 256, -1),
+        }
+    elif dtype == 'fp8' or dtype == 'bf8':
+        return {
+            '64'  : FmhaFwdAppendKVTileSize(64, 64,  64,  64, -1),
+            '128' : FmhaFwdAppendKVTileSize(64, 64, 128, 128, -1),
+            '256' : FmhaFwdAppendKVTileSize(64, 64, 256, 256, -1)
+        }
+    else:
+        return None
+
+def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdAppendKVApiPool, List[FmhaFwdAppendKVKernel]]:
+    # TODO: we don't support tuning yet, so pick up one value for vlayout/pipeline/pad
+    #       support this in future
+    def get_pipelines(dtype, hdim) -> List[FmhaFwdAppendKVPipeline]:
+        # this function will populate a list possible pipelines
+        # 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']:
+            # NOTICE: it will be very complicated if we consider all the hdim_q padding cases while
+            #         applying rotary embedding, so I just use 't' in inter/half pipelines
+            for vlayout in ['row', 'col']:
+                for pagedkv in ["t", "f"]:
+                    pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 'f', 't', 'f', 'f', 'no', pagedkv))
+                    pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 't', 't', 't', 't', 'no', pagedkv))
+
+                    pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 'f', 't', 't', 'f', 'inter', pagedkv))
+                    pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 't', 't', 't', 't', 'inter', pagedkv))
+
+                    pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 'f', 't', 't', 'f', 'half', pagedkv))
+                    pipelines.append(FmhaFwdAppendKVPipeline(vlayout, 't', 't', 't', 't', 'half', pagedkv))
+        elif dtype in ['fp8', 'bf8']:
+            # rope/paged-kv is not supported
+            pipelines.append(FmhaFwdAppendKVPipeline('col', 't', 't', 't', 't', 'no', 'f'))
+        else:
+            assert False
+        return pipelines
+
+    gen = list()
+    api_pool = FmhaFwdAppendKVApiPool(mask_impl)
+
+    for dtype in DTYPE_MAP.keys():
+        d = get_fmha_fwd_appendkv_tile_dict_from_dtype(dtype)
+        if d == None:
+            continue
+        for hdim_str in d.keys():
+            tile = d[hdim_str]
+            hdim = int(hdim_str)
+            for pipeline in get_pipelines(dtype, hdim):
+                k = FmhaFwdAppendKVKernel(F_idx=0,
+                                  F_hdim=hdim,
+                                  F_dtype=dtype,
+                                  F_tile=tile,
+                                  F_pipeline=pipeline,
+                                  mask_impl=mask_impl)
+                if kernel_filter != None:
+                    if not fnmatch.fnmatch(k.name, kernel_filter):
+                        continue
+                if receipt == 2:
+                    cond = dtype in ['fp16', 'bf16']
+                    cond &= pipeline.F_vlayout == 'row'
+                    if not cond:
+                        continue
+                api_pool.register_traits(k.api_trait())
+                gen.append(k)
+
+    return (api_pool, gen)
+
+def write_single_kernel(kernel: FmhaFwdAppendKVKernel, autogen_dir: Path) -> None:
+    (autogen_dir / kernel.filename).write_text(kernel.template)
+
+def write_fwd_appendkv_api(api_pool : FmhaFwdAppendKVApiPool, autogen_dir: Path) -> None:
+    (autogen_dir / FMHA_FWD_APPENDKV_API_FILENAME).write_text(api_pool.api)
+
+def write_blobs(output_dir : Path, kernel_filter : Optional[str], receipt, mask_impl) -> None:
+    api_pool, kernels = get_fwd_appendkv_blobs(kernel_filter, receipt, mask_impl)
+    for kernel in kernels:
+        write_single_kernel(kernel, output_dir)
+    write_fwd_appendkv_api(api_pool, output_dir)
+
+def list_blobs(file_path : Path, kernel_filter : Optional[str], receipt, mask_impl) -> None:
+    with file_path.open('a') as f:
+        _, kernels = get_fwd_appendkv_blobs(kernel_filter, receipt, mask_impl)
+        for kernel in kernels:
+            f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
+        f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_APPENDKV_API_FILENAME) + "\n")
\ No newline at end of file

example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py

@@ -21,6 +21,14 @@ from codegen.ops.fmha_fwd import (
 )
 
 
+DTYPE_BITS = {
+    "fp32": 32,
+    "fp16": 16,
+    "bf16": 16,
+    "fp8" : 8,
+    "bf8" : 8
+}
+
 FMHA_FWD_SPLITKV_PIPELINE_MAP = {
     "qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS",
     "qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync",
@@ -51,8 +59,8 @@ using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
                                                      {F_bias},
                                                      false,
                                                      {F_lse},
-                                                     {F_dropout},
                                                      {F_squant},
+                                                     {F_pagedkv},
                                                      kHasUnevenSplits,
                                                      {F_occupancy}>;
 
@@ -63,7 +71,6 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaFwdSplitKVPipelineProblem<
     typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::SaccDataType,
     typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::SMPLComputeDataType,
     typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::BiasDataType,
-    typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::RandValOutputDataType,
     typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::LSEDataType,
     typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::PDataType,
     typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
@@ -86,7 +93,7 @@ using fmha_kernel =
                   fmha_pipeline,
                   fmha_epilogue>;
 
-static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
+static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
 {{
     using k_ = fmha_kernel;
     auto [kargs, grids] = fmha_fwd_splitkv_create_kargs_and_grids<k_>(a);
@@ -97,16 +104,21 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
 }};
 }}
 
-using trait_{F_idx} = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout},
-                        {F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
+using trait_{F_idx} = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout},
+                        {F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, 
+                        {F_dvpad}>;
 
 #include <iostream>
 
 template<>
-void fmha_fwd_splitkv_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_args a)
+void fmha_fwd_splitkv_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
 {{
     if constexpr({F_mode} == false) {{ // batch mode
-        if (a.seqlen_k % (a.num_splits * {F_bn0}) == 0) {{
+        // we don't check every seqlen_k values for kvcache
+        if (a.seqlen_k_ptr != nullptr) {{
+            kernel_runner<true>::run(s, a);
+        // make sure F_bn0 is divisible by F_bk1
+        }} else if (a.seqlen_k % (a.num_splits * {F_bn0}) == 0) {{
             kernel_runner<false>::run(s, a);
         }} else {{
             kernel_runner<true>::run(s, a);
@@ -160,7 +172,7 @@ using fmha_kernel =
                   fmha_pipeline,
                   fmha_epilogue>;
 
-static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
+static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
 {{
     using k_ = fmha_kernel;
     auto [kargs, grids] = fmha_fwd_splitkv_combine_create_kargs_and_grids<k_>(a);
@@ -177,7 +189,7 @@ using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_m
 #include <iostream>
 
 template<>
-void fmha_fwd_splitkv_combine_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_args a)
+void fmha_fwd_splitkv_combine_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
 {{
     if (a.num_splits <= 16) {{
         kernel_runner<4>::run(s, a);
@@ -203,7 +215,7 @@ FMHA_FWD_SPLITKV_API="""
 #include <iostream>
 
 template<typename fmha_fwd_splitkv_traits_, typename fmha_fwd_splitkv_combine_traits_>
-float fmha_fwd_splitkv_(const ck_tile::stream_config& s, fmha_fwd_args a)
+float fmha_fwd_splitkv_(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
 {{
     if(s.log_level_ > 0)
     std::cout
@@ -217,22 +229,96 @@ float fmha_fwd_splitkv_(const ck_tile::stream_config& s, fmha_fwd_args a)
     );
 }}
 
-float fmha_fwd_splitkv(fmha_fwd_traits t, fmha_fwd_args a, const ck_tile::stream_config& s){{
+float fmha_fwd_splitkv(fmha_fwd_splitkv_traits t, fmha_fwd_splitkv_args a, const ck_tile::stream_config& s){{
     float r = -1;
 {F_dispatch}
     return r;
 }}
 """
 
-FMHA_FWD_SPLITKV_API_INNER_DISPATCH="""            {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse})  && (t.has_dropout == {F_dropout}) && (t.do_fp8_static_quant == {F_squant}) &&
-                        ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
-                using traits_ = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
+FMHA_FWD_SPLITKV_API_INNER_DISPATCH="""            {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.do_fp8_static_quant == {F_squant}) &&
+                        ((a.block_table_ptr != nullptr) == {F_pagedkv}) && ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
+                using traits_ = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
                 using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}, {F_lse}, {F_squant}, {F_spad}, {F_dvpad}>;
 
                 return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
             }}
 """
 
+@dataclass
+class FmhaFwdSplitKVApiTrait:
+    pipeline_tag : str
+    # sync with fmha_fwd_traits<>, to generate fallback calls
+    hdim      : str
+    dtype     : str  # data type
+    mode      : str  # value from MODE_MAP
+    bm0       : int  # tile size along q seqlen (block size)
+    bn0       : int  # tile size along qk seqlen
+    bk0       : int  # tile size along qk gemm unroll
+    bn1       : int  # tile size along v head_dim
+    bk1       : int  # tile size along kv gemm unroll
+    bk0blen   : int
+    vlayout   : str
+    mask      : str
+    bias      : str  #
+    lse       : str  #
+    squant    : str  #
+    spad      : str
+    skpad     : str
+    dpad      : str
+    dvpad     : str
+    pagedkv : str
+
+    @property
+    def name(self) -> str:
+        return f'{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0blen}-'+\
+                    f'{self.vlayout}-{self.mask}-{self.bias}-{self.lse}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-'+\
+                    f'{self.dvpad}-{self.pagedkv}'
+
+    @property
+    def scheck(self) -> str:
+        if self.mode == 'group': return 'true/*group mode spad always true*/'                  # group mode only generate spad/skpad == true
+        if self.pipeline_tag == 'qr_async':
+            if self.spad == 't' : return 'true' # always support
+            else :                return 'true'
+        elif self.pipeline_tag in ['qr']:
+            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 skcheck(self) -> str:
+        if self.mode == 'group': return 'true/*group mode skpad always true*/'                  # group mode only generate spad/skpad == true
+        if self.pipeline_tag == 'qr_async':
+            if self.skpad == 't' : return f'a.seqlen_k == 0 || a.seqlen_k % {self.bn0} != 0'
+            else :                 return f'a.seqlen_k != 0 && a.seqlen_k % {self.bn0} == 0'
+        elif self.pipeline_tag in ['qr', 'qr_fp8']:
+            if self.skpad == 't' : return f'true /*a.seqlen_k % {self.bn0} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
+            else :                return f'a.seqlen_k % {self.bn0} == 0'
+        else: assert False
+
+    @property
+    def dcheck(self) -> str:
+        if self.pipeline_tag == 'qr_async':
+            vec = int((32 * 4) / DTYPE_BITS[self.dtype])
+            if self.dpad == 't': return f'a.hdim_q % {vec} == 0'
+            else :               assert False
+        elif self.pipeline_tag in ['qr']:
+            if self.dpad == 't': return f'true /*a.hdim_q % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
+            else :               return f'a.hdim_q % {self.bk0blen} == 0'
+        else:   assert False
+
+    @property
+    def dvcheck(self) -> str:
+        if self.pipeline_tag == 'qr_async':
+            vec = int((32 * 4) / DTYPE_BITS[self.dtype])
+            if self.dvpad == 't': return f'a.hdim_v % {vec} == 0'
+            else :                assert False
+        elif self.pipeline_tag in ['qr']:
+            if self.dvpad == 't': return f'true /*a.hdim_v % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
+            else :                return f'a.hdim_v % {self.bk0blen} == 0'
+        else:   assert False
+
 @dataclass
 class FmhaFwdSplitKVPipeline:
     tag : str
@@ -244,8 +330,8 @@ class FmhaFwdSplitKVPipeline:
     F_dvpad     : str  #
     F_bias      : str  # true/false
     F_lse       : str  #
-    F_dropout   : str  #
     F_squant    : str  #
+    F_pagedkv   : str  # t/f
     F_mask      : str  # value from MASK_MAP
 
     @property
@@ -267,8 +353,8 @@ class FmhaFwdSplitKVPipeline:
         else:
             if self.F_mask != 'no' : n += f'_m{self.F_mask[0]}'
         if self.F_lse == 't' : n += '_lse'
-        if self.F_dropout == 't' : n += '_dropout'
         if self.F_squant == 't' : n += '_squant'
+        if self.F_pagedkv == 't' : n += '_pagedkv'
         return n
 
 @dataclass
@@ -300,7 +386,7 @@ class FmhaFwdSplitKVApiPool:
         self.pool = dict()
         self.mask_impl = mask_impl
 
-    def register_traits(self, trait : FmhaFwdApiTrait) -> None:
+    def register_traits(self, trait : FmhaFwdSplitKVApiTrait) -> None:
         # TODO: do we need to check duplication?
         if trait.dtype not in self.pool.keys():
             self.pool[trait.dtype] = dict()
@@ -322,8 +408,8 @@ class FmhaFwdSplitKVApiPool:
                     inners = inners + FMHA_FWD_SPLITKV_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_vlayout=LAYOUT_MAP[trait.vlayout],
                                    F_pipeline_enum=PIPELINE_ENUM_MAP[trait.pipeline_tag], F_mask=get_mask_map(self.mask_impl)[trait.mask],
                                    F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_bias_check=BIAS_CHECK_MAP[trait.bias], F_bias=BIAS_MAP[trait.bias],
-                                   F_lse=BOOL_MAP[trait.lse], F_dropout=BOOL_MAP[trait.dropout] ,
-                                   F_squant=BOOL_MAP[trait.squant], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
+                                   F_lse=BOOL_MAP[trait.lse], F_squant=BOOL_MAP[trait.squant], F_pagedkv=BOOL_MAP[trait.pagedkv], 
+                                   F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
                                    F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
                                    F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0blen=trait.bk0blen,
                                    F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
@@ -383,8 +469,8 @@ class FmhaFwdSplitKVKernel:
                 F_dvpad         = BOOL_MAP[self.F_pipeline.F_dvpad],    
                 F_bias          = BIAS_MAP[self.F_pipeline.F_bias],
                 F_lse           = BOOL_MAP[self.F_pipeline.F_lse],
-                F_dropout       = BOOL_MAP[self.F_pipeline.F_dropout],
                 F_squant        = BOOL_MAP[self.F_pipeline.F_squant],
+                F_pagedkv       = BOOL_MAP[self.F_pipeline.F_pagedkv],
                 F_occupancy     = self.F_tile.F_occupancy,
                 F_pipeline_enum = PIPELINE_ENUM_MAP[self.F_pipeline.tag],
                 F_mask          = get_mask_map(self.mask_impl)[self.F_pipeline.F_mask],
@@ -401,8 +487,8 @@ class FmhaFwdSplitKVKernel:
     def filename(self) -> str:
         return self.name + ".cpp"
 
-    def api_trait(self) -> FmhaFwdApiTrait:
-        return FmhaFwdApiTrait(
+    def api_trait(self) -> FmhaFwdSplitKVApiTrait:
+        return FmhaFwdSplitKVApiTrait(
                 pipeline_tag=self.F_pipeline.tag,
                 hdim=str(self.F_hdim),
                 dtype=self.F_dtype,
@@ -417,8 +503,8 @@ class FmhaFwdSplitKVKernel:
                 mask=self.F_pipeline.F_mask,
                 bias=self.F_pipeline.F_bias,
                 lse=self.F_pipeline.F_lse,
-                dropout=self.F_pipeline.F_dropout,
                 squant=self.F_pipeline.F_squant,
+                pagedkv=self.F_pipeline.F_pagedkv,
                 spad=self.F_pipeline.F_spad,
                 skpad=self.F_pipeline.F_skpad,
                 dpad=self.F_pipeline.F_dpad,
@@ -460,29 +546,6 @@ class FmhaFwdSplitKVCombineKernel:
     def filename(self) -> str:
         return self.name + ".cpp"
 
-    def api_trait(self) -> FmhaFwdApiTrait:
-        return FmhaFwdApiTrait(
-                pipeline_tag=self.F_pipeline.tag,
-                hdim=str(self.F_hdim),
-                dtype=self.F_dtype,
-                mode=self.F_mode,
-                bm0=self.F_tile.F_bm0,
-                bn0=self.F_tile.F_bn0,
-                bk0=self.F_tile.F_bk0,
-                bn1=self.F_tile.F_bn1,
-                bk1=self.F_tile.F_bk1,
-                bk0blen=self.F_tile.F_bk0blen,
-                vlayout=self.F_pipeline.F_vlayout,
-                mask=self.F_pipeline.F_mask,
-                bias=self.F_pipeline.F_bias,
-                lse=self.F_pipeline.F_lse,
-                dropout=self.F_pipeline.F_dropout,
-                squant=self.F_pipeline.F_squant,
-                spad=self.F_pipeline.F_spad,
-                skpad=self.F_pipeline.F_skpad,
-                dpad=self.F_pipeline.F_dpad,
-                dvpad=self.F_pipeline.F_dvpad)
-
 # TODO: design a more practical way to do it
 # this is current supported tile size per hdim
 def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
@@ -533,27 +596,27 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
         squant = 't' if dtype == 'fp8' else 'f'
         pipelines = []
         if dtype in ['fp16', 'bf16']:
-            # splitkv kernel donot support dropout
-            for mask, bias, lse, dropout in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["f"]):
-                if hdim == 256:
+            for mask, bias, lse, pagedkv in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"]):
+                # TODO: use async pipeline when compiler is more stable 
+                if hdim == 256 or hdim in [32, 64, 128]:
                 # if True:
-                    pipelines.append(Pipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
-                    pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
+                    pipelines.append(Pipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, squant, pagedkv, mask))
+                    pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, squant, pagedkv, mask))
 
-                    pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
-                    pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
+                    pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
+                    pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
                 else:
-                    pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
-                    pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
-                    pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
-                    pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
+                    pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask))
+                    pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
+                    pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask))
+                    pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
                     if receipt == 1:
-                        pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
-                        pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
+                        pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim
+                        pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim
         elif dtype in ['fp8', 'bf8']:
-            # no need lse/dropout kernels
+            # no need lse/paged-kv kernels
             for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
-                pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', 'f', squant, mask))
+                pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', squant, 'f', mask))
         else:
             assert False
         return pipelines
@@ -574,6 +637,9 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
                     if pipeline.F_spad != 't' or pipeline.F_skpad != 't':
                         # in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not
                         continue
+                    if pipeline.F_pagedkv == 't':
+                        # we only use batch mode kernels to handle (paged-) kvcache problems
+                        continue
                 k = Kernel(F_idx=0,
                            F_hdim=hdim,
                            F_dtype=dtype,

example/ck_tile/01_fmha/fmha_fwd.hpp

@@ -5,10 +5,13 @@
 
 #include "ck_tile/core.hpp"
 #include "ck_tile/host/kernel_launch.hpp"
-#include "ck_tile/ops/fmha.hpp"
 #include "ck_tile/ops/epilogue.hpp"
-#include "mask.hpp"
+#include "ck_tile/ops/fmha.hpp"
+
 #include "bias.hpp"
+#include "mask.hpp"
+#include "rotary.hpp"
+
 #include <type_traits>
 
 template <typename DataType>
@@ -93,13 +96,86 @@ struct fmha_fwd_args
     const void* v_ptr;
     const void* bias_ptr; // bias or alibi_slope pointer
     void* rand_val_ptr;
+    void* lse_ptr;
+    void* o_ptr;
+
+    const void* seqstart_q_ptr;
+    const void* seqstart_k_ptr;
+    const void*
+        seqlen_k_ptr; // only used if both 'seqstart_q_ptr' & 'seqstart_k_ptr' are not nullptr
+
+    ck_tile::index_t seqlen_q;
+    ck_tile::index_t seqlen_k;
+    ck_tile::index_t batch;
+    ck_tile::index_t max_seqlen_q;
+    ck_tile::index_t hdim_q;
+    ck_tile::index_t hdim_v;
+    ck_tile::index_t nhead_q;
+    ck_tile::index_t nhead_k;
+
+    float scale_s;
+    float scale_p;
+    float scale_o;
+
+    ck_tile::index_t stride_q;
+    ck_tile::index_t stride_k;
+    ck_tile::index_t stride_v;
+    ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
+    ck_tile::index_t stride_randval;
+    ck_tile::index_t stride_o;
+    ck_tile::index_t nhead_stride_q;
+    ck_tile::index_t nhead_stride_k;
+    ck_tile::index_t nhead_stride_v;
+    ck_tile::index_t nhead_stride_bias;
+    ck_tile::index_t nhead_stride_randval;
+    ck_tile::index_t nhead_stride_lse;
+    ck_tile::index_t nhead_stride_o;
+    ck_tile::index_t batch_stride_q;
+    ck_tile::index_t batch_stride_k;
+    ck_tile::index_t batch_stride_v;
+    ck_tile::index_t batch_stride_bias;
+    ck_tile::index_t batch_stride_randval;
+    ck_tile::index_t batch_stride_lse;
+    ck_tile::index_t batch_stride_o;
+
+    ck_tile::index_t window_size_left;
+    ck_tile::index_t window_size_right;
+    ck_tile::index_t mask_type;
+
+    float p_drop;
+    bool s_randval;
+    std::tuple<uint64_t, uint64_t> drop_seed_offset;
+};
+
+struct fmha_fwd_splitkv_args
+{
+    const void* q_ptr;
+    const void* k_ptr;
+    const void* v_ptr;
+    const void* bias_ptr; // bias or alibi_slope pointer
     void* lse_acc_ptr;
     void* o_acc_ptr;
     void* lse_ptr;
     void* o_ptr;
+
+    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
+
+    const void* cache_batch_idx;
+
+    // the real seqlen_q & seqlen_k are decided by following:
+    // batch mode: seqlen_q = kargs.seqlen_q
+    //             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):
+    //             seqlen_q = kargs.seqlen_q
+    //             seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
     const void* seqstart_q_ptr;
     const void* seqstart_k_ptr;
     const void* seqlen_k_ptr;
+
     ck_tile::index_t seqlen_q;
     ck_tile::index_t seqlen_k;
     ck_tile::index_t batch;
@@ -109,21 +185,21 @@ struct fmha_fwd_args
     ck_tile::index_t nhead_q;
     ck_tile::index_t nhead_k;
     ck_tile::index_t num_splits;
+
     float scale_s;
     float scale_p;
     float scale_o;
+
     ck_tile::index_t stride_q;
     ck_tile::index_t stride_k;
     ck_tile::index_t stride_v;
     ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
-    ck_tile::index_t stride_randval;
     ck_tile::index_t stride_o_acc;
     ck_tile::index_t stride_o;
     ck_tile::index_t nhead_stride_q;
     ck_tile::index_t nhead_stride_k;
     ck_tile::index_t nhead_stride_v;
     ck_tile::index_t nhead_stride_bias;
-    ck_tile::index_t nhead_stride_randval;
     ck_tile::index_t nhead_stride_lse;
     ck_tile::index_t nhead_stride_lse_acc;
     ck_tile::index_t nhead_stride_o_acc;
@@ -132,19 +208,62 @@ struct fmha_fwd_args
     ck_tile::index_t batch_stride_k;
     ck_tile::index_t batch_stride_v;
     ck_tile::index_t batch_stride_bias;
-    ck_tile::index_t batch_stride_randval;
     ck_tile::index_t batch_stride_lse;
     ck_tile::index_t batch_stride_lse_acc;
     ck_tile::index_t batch_stride_o_acc;
     ck_tile::index_t batch_stride_o;
     ck_tile::index_t split_stride_lse_acc;
     ck_tile::index_t split_stride_o_acc;
+
     ck_tile::index_t window_size_left;
     ck_tile::index_t window_size_right;
     ck_tile::index_t mask_type;
-    float p_drop;
-    bool s_randval;
-    std::tuple<uint64_t, uint64_t> drop_seed_offset;
+};
+
+struct fmha_fwd_appendkv_args
+{
+    void* q_ptr;
+    void* k_ptr;
+    const void* knew_ptr;
+    void* v_ptr;
+    const void* vnew_ptr;
+
+    const void* seqlen_k_ptr;
+
+    ck_tile::index_t seqlen_q;
+    ck_tile::index_t seqlen_knew;
+    ck_tile::index_t batch;
+    ck_tile::index_t hdim_q;
+    ck_tile::index_t hdim_v;
+    ck_tile::index_t nhead_q;
+    ck_tile::index_t nhead_k;
+
+    const void* rotary_cos_ptr; // only used if 'rotary_dim' > 0
+    const void* rotary_sin_ptr; // only used if 'rotary_dim' > 0
+    ck_tile::index_t rotary_dim;
+    bool has_mask;
+
+    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
+
+    const void* cache_batch_idx;
+
+    ck_tile::index_t stride_q;
+    ck_tile::index_t stride_k;
+    ck_tile::index_t stride_knew;
+    ck_tile::index_t stride_v;
+    ck_tile::index_t stride_vnew;
+    ck_tile::index_t nhead_stride_q;
+    ck_tile::index_t nhead_stride_k;
+    ck_tile::index_t nhead_stride_knew;
+    ck_tile::index_t nhead_stride_v;
+    ck_tile::index_t nhead_stride_vnew;
+    ck_tile::index_t batch_stride_q;
+    ck_tile::index_t batch_stride_k;
+    ck_tile::index_t batch_stride_knew;
+    ck_tile::index_t batch_stride_v;
+    ck_tile::index_t batch_stride_vnew;
 };
 
 template <typename FmhaKernel>
@@ -244,7 +363,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
 }
 
 template <typename Kernel>
-auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
+auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
 {
     assert(args.nhead_q % args.nhead_k == 0);
     auto kargs = [&] {
@@ -255,11 +374,9 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
                                      args.k_ptr,
                                      args.v_ptr,
                                      args.bias_ptr,
-                                     args.rand_val_ptr,
                                      args.lse_acc_ptr,
                                      args.o_acc_ptr,
                                      args.batch,
-                                     args.max_seqlen_q,
                                      args.seqstart_q_ptr,
                                      args.seqstart_k_ptr,
                                      args.seqlen_k_ptr,
@@ -274,24 +391,22 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
                                      args.stride_k,
                                      args.stride_v,
                                      args.stride_bias,
-                                     args.stride_randval,
                                      args.stride_o_acc,
                                      args.nhead_stride_q,
                                      args.nhead_stride_k,
                                      args.nhead_stride_v,
                                      args.nhead_stride_bias,
-                                     args.nhead_stride_randval,
                                      args.nhead_stride_lse_acc,
                                      args.nhead_stride_o_acc,
+                                     args.batch_stride_k,
+                                     args.batch_stride_v,
+                                     args.batch_stride_lse_acc,
                                      args.batch_stride_o_acc,
                                      args.split_stride_lse_acc,
                                      args.split_stride_o_acc,
                                      args.window_size_left,
                                      args.window_size_right,
-                                     args.mask_type,
-                                     args.p_drop,
-                                     args.s_randval,
-                                     args.drop_seed_offset);
+                                     args.mask_type);
         }
         else
         { // create batch mode kernel arguments
@@ -299,48 +414,45 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
                                      args.k_ptr,
                                      args.v_ptr,
                                      args.bias_ptr,
-                                     args.rand_val_ptr,
                                      args.lse_acc_ptr,
                                      args.o_acc_ptr,
                                      args.batch,
-                                     args.max_seqlen_q,
                                      args.seqlen_q,
                                      args.seqlen_k,
+                                     args.seqlen_k_ptr,
                                      args.hdim_q,
                                      args.hdim_v,
                                      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.cache_batch_idx,
                                      args.scale_s,
                                      args.scale_p,
                                      args.stride_q,
                                      args.stride_k,
                                      args.stride_v,
                                      args.stride_bias,
-                                     args.stride_randval,
                                      args.stride_o_acc,
                                      args.nhead_stride_q,
                                      args.nhead_stride_k,
                                      args.nhead_stride_v,
                                      args.nhead_stride_bias,
-                                     args.nhead_stride_randval,
                                      args.nhead_stride_lse_acc,
                                      args.nhead_stride_o_acc,
                                      args.batch_stride_q,
                                      args.batch_stride_k,
                                      args.batch_stride_v,
                                      args.batch_stride_bias,
-                                     args.batch_stride_randval,
                                      args.batch_stride_lse_acc,
                                      args.batch_stride_o_acc,
                                      args.split_stride_lse_acc,
                                      args.split_stride_o_acc,
                                      args.window_size_left,
                                      args.window_size_right,
-                                     args.mask_type,
-                                     args.p_drop,
-                                     args.s_randval,
-                                     args.drop_seed_offset);
+                                     args.mask_type);
         }
     }();
 
@@ -351,7 +463,7 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
 }
 
 template <typename Kernel>
-auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_args args)
+auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_splitkv_args args)
 {
     assert(args.nhead_q % args.nhead_k == 0);
     auto kargs = [&] {
@@ -410,6 +522,51 @@ auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_args args)
     return ck_tile::make_tuple(kargs, grids);
 }
 
+template <typename Kernel>
+auto fmha_fwd_appendkv_create_kargs_and_grids(fmha_fwd_appendkv_args args)
+{
+    assert(args.nhead_q % args.nhead_k == 0);
+    auto kargs = Kernel::MakeKargs(args.q_ptr,
+                                   args.k_ptr,
+                                   args.knew_ptr,
+                                   args.v_ptr,
+                                   args.vnew_ptr,
+                                   args.seqlen_q,
+                                   args.seqlen_k_ptr,
+                                   args.seqlen_knew,
+                                   args.hdim_q,
+                                   args.hdim_v,
+                                   args.nhead_q,
+                                   args.nhead_q / args.nhead_k,
+                                   args.rotary_cos_ptr,
+                                   args.rotary_sin_ptr,
+                                   args.rotary_dim,
+                                   args.has_mask,
+                                   args.block_table_ptr,
+                                   args.batch_stride_block_table,
+                                   args.page_block_size,
+                                   args.cache_batch_idx,
+                                   args.stride_q,
+                                   args.stride_k,
+                                   args.stride_knew,
+                                   args.stride_v,
+                                   args.stride_vnew,
+                                   args.nhead_stride_q,
+                                   args.nhead_stride_k,
+                                   args.nhead_stride_knew,
+                                   args.nhead_stride_v,
+                                   args.nhead_stride_vnew,
+                                   args.batch_stride_q,
+                                   args.batch_stride_k,
+                                   args.batch_stride_knew,
+                                   args.batch_stride_v,
+                                   args.batch_stride_vnew);
+
+    dim3 grids = Kernel::GridSize(args.batch, args.nhead_q, args.seqlen_q, args.seqlen_knew);
+
+    return ck_tile::make_tuple(kargs, grids);
+}
+
 // this is used to pattern-match internl kernel implementation, not to instantiate kernel
 template <ck_tile::index_t HDim_,
           typename DataType_,
@@ -458,8 +615,52 @@ struct fmha_fwd_traits_
 template <typename Traits_>
 float fmha_fwd_(const ck_tile::stream_config&, fmha_fwd_args);
 
+template <ck_tile::index_t HDim_,
+          typename DataType_,
+          bool kIsGroupMode_,
+          ck_tile::index_t kM0_,
+          ck_tile::index_t kN0_,
+          ck_tile::index_t kK0_,
+          ck_tile::index_t kN1_,
+          ck_tile::index_t kK1_,
+          ck_tile::index_t kK0BlockLength_,
+          bool kIsVLayoutRowMajor_,
+          ck_tile::BlockFmhaPipelineEnum FmhaPipelineEnum_,
+          typename FmhaMask_,
+          ck_tile::BlockAttentionBiasEnum BiasEnum_,
+          bool kStoreLse_,
+          bool kDoFp8StaticQuant_,
+          bool kIsPagedKV_,
+          bool kPadS_,
+          bool kPadSK_,
+          bool kPadD_,
+          bool kPadDv_>
+struct fmha_fwd_splitkv_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 kN0            = kN0_;
+    static constexpr ck_tile::index_t kK0            = kK0_;
+    static constexpr ck_tile::index_t kN1            = kN1_;
+    static constexpr ck_tile::index_t kK1            = kK1_;
+    static constexpr ck_tile::index_t kK0BlockLength = kK0BlockLength_;
+    static constexpr bool kIsVLayoutRowMajor         = kIsVLayoutRowMajor_;
+    static constexpr auto FmhaPipelineEnum           = FmhaPipelineEnum_;
+    using FmhaMask                                   = ck_tile::remove_cvref_t<FmhaMask_>;
+    static constexpr auto BiasEnum                   = BiasEnum_;
+    static constexpr bool kStoreLse                  = kStoreLse_;
+    static constexpr bool kDoFp8StaticQuant          = kDoFp8StaticQuant_;
+    static constexpr bool kPadS                      = kPadS_;
+    static constexpr bool kPadSK                     = kPadSK_;
+    static constexpr bool kPadD                      = kPadD_;
+    static constexpr bool kPadDv                     = kPadDv_;
+    static constexpr bool kIsPagedKV                 = kIsPagedKV_;
+};
+
 template <typename Traits_>
-void fmha_fwd_splitkv_oneshot_(const ck_tile::stream_config&, fmha_fwd_args);
+void fmha_fwd_splitkv_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);
 
 template <typename Traits_>
 std::string fmha_fwd_splitkv_get_name_();
@@ -487,11 +688,45 @@ struct fmha_fwd_splitkv_combine_traits_
 };
 
 template <typename Traits_>
-void fmha_fwd_splitkv_combine_oneshot_(const ck_tile::stream_config&, fmha_fwd_args);
+void fmha_fwd_splitkv_combine_oneshot_(const ck_tile::stream_config&, fmha_fwd_splitkv_args);
 
 template <typename Traits_>
 std::string fmha_fwd_splitkv_combine_get_name_();
 
+// this is used to pattern-match internl kernel implementation, not to instantiate kernel
+template <ck_tile::index_t HDim_,
+          typename DataType_,
+          ck_tile::index_t kTileSizeS_,
+          ck_tile::index_t kTileSizeSk_,
+          ck_tile::index_t kTileSizeD_,
+          ck_tile::index_t kTileSizeDv_,
+          bool kIsVLayoutRowMajor_,
+          bool kPadS_,
+          bool kPadSk_,
+          bool kPadD_,
+          bool kPadDv_,
+          ck_tile::RotaryEmbeddingEnum RotaryEnum_,
+          bool kIsPagedKV_>
+struct fmha_fwd_appendkv_traits_
+{
+    static constexpr ck_tile::index_t HDim        = HDim_;
+    using DataType                                = ck_tile::remove_cvref_t<DataType_>;
+    static constexpr ck_tile::index_t kTileSizeS  = kTileSizeS_;
+    static constexpr ck_tile::index_t kTileSizeSk = kTileSizeSk_;
+    static constexpr ck_tile::index_t kTileSizeD  = kTileSizeD_;
+    static constexpr ck_tile::index_t kTileSizeDv = kTileSizeDv_;
+    static constexpr bool kIsVLayoutRowMajor      = kIsVLayoutRowMajor_;
+    static constexpr bool kPadS                   = kPadS_;
+    static constexpr bool kPadSk                  = kPadSk_;
+    static constexpr bool kPadD                   = kPadD_;
+    static constexpr bool kPadDv                  = kPadDv_;
+    static constexpr auto RotaryEnum              = RotaryEnum_;
+    static constexpr bool kIsPagedKV              = kIsPagedKV_;
+};
+
+template <typename Traits_>
+float fmha_fwd_appendkv_(const ck_tile::stream_config&, fmha_fwd_appendkv_args);
+
 // This is the public API, will be generated by script
 struct fmha_fwd_traits
 {
@@ -508,4 +743,32 @@ struct fmha_fwd_traits
     // TODO: padding check is inside this api
 };
 float fmha_fwd(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
-float fmha_fwd_splitkv(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
+
+struct fmha_fwd_splitkv_traits
+{
+    int hdim_q;
+    int hdim_v;
+    std::string data_type;
+    bool is_group_mode;
+    bool is_v_rowmajor;
+    mask_enum mask_type;
+    bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
+    bool has_lse;
+    bool do_fp8_static_quant;
+    // TODO: padding check is inside this api
+};
+float fmha_fwd_splitkv(fmha_fwd_splitkv_traits,
+                       fmha_fwd_splitkv_args,
+                       const ck_tile::stream_config&);
+
+struct fmha_fwd_appendkv_traits
+{
+    int hdim_q;
+    int hdim_v;
+    std::string data_type;
+    bool is_v_rowmajor;
+    rope_enum rope_type;
+};
+float fmha_fwd_appendkv(fmha_fwd_appendkv_traits,
+                        fmha_fwd_appendkv_args,
+                        const ck_tile::stream_config&);

example/ck_tile/01_fmha/generate.py

@@ -5,25 +5,30 @@
 import argparse
 from enum import IntEnum
 from pathlib import Path
+import pkgutil
+import sys
 from typing import List, Optional
 
+import codegen.ops
 from codegen.cmake_config import *
-from codegen.ops import (
-    fmha_fwd,
-    fmha_fwd_splitkv,
-    fmha_bwd
-)
 
 
 class HandlerId(IntEnum):
     LIST_BLOBS = 0
     WRITE_BLOBS = 1
 
-handlers = {
-    'fwd'         : (fmha_fwd.list_blobs, fmha_fwd.write_blobs),
-    'fwd_splitkv' : (fmha_fwd_splitkv.list_blobs, fmha_fwd_splitkv.write_blobs),
-    'bwd'         : (fmha_bwd.list_blobs, fmha_bwd.write_blobs),
-}
+# inspect all modules under 'codegen.ops' and register API handlers 
+ops = []
+for importer, module_name, _ in pkgutil.iter_modules(codegen.ops.__path__):
+    full_module_name = '%s.%s' % (codegen.ops.__name__, module_name)
+    if full_module_name not in sys.modules:
+        ops.append(importer.find_spec(module_name).loader.load_module(module_name))
+unwanted_prefix = 'fmha_'
+handlers = dict(
+    [(op.__name__[len(unwanted_prefix):] if op.__name__.startswith(unwanted_prefix) else op.__name__,
+        (op.list_blobs, op.write_blobs)) for op in ops]
+)
+assert 0 < len(handlers)
 
 def write_blobs(output_dir: Optional[str], api_list : List[str], kernel_filter : Optional[str], receipt, mask_impl) -> None:
     if output_dir is None:
@@ -103,4 +108,4 @@ if __name__ == "__main__":
     if args.list_blobs is not None:
         list_blobs(args.list_blobs, api_list, args.filter, int(args.receipt), mask_impl=args.mask)
     else:
-        write_blobs(args.output_dir, api_list, args.filter, int(args.receipt), mask_impl=args.mask)
+        write_blobs(args.output_dir, api_list, args.filter, int(args.receipt), mask_impl=args.mask)
\ No newline at end of file

example/ck_tile/01_fmha/rotary.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/host_tensor.hpp"
+
+#include <cassert>
+#include <cmath>
+#include <functional>
+#include <iterator>
+#include <optional>
+#include <random>
+#include <tuple>
+
+// keep sync with RotaryEmbeddingEnum
+enum class rope_enum
+{
+    none         = 0,
+    interleaved  = 1,
+    half_rotated = 2,
+};
+
+template <typename DataType>
+std::tuple<ck_tile::HostTensor<DataType>, ck_tile::HostTensor<DataType>>
+generate_rotary_cos_sin(ck_tile::index_t seqlen,
+                        ck_tile::index_t rotary_dim,
+                        std::optional<unsigned> seed = std::nullopt)
+{
+    // return dummy tensors if we won't apply RoPE at all
+    if(rotary_dim <= 0)
+    {
+        ck_tile::HostTensor<DataType> dummy({1, 1});
+        return std::make_tuple(dummy, dummy);
+    }
+
+    std::mt19937 random_engine(seed.has_value() ? *seed : std::random_device{}());
+    std::uniform_real_distribution<float> generator(0.0f, 1.0f);
+
+    const ck_tile::index_t num_rows = seqlen * 2;
+    const ck_tile::index_t num_cols = rotary_dim / 2;
+
+    using std::begin, std::end;
+
+    ck_tile::HostTensor<float> angle({num_rows, num_cols});
+    std::generate(begin(angle), end(angle), [&] { return generator(random_engine) * 2 * M_PI; });
+
+    ck_tile::HostTensor<DataType> cos({num_rows, num_cols});
+    std::transform(begin(angle), end(angle), begin(cos), [](float origin_value) {
+        return ck_tile::type_convert<DataType>(std::cos(origin_value));
+    });
+
+    ck_tile::HostTensor<DataType> sin({num_rows, num_cols});
+    std::transform(begin(angle), end(angle), begin(sin), [](float origin_value) {
+        return ck_tile::type_convert<DataType>(std::sin(origin_value));
+    });
+
+    return std::make_tuple(cos, sin);
+}
+
+template <typename DataType>
+std::tuple<ck_tile::HostTensor<DataType>, ck_tile::HostTensor<DataType>>
+slice_rotary_cos_sin(const ck_tile::HostTensor<DataType>& cos,
+                     const ck_tile::HostTensor<DataType>& sin,
+                     ck_tile::index_t seqlen_offset,
+                     ck_tile::index_t seqlen)
+{
+    assert(cos.get_num_of_dimension() == 2 && sin.get_num_of_dimension() == 2);
+    assert(cos.get_length(0) == sin.get_length(0) && cos.get_length(1) == sin.get_length(1));
+
+    assert(static_cast<std::size_t>(seqlen_offset + seqlen) <= cos.get_length(0));
+
+    const ck_tile::index_t num_rows = seqlen;
+    const ck_tile::index_t num_cols = cos.get_length(1);
+
+    ck_tile::HostTensor<DataType> cos_pt({num_rows, num_cols});
+    cos_pt.ForEach([&](auto& self, auto i) { self(i) = cos(i[0] + seqlen_offset, i[1]); });
+
+    ck_tile::HostTensor<DataType> sin_pt({num_rows, num_cols});
+    sin_pt.ForEach([&](auto& self, auto i) { self(i) = sin(i[0] + seqlen_offset, i[1]); });
+
+    return std::make_tuple(cos_pt, sin_pt);
+}

example/ck_tile/01_fmha/script/benchmark_bwd.sh

 #!/bin/sh
-# TODO: run this script from CK root
-BUILD=build
-EXE=$BUILD/bin/tile_example_fmha_bwd
+# TODO: run this script from CK root or build directory
+EXE="$(find . -name tile_example_fmha_bwd -type f | head -n 1)"
 VALID=0
 
 for prec in "fp16" "bf16" ; do

example/ck_tile/01_fmha/script/benchmark_fwd.sh

 #!/bin/sh
-# TODO: run this script from CK root
-BUILD=build
-EXE=$BUILD/bin/tile_example_fmha_fwd
+# TODO: run this script from CK root or build directory
+EXE="$(find . -name tile_example_fmha_fwd -type f | head -n 1)"
 VALID=0
 
 for prec in "fp16" "bf16" ; do

example/ck_tile/01_fmha/script/smoke_test_bwd.sh

 #!/bin/sh
-# TODO: run this script from CK root
-BUILD=build
-EXE=$BUILD/bin/tile_example_fmha_bwd
+# TODO: run this script from CK root or build directory
+EXE="$(find . -name tile_example_fmha_bwd -type f | head -n 1)"
 KNAME=1
 
 export CK_WARMUP=0

example/ck_tile/01_fmha/script/smoke_test_fwd.sh

-#!/bin/sh
-# TODO: run this script from CK root
-BUILD=build
-EXE=$BUILD/bin/tile_example_fmha_fwd
+#!/bin/bash
+# TODO: run this script from CK root or build directory
+EXE="$(find . -name tile_example_fmha_fwd -type f | head -n 1)"
 KNAME=1
 
 export CK_WARMUP=0
@@ -10,44 +9,98 @@ export CK_REPEAT=1
 COMMON_ARGS='-v=1 -warmup=0 -repeat=1'
 # mode=0
 # export HIP_VISIBLE_DEVICES=4
-set -x
-for prec in "fp16" "bf16" ; do
-for mode in 1 0 ; do
-for perm in 0 1 ; do
-for vlayout in "r" "c" ; do
-for hdim in 32 64 128 256 ; do
-for lse in 0 1 ; do
-for bias in "n" "e" "a" ; do
-for p_drop in 0.0 0.2; do
-
-# $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
-$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1 -s_k=10 -s_kpad=32 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
 
-done
-done
-done
-done
-done
-done
-done
+TEST_SPLITKV=0
+TEST_APPENDKV=0
+# options:
+#    -s: run splitkv tests
+#    -a: run appendkv tests
+while getopts ":sa" opt; do
+    case "${opt}" in
+        s)
+            TEST_SPLITKV=1
+            ;;
+        a)
+            TEST_APPENDKV=1
+            ;;
+        *)
+            ;;
+    esac
 done
 
+run_fp16_bf16_tests() {
+    local NUM_SPLITS=(1)
+    local PAGE_BLOCK_SIZE=(0)
+    local CACHE_BATCH_IDX=(0)
 
-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
-$EXE -prec=fp8 -init=3 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=c -squant=1 -kname=$KNAME $COMMON_ARGS
-done
-done
-done
-done
-set +x
+    if [ $TEST_SPLITKV -eq 1 ] ; then
+        NUM_SPLITS+=(2 3)
+        PAGE_BLOCK_SIZE+=(128)
+        CACHE_BATCH_IDX+=(1)
+    fi
+
+    for prec in "fp16" "bf16" ; do
+    for mode in 1 0 ; do
+    for perm in 0 1 ; do
+    for vlayout in "r" "c" ; do
+    for hdim in 32 64 128 256 ; do
+    for lse in 0 1 ; do
+    for bias in "n" "e" "a" ; do
+    for p_drop in 0.0 0.2 ; do
+    for num_splits in "${NUM_SPLITS[@]}" ; do
+    for page_block_size in "${PAGE_BLOCK_SIZE[@]}" ; do
+    for cache_batch_idx in "${CACHE_BATCH_IDX[@]}" ; do
+
+    # $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+    $EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1 -s_k=10 -s_kpad=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS  
+
+    done ; done ; done ; done ; done
+    done ; done ; done ; done ; done
+    done ;
+}
+
+run_fp8_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
+    
+    $EXE -prec=fp8 -init=3 -b=$b -h=1 -d=128 -s=128 -bias=$bias -iperm=$perm -operm=$perm -vlayout=c -squant=1 -kname=$KNAME $COMMON_ARGS  
+
+    done ; done ; done ; done
+}
+
+run_fp16_appendkv_tests() {
+    for s in $(seq 63 1 65) ; do
+    for s_k in 65 129 ; do
+    for s_knew in 0 64 $s_k ; do
+    for hdim in 32 64 128 256 ; do
+    for ri in 0 1 ; do
+    for rdim in 0 16 32 $hdim ; do
+    for page_block_size in 0 128 ; do
+    for cache_batch_idx in 0 1 ; do
+
+    $EXE -prec=fp16 -b=3 -h=3 -d=$hdim -s=$s -s_k=$s_k -s_knew=$s_knew -rotary_dim=$rdim -rotary_interleaved=$ri -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -iperm=1 -operm=1 -kname=1 $COMMON_ARGS
+
+    done ; done ; done ; done ; done 
+    done ; done ; done
+}
+
+set -x
+
+run_fp16_bf16_tests
+run_fp8_tests
+
+if [ $TEST_APPENDKV -eq 1 ] ; then
+    run_fp16_appendkv_tests
+fi
+
+set +x
\ No newline at end of file

example/ck_tile/01_fmha/utils.hpp

@@ -3,15 +3,17 @@
 
 #pragma once
 
+#include <algorithm>
 #include <cstdint>
 #include <cstdlib>
+#include <functional>
 #include <optional>
 #include <ostream>
+#include <sstream>
+#include <string>
 #include <tuple>
 #include <utility>
 #include <vector>
-#include <functional>
-#include <string>
 
 #include "ck_tile/core/container/span.hpp"
 
@@ -37,18 +39,21 @@ std::vector<int32_t> to_seqstarts(ck_tile::span<const int32_t> seqlens)
     return seqstarts;
 }
 
-std::vector<int32_t> generate_seqlens(mode_enum mode,
-                                      unsigned count,
+std::vector<int32_t> generate_seqlens(unsigned count,
                                       int32_t seqlen_avg,
+                                      int32_t seqlen_min = -1, // if not negative, clamp min
                                       int32_t seqlen_max = -1, // if not negative, clamp max
                                       std::optional<unsigned> seed = std::nullopt)
 {
     assert(0 < count);
 
-    std::vector<int32_t> seqlens(
-        count, seqlen_max > 0 ? (seqlen_avg < seqlen_max ? seqlen_avg : seqlen_max) : seqlen_avg);
+    seqlen_min = (0 < seqlen_min ? seqlen_min : 1);
+    seqlen_max = (0 < seqlen_max ? seqlen_max : std::numeric_limits<int32_t>::max());
+    assert(seqlen_min <= seqlen_max);
 
-    if(mode == mode_enum::group && 1 < count)
+    std::vector<int32_t> seqlens(count, std::clamp(seqlen_avg, seqlen_min, seqlen_max));
+
+    if(1 < count)
     {
         using size_type = std::vector<int32_t>::size_type;
 
@@ -62,15 +67,15 @@ std::vector<int32_t> generate_seqlens(mode_enum mode,
         for(unsigned repeat = seqlen_avg * (count / 2); 0 < repeat; --repeat)
         {
             const size_type to_decrease = next_idx();
-            // make sure each elements of seqlens is always greater than 0
-            if(seqlens[to_decrease] == 1)
+            // make sure each elements of seqlens is always greater than seqlen_min
+            if(seqlens[to_decrease] == seqlen_min)
             {
                 continue;
             }
 
             const size_type to_increase = (to_decrease + next_step()) % count;
 
-            if(seqlen_max > 0 && seqlens[to_increase] >= seqlen_max)
+            if(seqlens[to_increase] >= seqlen_max)
             {
                 continue;
             }
@@ -83,13 +88,29 @@ std::vector<int32_t> generate_seqlens(mode_enum mode,
     return seqlens;
 }
 
-std::vector<int32_t> generate_seqstarts(mode_enum mode,
-                                        unsigned count,
-                                        int32_t seqlen_avg,
-                                        int32_t seqlen_max           = -1,
-                                        std::optional<unsigned> seed = std::nullopt)
+// return random integer generated uniformly in range [low, high]
+template <typename Int = int>
+auto randint(Int low, Int high, std::optional<unsigned> seed = std::nullopt)
+    -> std::enable_if_t<std::is_integral_v<Int>, Int>
+{
+    std::mt19937 engine(seed.has_value() ? *seed : std::random_device{}());
+    std::uniform_int_distribution<Int> dist(low, high);
+    return dist(engine);
+}
+
+// return random integers generated uniformly in range [low, high]
+template <typename Int, typename ForwardIterator>
+auto randints(ForwardIterator first,
+              ForwardIterator last,
+              Int low,
+              Int high,
+              std::optional<unsigned> seed = std::nullopt)
+    -> std::enable_if_t<std::is_integral_v<Int>>
 {
-    return to_seqstarts(generate_seqlens(mode, count, seqlen_avg, seqlen_max, seed));
+    std::mt19937 engine(seed.has_value() ? *seed : std::random_device{}());
+    std::uniform_int_distribution<Int> dist(low, high);
+
+    std::generate(first, last, [&] { return dist(engine); });
 }
 
 /*
@@ -112,16 +133,45 @@ decode_seqlen(mode_enum mode,
               std::string q_val,
               std::string k_val,
               std::string k_pad_val,
-              std::optional<unsigned> seed = std::nullopt)
+              ck_tile::index_t seqlen_k_min = 0,
+              bool use_kvcache              = false,
+              std::optional<unsigned> seed  = std::nullopt)
 {
 #define _S2I_(str_) static_cast<ck_tile::index_t>(std::atoi((str_).c_str()))
     if(mode == mode_enum::batch)
     {
         ck_tile::index_t q = _S2I_(q_val);
         ck_tile::index_t k = _S2I_(k_val);
-        auto s_q           = std::vector<ck_tile::index_t>(batch, q);
-        auto s_k           = std::vector<ck_tile::index_t>(batch, k < 0 ? q : k);
+
+        auto s_q = std::vector<ck_tile::index_t>(batch, q);
+        auto s_k = [&] {
+            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)
+            {
+                // to keep the original s_k value, we always use seqlen_k_max in first batch
+                randints(std::next(seqlen_ks.begin()),
+                         seqlen_ks.end(),
+                         seqlen_k_min,
+                         seqlen_k_max,
+                         seed);
+                return seqlen_ks;
+            }
+
+            return seqlen_ks;
+        }();
         auto s_kpad = std::vector<ck_tile::index_t>(batch, -1); // TODO: batch not support k_padding
+
+        // s_k should be greater than or equal to seqlen_k_min if provided
+        if(s_k.back() < seqlen_k_min)
+        {
+            std::ostringstream msg;
+            msg << __FILE__ << ":" << __LINE__ << ": seqlen_k (=" << s_k.back()
+                << ") is less than minimum seqlen_k (=" << seqlen_k_min << ")";
+            throw std::runtime_error(msg.str());
+        }
+
         return std::make_tuple(s_q, s_k, s_kpad);
     }
     else
@@ -149,6 +199,16 @@ decode_seqlen(mode_enum mode,
             s_q.push_back(q);
             s_k.push_back(k < 0 ? q : k);
             s_kpad.push_back(kp);
+
+            // s_k should be greater than or equal to seqlen_k_min
+            if(s_k.back() < seqlen_k_min)
+            {
+                std::ostringstream msg;
+                msg << __FILE__ << ":" << __LINE__ << ": seqlen_k (=" << s_k.back()
+                    << ") is less than minimum seqlen_k (=" << seqlen_k_min << ")";
+                throw std::runtime_error(msg.str());
+            }
+
             idx++;
             if(found_q == std::string::npos || idx >= batch)
             {
@@ -160,8 +220,9 @@ decode_seqlen(mode_enum mode,
         }
         if(idx < batch)
         {
-            auto rem_q = generate_seqlens(mode, batch - idx, s_q.back(), s_kpad.back(), seed);
-            auto rem_k = generate_seqlens(mode, batch - idx, s_k.back(), s_kpad.back(), seed);
+            auto rem_q = generate_seqlens(batch - idx, s_q.back(), 1, s_kpad.back(), seed);
+            auto rem_k =
+                generate_seqlens(batch - idx, s_k.back(), seqlen_k_min, s_kpad.back(), seed);
 
             s_q.insert(s_q.end(), rem_q.begin(), rem_q.end());
             s_k.insert(s_k.end(), rem_k.begin(), rem_k.end());
@@ -180,3 +241,15 @@ int env_get_int(const char* var_name, int default_int)
         r = std::atoi(v);
     return r;
 }
+
+template <typename RandomAccessIterator, typename Int>
+std::enable_if_t<std::is_integral_v<Int>> iota_shuffle(RandomAccessIterator first,
+                                                       RandomAccessIterator last,
+                                                       Int value,
+                                                       std::optional<unsigned> seed = std::nullopt)
+{
+    std::iota(first, last, value);
+
+    std::mt19937 engine(seed.has_value() ? *seed : std::random_device{}());
+    std::shuffle(first, last, engine);
+}

include/ck_tile/core/numeric/math.hpp

@@ -536,13 +536,20 @@ float log(float x) { return __logf(x); };
 CK_TILE_HOST
 float log(float x) { return std::logf(x); };
 
-CK_TILE_DEVICE uint32_t sad(uint32_t x, uint32_t y, uint32_t acc)
+CK_TILE_DEVICE uint16_t sad_u16(uint16_t x, uint16_t y, uint16_t acc)
 {
-    // TODO: this is hacky, we use u16
     return __builtin_amdgcn_sad_u16(x, y, acc);
 }
 
-CK_TILE_HOST uint32_t sad(uint32_t x, uint32_t y, uint32_t acc)
+CK_TILE_DEVICE uint32_t sad_u32(uint32_t x, uint32_t y, uint32_t acc)
+{
+    /// TODO: replace inline asm when intrinsic is available
+    uint32_t res;
+    asm volatile("v_sad_u32 %0, %1, %2, %3" : "=v"(res) : "v"(x), "v"(y), "v"(acc));
+    return res;
+}
+
+CK_TILE_HOST uint32_t sad_u32(uint32_t x, uint32_t y, uint32_t acc)
 {
     return (x > y ? (x - y) : (y - x)) + acc;
 }

include/ck_tile/core/tensor/tile_window.hpp

@@ -214,6 +214,12 @@ struct tile_window_with_static_distribution
 
     CK_TILE_DEVICE constexpr auto get_window_origin() const { return window_origin_; }
 
+    CK_TILE_DEVICE constexpr void
+    set_bottom_tensor_view_data_ptr(typename BottomTensorView::DataType* data)
+    {
+        bottom_tensor_view_.buf_.p_data_ = data;
+    }
+
     // move thread's window adaptor coordinate and bottom tensor coordinate
     // [p0, p1, ..., y0, y1, ...] ==> [x0, x1, ...] ==> [x0', x1', ...] ==> [offset]
     CK_TILE_DEVICE void move_window_adaptor_and_bottom_tensor_thread_coordinate(
@@ -843,6 +849,17 @@ struct tile_window_with_static_lengths
 
     CK_TILE_DEVICE constexpr auto get_window_origin() const { return window_origin_; }
 
+    CK_TILE_DEVICE void set_window_origin(const BottomTensorIndex& new_window_origin)
+    {
+        window_origin_ = new_window_origin;
+    }
+
+    CK_TILE_DEVICE constexpr void
+    set_bottom_tensor_view_data_ptr(typename BottomTensorView::DataType* data)
+    {
+        bottom_tensor_view_.buf_.p_data_ = data;
+    }
+
     // move window-origin
     CK_TILE_DEVICE void move(const BottomTensorIndex& step) { window_origin_ += step; }
 
@@ -871,6 +888,39 @@ make_tile_window(const TensorView_& tensor_view,
         tensor_view, window_lengths, origin};
 }
 
+// duplicate tile window and replace its origin
+template <typename TensorView, typename WindowLengths>
+CK_TILE_DEVICE constexpr auto
+make_tile_window(const tile_window_with_static_lengths<TensorView, WindowLengths>& tile_window,
+                 const multi_index<TensorView::get_num_of_dimension()>& origin)
+{
+    return tile_window_with_static_lengths<TensorView, WindowLengths>{
+        tile_window.get_bottom_tensor_view(), tile_window.get_window_lengths(), origin};
+}
+
+template <typename TensorView, typename WindowLengths, typename StaticTileDistribution>
+CK_TILE_DEVICE constexpr auto
+make_tile_window(const tile_window_with_static_lengths<TensorView, WindowLengths>& tile_window,
+                 const multi_index<TensorView::get_num_of_dimension()>& origin,
+                 const StaticTileDistribution& tile_distribution)
+{
+    return make_tile_window(tile_window.get_bottom_tensor_view(),
+                            tile_window.get_window_lengths(),
+                            origin,
+                            tile_distribution);
+}
+
+template <typename TensorView, typename WindowLengths, typename StaticTileDistribution>
+CK_TILE_DEVICE constexpr auto
+make_tile_window(const tile_window_with_static_lengths<TensorView, WindowLengths>& tile_window,
+                 const StaticTileDistribution& tile_distribution)
+{
+    return make_tile_window(tile_window.get_bottom_tensor_view(),
+                            tile_window.get_window_lengths(),
+                            tile_window.get_window_origin(),
+                            tile_distribution);
+}
+
 template <typename TensorView_, typename WindowLengths_>
 CK_TILE_DEVICE void move_tile_window(
     tile_window_with_static_lengths<TensorView_, WindowLengths_>& window,

include/ck_tile/core/utility/type_traits.hpp

@@ -22,6 +22,23 @@ using remove_cvref_t = remove_cv_t<std::remove_reference_t<T>>;
 template <typename T>
 using remove_pointer_t = typename std::remove_pointer<T>::type;
 
+template <typename From, typename To>
+struct copy_const
+{
+    static_assert(!std::is_const_v<From>);
+
+    using type = To;
+};
+
+template <typename From, typename To>
+struct copy_const<const From, To>
+{
+    using type = std::add_const_t<typename copy_const<From, To>::type>;
+};
+
+template <typename From, typename To>
+using copy_const_t = typename copy_const<From, To>::type;
+
 namespace detail {
 template <class Default, class AlwaysVoid, template <class...> class Op, class... Args>
 struct detector

include/ck_tile/host.hpp

@@ -15,6 +15,7 @@
 #include "ck_tile/host/reference/reference_batched_elementwise.hpp"
 #include "ck_tile/host/reference/reference_batched_gemm.hpp"
 #include "ck_tile/host/reference/reference_batched_masking.hpp"
+#include "ck_tile/host/reference/reference_batched_rotary_position_embedding.hpp"
 #include "ck_tile/host/reference/reference_batched_softmax.hpp"
 #include "ck_tile/host/reference/reference_gemm.hpp"
 #include "ck_tile/host/reference/reference_im2col.hpp"

include/ck_tile/host/host_tensor.hpp

@@ -155,7 +155,12 @@ struct HostTensorDescriptor
         return space;
     }
 
+    std::size_t get_length(std::size_t dim) const { return mLens[dim]; }
+
     const std::vector<std::size_t>& get_lengths() const { return mLens; }
+
+    std::size_t get_stride(std::size_t dim) const { return mStrides[dim]; }
+
     const std::vector<std::size_t>& get_strides() const { return mStrides; }
 
     template <typename... Is>
@@ -325,8 +330,12 @@ struct HostTensor
     {
     }
 
+    std::size_t get_length(std::size_t dim) const { return mDesc.get_length(dim); }
+
     decltype(auto) get_lengths() const { return mDesc.get_lengths(); }
 
+    std::size_t get_stride(std::size_t dim) const { return mDesc.get_stride(dim); }
+
     decltype(auto) get_strides() const { return mDesc.get_strides(); }
 
     std::size_t get_num_of_dimension() const { return mDesc.get_num_of_dimension(); }

include/ck_tile/host/kernel_launch.hpp

@@ -73,17 +73,17 @@ CK_TILE_HOST float launch_kernel(const stream_config& s, Callables... callables)
 {
     // clang-format off
     if(!s.time_kernel_) {
-        (callables(s),...); hip_check_error(hipGetLastError());
+        (callables(s),...); HIP_CHECK_ERROR(hipGetLastError());
         return 0;
     }
     if(s.is_gpu_timer_) {
         gpu_timer timer {};
 
         // warmup
-        for(int i = 0; i < s.cold_niters_; i++) { (callables(s),...); } hip_check_error(hipGetLastError());
+        for(int i = 0; i < s.cold_niters_; i++) { (callables(s),...); } HIP_CHECK_ERROR(hipGetLastError());
 
         timer.start(s.stream_id_);
-        for(int i = 0; i < s.nrepeat_; i++) { (callables(s),...); } hip_check_error(hipGetLastError());
+        for(int i = 0; i < s.nrepeat_; i++) { (callables(s),...); } HIP_CHECK_ERROR(hipGetLastError());
         timer.stop(s.stream_id_);
 
         return timer.duration() / s.nrepeat_;
@@ -92,10 +92,10 @@ CK_TILE_HOST float launch_kernel(const stream_config& s, Callables... callables)
         cpu_timer timer {};
 
         // warmup
-        for(int i = 0; i < s.cold_niters_; i++) { (callables(s),...); } hip_check_error(hipGetLastError());
+        for(int i = 0; i < s.cold_niters_; i++) { (callables(s),...); } HIP_CHECK_ERROR(hipGetLastError());
 
         timer.start(s.stream_id_);
-        for(int i = 0; i < s.nrepeat_; i++) { (callables(s),...); } hip_check_error(hipGetLastError());
+        for(int i = 0; i < s.nrepeat_; i++) { (callables(s),...); } HIP_CHECK_ERROR(hipGetLastError());
         timer.stop(s.stream_id_);
 
         return timer.duration() / s.nrepeat_;

include/ck_tile/host/reference/reference_batched_rotary_position_embedding.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/host_tensor.hpp"
+
+#include <cassert>
+#include <thread>
+
+namespace ck_tile {
+
+template <typename DataType, typename ComputeDataType = float>
+CK_TILE_HOST void reference_batched_rotary_position_embedding(const HostTensor<DataType>& input_bsd,
+                                                              const HostTensor<DataType>& cos_sd,
+                                                              const HostTensor<DataType>& sin_sd,
+                                                              bool interleaved,
+                                                              HostTensor<DataType>& output_bsd,
+                                                              bool use_1_row_sin_cos = false)
+{
+    assert(cos_sd.get_num_of_dimension() == 2 && sin_sd.get_num_of_dimension() == 2);
+    assert(cos_sd.get_length(0) == sin_sd.get_length(0) &&
+           cos_sd.get_length(1) == sin_sd.get_length(1));
+
+    const index_t rotary_dim = cos_sd.get_length(1) * 2;
+    assert(static_cast<std::size_t>(rotary_dim) <= input_bsd.get_length(2));
+
+    output_bsd.ForEach([&](auto& self, auto i) {
+        const index_t i_d = i[2];
+        if(rotary_dim <= i_d)
+        {
+            self(i) = input_bsd(i);
+            return;
+        }
+        assert(i_d < rotary_dim);
+
+        const index_t i_s         = i[1];
+        const index_t i_s_cos_sin = (use_1_row_sin_cos ? 0 : i_s);
+
+        const ComputeDataType cos = type_convert<ComputeDataType>(
+            interleaved ? cos_sd(i_s_cos_sin, i_d / 2)
+                        : cos_sd(i_s_cos_sin, i_d % cos_sd.get_length(1)));
+        const ComputeDataType sin = type_convert<ComputeDataType>(
+            interleaved ? sin_sd(i_s_cos_sin, i_d / 2)
+                        : sin_sd(i_s_cos_sin, i_d % sin_sd.get_length(1)));
+
+        const ComputeDataType half_rotated_input = [&] {
+            const index_t i_b = i[0];
+
+            if(interleaved)
+            {
+                const bool is_even         = (i_d % 2 == 0);
+                const index_t pos          = i_d + (is_even ? 1 : -1);
+                const ComputeDataType sign = (is_even ? -1 : 1);
+                return sign * type_convert<ComputeDataType>(input_bsd(i_b, i_s, pos));
+            }
+            else
+            {
+                const index_t half_rdim    = (rotary_dim / 2);
+                const index_t pos          = (i_d + half_rdim) % rotary_dim;
+                const ComputeDataType sign = (pos < half_rdim ? 1 : -1);
+                return sign * type_convert<ComputeDataType>(input_bsd(i_b, i_s, pos));
+            }
+        }();
+        ComputeDataType result =
+            type_convert<ComputeDataType>(input_bsd(i)) * cos + half_rotated_input * sin;
+
+        self(i) = type_convert<DataType>(result);
+    });
+}
+
+} // namespace ck_tile