mirror of
https://github.com/ROCm/composable_kernel.git
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[CK_TILE] fmha forward split-kv + combine kernels (#1338)
* FA fwd dropout * FA bwd * epilogue reuse * CMakeLists update * [CK_TILE] support alibi (#1269) * add alibi support * fix code * update code based on comment * Support more hdim * fix fp8 bias * support seqlen_k=0 case * remove unused printf * fix format --------- Co-authored-by: rocking <ChunYu.Lai@amd.com> * now fwd/bwd can build * bwd alibi * add bwd validation stream_config * update generated filenames * update bwd kernel launch * CK_TILE_HOST_DEVICE in philox * Transpose -> transpose * format * format * format * Generate the instance for FA required * format * fix error in WarpGemm * Add num_splits option and dummy split-kv api method * Generate fmha_fwd_splitkv() * Add SplitKV kernel codegen logics * Add SplitKV combine kernel codegen logics * Fix mismatched return type * Clean-up code * Replace sentinel value before storing * Fix wrong layout of LSE/LSEacc/Oacc * Format codes * Fix o_acc memory error * Fix wrong kBlockSize used in policy * Reduce # of combine kernels * Fix split-kv combine kernel name * Fix wrong LDS indexing logics * Fix wrong loop counter step logic * Undo vector size changes * Remove no-longer used field * Remove in-consistent comment * Remove debug statements in example * Remove more debug statements * Add constness to local variables * Clearn up generate.py * Fix unstable clang-format comment * Remove unused include directive * Use shorter template parameter name * Enable non-split-kv blobs * Update license date * Print num_splits conditionally * Undo disabling data types * Remove unnessary tile size for fp8 * Fix wrong pipeline args for fp8 * Fix example output format * Remove more debug code in combine pipeline * Add stride kernel arguments for LSE/O acc workspace * Re-order split-kv pipeline call operator arguments * Pass LSE/O strides in kernel argument * Re-order pipeline call operator arguments * Use tensor_descriptor to locate LSEacc elements * Support providing invalid element for tensor view * Set invalid element value for LSEacc tensor view * Remove hand-written store_tile() code * Remove necessary value-overwrite logic * Add transposed lds descriptor * Support load_tile() for tile_window_with_static_lengths<> * Undo removing necessary value-overwrite logic * Use read descriptor to locate lds elements * Simplify pipeline source code * Add constraint to kMaxSplits * Default use kMaxSplits=64 in generate.py * Revert "Add constraint to kMaxSplits" This reverts commit0a2132d758. * Revert "Default use kMaxSplits=64 in generate.py" This reverts commitc7d9c80b77. * Decide alignment by the padding parameter * Remove no-longer used utility functions * Remove not-working code * Add comment & remove no-longer used code * Fix computation errors * Add heuristic to override num_splits option * Add constraint to kMaxSplits * Fix compilation error * Clean up pipeline code * Wrap pointer access as lambda function * Rename confusing methods * Use kLogMasSplits as template parameter * Finish splitkv combine kernel codegen * Update kMaxSplits limit * Use smaller kM0 for splitkv combine kernel * Ignore droupout flag in splitkv pipeline * Unify flag usage * Add back flag kStoreLSE * Merge lambda calls in pipeline * Fix compilation errors * Avoid all empty splits * Always check for empty loop in splitkv pipelines * Re-order parameters * Remove redundant p_drop option check * Add traits/problem for fwd splitkv kernel * Conditionally enable uneven split boundary checks * Add comment for the splitkv traits field * Change even split criteria * Re-order statements * Refine occupancy value for hdim=128&256 * Refine occupancy value for hdim=32&64 * Remove redundant kernel argument * Separate fmha bwd codegen logics * Separate fmha fwd codegen logics * Remove redundant direction parameter in fwd&bwd codegen logics * Support generate multiple APIs for an example * Let 'api' an alias of 'direction' option * Remove choices for the 'direction' option * Use dictionary to config all the functions * Move fmha splitkv codegen logics to other file * Add fwd_splitkv api for tile_example_fmha_fwd --------- Co-authored-by: danyao12 <danyao12> Co-authored-by: carlushuang <carlus.huang@amd.com> Co-authored-by: rocking <ChunYu.Lai@amd.com> Co-authored-by: Jing Zhang <jizhan@amd.com> [ROCm/composable_kernel commit:0cb2e06ddc]
This commit is contained in:
Po Yen Chen
@@ -1,12 +1,12 @@
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# generate a list of kernels, but not actually emit files at config stage
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execute_process(
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--direction fwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
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--api fwd,fwd_splitkv --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
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)
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execute_process(
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--direction bwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt
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--api bwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt
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)
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# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS/FMHA_BWD_GEN_BLOBS files must be in the same directory
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@@ -17,13 +17,13 @@ file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt FMHA_BWD_GEN_BLOBS)
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add_custom_command(
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OUTPUT ${FMHA_FWD_GEN_BLOBS}
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--direction fwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
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--api fwd,fwd_splitkv --output_dir ${CMAKE_CURRENT_BINARY_DIR}
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)
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add_custom_command(
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OUTPUT ${FMHA_BWD_GEN_BLOBS}
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COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
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--direction bwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
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--api bwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
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)
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set(EXAMPLE_FMHA_FWD "tile_example_fmha_fwd")
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0
example/ck_tile/01_fmha/codegen/__init__.py
Normal file
0
example/ck_tile/01_fmha/codegen/__init__.py
Normal file
5
example/ck_tile/01_fmha/codegen/cmake_config.py
Normal file
5
example/ck_tile/01_fmha/codegen/cmake_config.py
Normal file
@@ -0,0 +1,5 @@
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# SPDX-License-Identifier: MIT
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# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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# generate kernel instances to speed up compilation
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GEN_DIR = "" # in Cmake, have to generate files in same folder
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92
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
Normal file
92
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
Normal file
@@ -0,0 +1,92 @@
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# SPDX-License-Identifier: MIT
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# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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# generate kernel instances to speed up compilation
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DTYPE_MAP = {
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"fp16": "ck_tile::fp16_t",
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"bf16": "ck_tile::bf16_t",
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"fp8" : "ck_tile::fp8_t"
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}
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MASK_IMPL = {
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"generic" : "ck_tile::GenericAttentionMask",
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"simplified" : "ck_tile::SimplifiedGenericAttentionMask"
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}
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_MASK_SIMPLIFIED_MAP = {
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"s_no" : "ck_tile::SimplifiedGenericAttentionMask<false>",
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"s_mask" : "ck_tile::SimplifiedGenericAttentionMask<true>",
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}
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_MASK_MAP = {
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"no" : "FmhaMasks::NoMask",
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"causal" : "FmhaMasks::CausalMask",
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"generic" : "FmhaMasks::GenericMask"
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}
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def get_mask_map(mask : str):
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if mask == "generic":
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return _MASK_MAP
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elif mask == "simplified":
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return _MASK_SIMPLIFIED_MAP
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else:
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assert False
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return None
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_MASK_CHECK_MAP = {
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"no" : "t.mask_type == mask_enum::no_mask",
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"causal" : "t.mask_type == mask_enum::mask_top_left || t.mask_type == mask_enum::mask_bottom_right",
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"generic" : "t.mask_type == mask_enum::window_generic",
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}
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_MASK_SIMPLIFIED_CHECK_MAP = {
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"s_no" : "t.mask_type == mask_enum::no_mask",
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"s_mask" : "t.mask_type != mask_enum::no_mask",
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}
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def get_mask_check_map(mask : str):
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if mask == "generic":
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return _MASK_CHECK_MAP
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elif mask == "simplified":
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return _MASK_SIMPLIFIED_CHECK_MAP
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else:
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assert False
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return None
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BIAS_MAP = {
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"no" : "ck_tile::BlockAttentionBiasEnum::NO_BIAS",
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"bias" : "ck_tile::BlockAttentionBiasEnum::ELEMENTWISE_BIAS",
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"alibi" : "ck_tile::BlockAttentionBiasEnum::ALIBI"
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}
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# TODO: this is ugly
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BIAS_CHECK_MAP = {
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"no" : "bias_enum::no_bias",
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"bias" : "bias_enum::elementwise_bias",
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"alibi" : "bias_enum::alibi"
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}
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MODE_MAP = {
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"batch" : "false",
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"group" : "true"
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}
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LAYOUT_MAP = {
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"row" : "true",
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"col" : "false"
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}
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PIPELINE_MAP = {
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"qr" : "ck_tile::BlockFmhaPipelineQRKSVS",
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"qr_async" : "ck_tile::BlockFmhaPipelineQRKSVSAsync",
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}
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PIPELINE_ENUM_MAP = {
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"qr" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS",
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"qr_async" : "ck_tile::BlockFmhaPipelineEnum::QRKSVS_ASYNC",
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}
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BOOL_MAP = {
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"t" : "true",
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"f" : "false"
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}
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0
example/ck_tile/01_fmha/codegen/ops/__init__.py
Normal file
0
example/ck_tile/01_fmha/codegen/ops/__init__.py
Normal file
611
example/ck_tile/01_fmha/codegen/ops/fmha_bwd.py
Normal file
611
example/ck_tile/01_fmha/codegen/ops/fmha_bwd.py
Normal file
@@ -0,0 +1,611 @@
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# SPDX-License-Identifier: MIT
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# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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# generate kernel instances to speed up compilation
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import copy
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from dataclasses import dataclass
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import fnmatch
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import itertools
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from pathlib import Path
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from typing import List, Optional, Tuple
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from codegen.cmake_config import *
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from codegen.cpp_symbol_map import *
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BWD_DQDKDV_PIPELINE_MAP = {
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"ks_kts_vr" : "ck_tile::BlockFmhaBwdDQDKDVPipelineKSKTSVR",
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"qs_ks_vr_dos" : "ck_tile::BlockFmhaBwdDQDKDVPipelineQSKSVROGradS",
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"ks_vr" : "ck_tile::BlockFmhaBwdDQDKDVPipelineKSVR",
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}
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BWD_DQDKDV_PIPELINE_ENUM_MAP = {
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"ks_kts_vr" : "ck_tile::BlockFmhaBwdPipelineEnum::KSKTSVR",
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"qs_ks_vr_dos" : "ck_tile::BlockFmhaBwdPipelineEnum::QSKSVROGradS",
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"ks_vr" : "ck_tile::BlockFmhaBwdPipelineEnum::KSVR",
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}
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FMHA_BWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.\n
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// auto generated by generate.py
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#include "fmha_bwd.hpp"
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"""
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FMHA_BWD_DQ_DK_DV_KERNEL_BODY="""
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using fmha_dtype_{F_idx} = {F_dtype};
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using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bk1}, {F_bk2}, {F_bk3}, {F_bk4}, {F_bhdq}, {F_bhdv}>;
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using fmha_block_warps0_{F_idx} = ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>;
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using fmha_block_warps1_{F_idx} = ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>;
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using fmha_block_warps2_{F_idx} = ck_tile::sequence<{F_rm2}, {F_rn2}, {F_rk2}>;
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using fmha_warp_tile_{F_idx} = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
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// TODO: simplify Gemm0~4BlockWarps in TileFmhaBwdShape
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// G0&G2 -> GSdP
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// G1&G3 -> GdKV
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// G4 -> GdQ
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using fmha_bwd_shape_{F_idx} = ck_tile::TileFmhaBwdShape<fmha_block_tile_{F_idx},
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fmha_block_warps0_{F_idx},
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fmha_warp_tile_{F_idx},
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fmha_block_warps1_{F_idx},
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fmha_warp_tile_{F_idx},
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fmha_block_warps0_{F_idx},
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fmha_warp_tile_{F_idx},
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fmha_block_warps1_{F_idx},
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fmha_warp_tile_{F_idx},
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fmha_block_warps2_{F_idx},
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fmha_warp_tile_{F_idx}>;
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using fmha_bwd_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
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{F_skpad},
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{F_dpad},
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{F_dvpad},
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{F_bias},
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{F_dbias},
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false,
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{F_dropout},
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false,
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{F_occupancy}>;
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using fmha_mask_{F_idx} = {F_mask};
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using fmha_bwd_pipeline_problem_{F_idx} = ck_tile::BlockFmhaBwdPipelineProblem<
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::KDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::VDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::GemmDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::LSEDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::AccDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::DDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::BiasDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::RandValOutputDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::OGradDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::QGradDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::KGradDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::VGradDataType,
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typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::BiasGradDataType,
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fmha_bwd_shape_{F_idx},
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{F_mode},
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fmha_mask_{F_idx},
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fmha_bwd_trait_{F_idx}>;
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using fmha_bwd_pipeline_{F_idx} = {F_pipeline}<
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fmha_bwd_pipeline_problem_{F_idx}>;
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using fmha_bwd_dk_epilogue_{F_idx} =
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ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaBwdTypeConfig<{F_dtype}>::AccDataType,
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typename FmhaBwdTypeConfig<{F_dtype}>::KGradDataType,
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false, false>>;
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using fmha_bwd_dv_epilogue_{F_idx} =
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ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaBwdTypeConfig<{F_dtype}>::AccDataType,
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typename FmhaBwdTypeConfig<{F_dtype}>::VGradDataType,
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false, false>>;
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using fmha_bwd_dq_dk_dv_kernel_{F_idx} =
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ck_tile::FmhaBwdDQDKDVKernel<ck_tile::FmhaBwdTilePartitioner<fmha_bwd_shape_{F_idx}>,
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fmha_bwd_pipeline_{F_idx},
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fmha_bwd_dk_epilogue_{F_idx},
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fmha_bwd_dv_epilogue_{F_idx}>;
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using dq_dk_dv_trait_{F_idx} = fmha_bwd_dq_dk_dv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_dbias}, {F_dropout}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
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#include <iostream>
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template<>
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float fmha_bwd_dq_dk_dv_<dq_dk_dv_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
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{{
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using k_ = fmha_bwd_dq_dk_dv_kernel_{F_idx};
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if(s.log_level_ > 0)
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std::cout << ", " << k_::GetName() << std::flush;
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auto [kargs, grids] = fmha_bwd_dq_dk_dv_create_kargs_and_grids<k_>(a);
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constexpr dim3 blocks = k_::BlockSize();
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constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
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return ck_tile::launch_kernel(s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs));
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}}
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template<>
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void fmha_bwd_dq_dk_dv_oneshot_<dq_dk_dv_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
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{{
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using k_ = fmha_bwd_dq_dk_dv_kernel_{F_idx};
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auto [kargs, grids] = fmha_bwd_dq_dk_dv_create_kargs_and_grids<k_>(a);
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constexpr dim3 blocks = k_::BlockSize();
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constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
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ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs)(ck_tile::stream_config{{s.stream_id_}});
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}}
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template<>
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std::string fmha_bwd_dq_dk_dv_get_name_<dq_dk_dv_trait_{F_idx}>()
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{{
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using k_ = fmha_bwd_dq_dk_dv_kernel_{F_idx};
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return k_::GetName();
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}}
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"""
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FMHA_BWD_API_FILENAME="fmha_bwd_api.cpp"
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FMHA_BWD_API="""
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#include <iostream>
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template<typename dot_do_o_trait_, typename dq_dk_dv_trait_>
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float fmha_bwd_(const ck_tile::stream_config& s, fmha_bwd_args a)
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{{
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if(s.log_level_ > 0)
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std::cout << ", " << fmha_bwd_dot_do_o_get_name_<dot_do_o_trait_>() << ", " << fmha_bwd_dq_dk_dv_get_name_<dq_dk_dv_trait_>() << std::flush;
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return ck_tile::launch_kernel(s,
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[=](const ck_tile::stream_config& s_){{ fmha_bwd_dot_do_o_oneshot_<dot_do_o_trait_>(s_, a); }},
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[=](const ck_tile::stream_config& s_){{ fmha_bwd_dq_dk_dv_oneshot_<dq_dk_dv_trait_>(s_, a); }}
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);
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}}
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float fmha_bwd(fmha_bwd_traits t, fmha_bwd_args a, const ck_tile::stream_config& s){{
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float r = -1;
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{F_dispatch}
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return r;
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}}
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"""
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FMHA_BWD_API_PER_DTYPE=""" {F_if}(t.data_type.compare(\"{F_dtype}\") == 0){{
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{F_hdim_case}
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}}
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"""
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FMHA_BWD_API_PER_HDIM_CASE=""" {F_if} (t.hdim_q <= {F_hdim} && t.hdim_v <= {F_hdim}) {{
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{F_inner_dispatch}
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}}
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"""
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|
||||
FMHA_BWD_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_dbias == {F_dbias}) && (t.has_dropout == {F_dropout}) &&
|
||||
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
|
||||
using dq_dk_dv_trait_ = fmha_bwd_dq_dk_dv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_dbias}, {F_dropout}, {F_spad0}, {F_skpad}, {F_dpad}, {F_dvpad}>;
|
||||
using dot_do_o_trait_ = fmha_bwd_dot_do_o_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_spad1}, {F_dvpad}>;
|
||||
r = fmha_bwd_<dot_do_o_trait_, dq_dk_dv_trait_>(s, a);
|
||||
return r;
|
||||
}}
|
||||
"""
|
||||
|
||||
@dataclass
|
||||
class FmhaBwdDQDKDVApiTrait:
|
||||
pipeline : str
|
||||
# sync with fmha_bwd_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 k seqlen
|
||||
bhdq : int # q head_dim
|
||||
bhdv : int # v head_dim
|
||||
mask : str
|
||||
bias : str
|
||||
dbias : str
|
||||
dropout : str
|
||||
spad : str
|
||||
skpad : str
|
||||
dpad : str
|
||||
dvpad : str
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return f'{self.pipeline}-{self.hdim}-{self.dtype}-{self.mode}-{self.mask}-{self.bias}-{self.dbias}-{self.dropout}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}'
|
||||
|
||||
def scheck(self, spad1 : str) -> str:
|
||||
if self.mode == 'group':
|
||||
return 'true' # always support
|
||||
elif self.spad == 't' and spad1 == 't':
|
||||
return f'a.seqlen_q % {self.bm0} != 0'
|
||||
elif self.spad == 'f' and spad1 == 't':
|
||||
return f'a.seqlen_q % {self.bm0} == 0 and a.seqlen_q % 256 != 0' # BlockSize
|
||||
else: # self.skpad == 'f' and skpad1 == 'f'
|
||||
return f'a.seqlen_q % 256 == 0' # BlockSize
|
||||
|
||||
@property
|
||||
def skcheck(self) -> str:
|
||||
if self.mode == 'group':
|
||||
return 'true' # always support
|
||||
elif self.skpad == 't':
|
||||
return f'a.seqlen_k % {self.bn0} != 0'
|
||||
else:
|
||||
return f'a.seqlen_k % {self.bn0} == 0'
|
||||
|
||||
@property
|
||||
def dcheck(self) -> str:
|
||||
if self.dpad == 't': return f'a.hdim_q % {self.bhdq} != 0'
|
||||
else : return f'a.hdim_q % {self.bhdq} == 0'
|
||||
|
||||
@property
|
||||
def dvcheck(self) -> str:
|
||||
if self.dvpad == 't': return f'a.hdim_v % {self.bhdv} != 0'
|
||||
else : return f'a.hdim_v % {self.bhdv} == 0'
|
||||
|
||||
class FmhaBwdApiPool:
|
||||
def __init__(self, mask_impl):
|
||||
self.dq_dk_dv_pool = dict()
|
||||
self.mask_impl = mask_impl
|
||||
|
||||
def register_dq_dk_dv_traits(self, trait : FmhaBwdDQDKDVApiTrait) -> None:
|
||||
# TODO: do we need to check duplication?
|
||||
if trait.dtype not in self.dq_dk_dv_pool.keys():
|
||||
self.dq_dk_dv_pool[trait.dtype] = dict()
|
||||
if trait.hdim not in self.dq_dk_dv_pool[trait.dtype].keys():
|
||||
self.dq_dk_dv_pool[trait.dtype][trait.hdim] = list()
|
||||
|
||||
self.dq_dk_dv_pool[trait.dtype][trait.hdim].append(copy.copy(trait))
|
||||
|
||||
@property
|
||||
def api(self) -> str:
|
||||
per_dtypes=str()
|
||||
for i, dtype in enumerate(self.dq_dk_dv_pool.keys()):
|
||||
per_hdim_case=str()
|
||||
for j, hdim in enumerate(self.dq_dk_dv_pool[dtype].keys()):
|
||||
traits=self.dq_dk_dv_pool[dtype][hdim]
|
||||
inners=str()
|
||||
for k, trait in enumerate(traits):
|
||||
if_k = 'if' if k == 0 else 'else if'
|
||||
for spad1 in ["t", "f"]:
|
||||
if ((spad1 == "f" and trait.spad == "t") or (trait.mode == "group" and spad1 == "f")):
|
||||
continue
|
||||
inners = inners + FMHA_BWD_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_mask=get_mask_map(self.mask_impl)[trait.mask], F_pipeline_enum=BWD_DQDKDV_PIPELINE_ENUM_MAP[trait.pipeline],
|
||||
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_dbias=BOOL_MAP[trait.dbias], F_dropout=BOOL_MAP[trait.dropout],
|
||||
F_scheck=trait.scheck(spad1=spad1), F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype],
|
||||
F_spad0=BOOL_MAP[trait.spad], F_spad1=BOOL_MAP[spad1], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad])
|
||||
|
||||
if_j = 'if' if j == 0 else 'else if'
|
||||
per_hdim_case = per_hdim_case + FMHA_BWD_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_BWD_API_PER_DTYPE.format(F_if=if_i, F_dtype=dtype, F_hdim_case=per_hdim_case)
|
||||
|
||||
return FMHA_BWD_KERNEL_HEADER + FMHA_BWD_API.format(F_dispatch = per_dtypes)
|
||||
|
||||
# GEMM0: Q@K=S^T
|
||||
# GEMM1: P^T@dO^T=dV(This was chosen as G1 to match fwd, but N1 must be equal to headdim_v)
|
||||
# GEMM2: dO@V=dP^T(This was chosen as G2 because of the calculation order)
|
||||
# GEMM3: dS^T@Q^T=dK(Similar to G1, but N3 must be equal to headdim_qk)
|
||||
# GEMM4: dS@K^T=dQ(N4 must be equal to headdim_qk)
|
||||
# Is it necessary to distinguish between K0~K4?
|
||||
@dataclass
|
||||
class FmhaBwdDQDKDVTileSize:
|
||||
F_bm0 : int # tile size along q seqlen (block size)
|
||||
F_bn0 : int # tile size along k seqlen
|
||||
F_bk0 : int # tile size along gemm0 unroll(F_bhdq)
|
||||
F_bk1 : int # tile size along gemm1 unroll(F_bm0)
|
||||
F_bk2 : int # tile size along gemm2 unroll(F_bhdv)
|
||||
F_bk3 : int # tile size along gemm3 unroll(F_bm0)
|
||||
F_bk4 : int # tile size along gemm4 unroll(F_bn0)
|
||||
F_bhdq : int # q head_dim
|
||||
F_bhdv : int # v head_dim
|
||||
F_rm0 : int # number of warps along q seqlen (block warps) in gemm0/gemm2
|
||||
F_rn0 : int # number of warps along k seqlen (block warps) in gemm0/gemm2
|
||||
F_rk0 : int # number of warps along gemm-k (not used) in gemm0/gemm2
|
||||
F_rm1 : int # number of warps along k seqlen (block warps) in gemm1/gemm3
|
||||
F_rn1 : int # number of warps along q seqlen (block warps) in gemm1/gemm3
|
||||
F_rk1 : int # number of warps along gemm-k (not used) in gemm1/gemm3
|
||||
F_rm2 : int # number of warps along k seqlen (block warps) in gemm4
|
||||
F_rn2 : int # number of warps along q seqlen (block warps) in gemm4
|
||||
F_rk2 : int # number of warps along gemm-k (not used) in gemm4
|
||||
F_wm : int # warp size along m (warp size)
|
||||
F_wn : int # warp size along n
|
||||
F_wk : int # warp size along k
|
||||
F_occupancy : int # occupancy
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bk1}x{self.F_bk2}x{self.F_bk3}x{self.F_bk4}x{self.F_bhdq}x{self.F_bhdv}" +\
|
||||
f"_r{self.F_rm0}x{self.F_rn0}x{self.F_rk0}_r{self.F_rm1}x{self.F_rn1}x{self.F_rk1}_r{self.F_rm2}x{self.F_rn2}x{self.F_rk2}" +\
|
||||
f"_w{self.F_wm}x{self.F_wn}x{self.F_wk}_o{self.F_occupancy}"
|
||||
|
||||
@dataclass
|
||||
class FmhaBwdDQDKDVKernel:
|
||||
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 : FmhaBwdDQDKDVTileSize
|
||||
F_spad : str # true/false
|
||||
F_skpad : str #
|
||||
F_dpad : str #
|
||||
F_dvpad : str #
|
||||
F_bias : str #
|
||||
F_dbias : str #
|
||||
F_dropout : str #
|
||||
F_mask : str # value from MASK_MAP
|
||||
F_mode : str # value from MODE_MAP
|
||||
F_pipeline : str
|
||||
mask_impl : str
|
||||
|
||||
@property
|
||||
def template(self) -> str:
|
||||
return FMHA_BWD_KERNEL_HEADER + \
|
||||
FMHA_BWD_DQ_DK_DV_KERNEL_BODY.format(
|
||||
F_idx = self.F_idx,
|
||||
F_hdim = self.F_hdim,
|
||||
F_dtype = DTYPE_MAP[self.F_dtype],
|
||||
F_bm0 = self.F_tile.F_bm0,
|
||||
F_bn0 = self.F_tile.F_bn0,
|
||||
F_bk0 = self.F_tile.F_bk0,
|
||||
F_bk1 = self.F_tile.F_bk1,
|
||||
F_bk2 = self.F_tile.F_bk2,
|
||||
F_bk3 = self.F_tile.F_bk3,
|
||||
F_bk4 = self.F_tile.F_bk4,
|
||||
F_bhdq = self.F_tile.F_bhdq,
|
||||
F_bhdv = self.F_tile.F_bhdv,
|
||||
F_rm0 = self.F_tile.F_rm0,
|
||||
F_rn0 = self.F_tile.F_rn0,
|
||||
F_rk0 = self.F_tile.F_rk0,
|
||||
F_rm1 = self.F_tile.F_rm1,
|
||||
F_rn1 = self.F_tile.F_rn1,
|
||||
F_rk1 = self.F_tile.F_rk1,
|
||||
F_rm2 = self.F_tile.F_rm2,
|
||||
F_rn2 = self.F_tile.F_rn2,
|
||||
F_rk2 = self.F_tile.F_rk2,
|
||||
F_wm = self.F_tile.F_wm,
|
||||
F_wn = self.F_tile.F_wn,
|
||||
F_wk = self.F_tile.F_wk,
|
||||
F_spad = BOOL_MAP[self.F_spad],
|
||||
F_skpad = BOOL_MAP[self.F_skpad],
|
||||
F_dpad = BOOL_MAP[self.F_dpad],
|
||||
F_dvpad = BOOL_MAP[self.F_dvpad],
|
||||
F_bias = BIAS_MAP[self.F_bias],
|
||||
F_dbias = BOOL_MAP[self.F_dbias],
|
||||
F_dropout = BOOL_MAP[self.F_dropout],
|
||||
F_occupancy = self.F_tile.F_occupancy,
|
||||
F_mask = get_mask_map(self.mask_impl)[self.F_mask],
|
||||
F_mode = MODE_MAP[self.F_mode],
|
||||
F_pipeline_enum = BWD_DQDKDV_PIPELINE_ENUM_MAP[self.F_pipeline],
|
||||
F_pipeline = BWD_DQDKDV_PIPELINE_MAP[self.F_pipeline])
|
||||
|
||||
@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"fmha_bwd_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_" + self.F_tile.name
|
||||
if pn != '' : n += f'_{pn}'
|
||||
if self.F_bias != 'no' : n += f'_{self.F_bias}'
|
||||
if self.F_dbias == 't' : n += '_dbias'
|
||||
if self.F_mask[0:2] == 's_':
|
||||
if self.F_mask == 's_mask': n += f'_mask'
|
||||
else:
|
||||
if self.F_mask != 'no' : n += f'_m{self.F_mask[0]}'
|
||||
if self.F_dropout == 't' : n += '_dropout'
|
||||
return n
|
||||
|
||||
@property
|
||||
def filename(self) -> str:
|
||||
return self.name + ".cpp"
|
||||
|
||||
def api_trait(self) -> FmhaBwdDQDKDVApiTrait:
|
||||
return FmhaBwdDQDKDVApiTrait(pipeline=self.F_pipeline,
|
||||
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,
|
||||
bhdq=self.F_tile.F_bhdq,
|
||||
bhdv=self.F_tile.F_bhdv,
|
||||
mask=self.F_mask,
|
||||
bias=self.F_bias,
|
||||
dbias=self.F_dbias,
|
||||
dropout=self.F_dropout,
|
||||
spad=self.F_spad,
|
||||
skpad=self.F_skpad,
|
||||
dpad=self.F_dpad,
|
||||
dvpad=self.F_dvpad)
|
||||
|
||||
# TODO: design a more practical way to do it
|
||||
# this is current supported tile size & pipeline.
|
||||
def get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype : str) -> Optional[dict]:
|
||||
if dtype == 'fp16' or dtype == 'bf16':
|
||||
return {
|
||||
'32' : [FmhaBwdDQDKDVTileSize(128, 128, 32, 32, 32, 32, 32, 32, 32, 1, 4, 1, 4, 1, 1, 4, 1, 1, 32, 32, 16, 1),
|
||||
"qs_ks_vr_dos"],
|
||||
'64' : [FmhaBwdDQDKDVTileSize( 64, 128, 32, 32, 32, 32, 32, 64, 64, 1, 4, 1, 4, 1, 1, 2, 2, 1, 32, 32, 16, 1),
|
||||
"qs_ks_vr_dos"],
|
||||
'128' : [FmhaBwdDQDKDVTileSize( 64, 128, 32, 32, 32, 32, 32, 128, 128, 1, 4, 1, 4, 1, 1, 2, 2, 1, 32, 32, 16, 1),
|
||||
"ks_vr"]
|
||||
}
|
||||
else:
|
||||
return None
|
||||
|
||||
def get_bwd_dq_dk_dv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaBwdApiPool, List[FmhaBwdDQDKDVKernel]]:
|
||||
# TODO: we don't support tuning yet, so pick up one value for pad
|
||||
# support this in future
|
||||
gen = list()
|
||||
api_pool = FmhaBwdApiPool(mask_impl)
|
||||
|
||||
for dtype in DTYPE_MAP.keys():
|
||||
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
|
||||
if d == None:
|
||||
continue
|
||||
for hdim_str, mode, mask, bias, dbias, dropout, spad, skpad, dpad, dvpad in itertools.product(d.keys(), MODE_MAP.keys(), get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"]):
|
||||
tile = d[hdim_str][0]
|
||||
ppl = d[hdim_str][1]
|
||||
hdim = int(hdim_str)
|
||||
if (mode == "group") and (spad == "f" or skpad == "f"):
|
||||
continue
|
||||
if ((bias == "no" or bias == "alibi") and dbias == "t"):
|
||||
continue
|
||||
k = FmhaBwdDQDKDVKernel(F_idx=0, F_hdim=hdim, F_dtype=dtype, F_tile=tile,
|
||||
F_spad=spad, F_skpad=skpad, F_dpad=dpad, F_dvpad=dvpad,
|
||||
F_bias=bias, F_dbias=dbias, F_dropout=dropout, F_mask=mask, F_mode=mode,
|
||||
F_pipeline=ppl, 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 &= bias in ['no', 'alibi']
|
||||
if not cond:
|
||||
continue
|
||||
api_pool.register_dq_dk_dv_traits(k.api_trait())
|
||||
gen.append(k)
|
||||
|
||||
return (api_pool, gen)
|
||||
|
||||
FMHA_BWD_DOT_DO_O_KERNEL_BODY="""
|
||||
using fmha_dtype_{F_idx} = {F_dtype};
|
||||
|
||||
using fmha_bwd_dot_do_o_trait_{F_idx} = ck_tile::TileFmhaBwdOGradDotOTraits<{F_spad},
|
||||
{F_dvpad},
|
||||
{F_occupancy}>;
|
||||
|
||||
using fmha_bwd_dot_do_o_pipeline_problem_{F_idx} = ck_tile::BlockFmhaBwdOGradDotOPipelineProblem<
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::OGradDataType,
|
||||
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::DDataType,
|
||||
/* BlockSize = */ 256,
|
||||
{F_hdim},
|
||||
{F_mode},
|
||||
fmha_bwd_dot_do_o_trait_{F_idx}>;
|
||||
|
||||
using fmha_bwd_dot_do_o_{F_idx} = typename ck_tile::BlockFmhaBwdOGradDotO<
|
||||
fmha_bwd_dot_do_o_pipeline_problem_{F_idx}>;
|
||||
|
||||
using fmha_bwd_dot_do_o_kernel_{F_idx} =
|
||||
ck_tile::FmhaBwdOGradDotOKernel<ck_tile::FmhaBwdOGradDotOTilePartitioner</* BlockSize = */ 256>,
|
||||
fmha_bwd_dot_do_o_{F_idx}>;
|
||||
|
||||
using dot_do_o_trait_{F_idx} = fmha_bwd_dot_do_o_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_spad}, {F_dvpad}>;
|
||||
|
||||
#include <iostream>
|
||||
|
||||
template<>
|
||||
float fmha_bwd_dot_do_o_<dot_do_o_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
|
||||
{{
|
||||
using k_ = fmha_bwd_dot_do_o_kernel_{F_idx};
|
||||
if(s.log_level_ > 0)
|
||||
std::cout << ", " << k_::GetName() << std::flush;
|
||||
auto [kargs, grids] = fmha_bwd_dot_do_o_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));
|
||||
}}
|
||||
|
||||
template<>
|
||||
void fmha_bwd_dot_do_o_oneshot_<dot_do_o_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
|
||||
{{
|
||||
using k_ = fmha_bwd_dot_do_o_kernel_{F_idx};
|
||||
auto [kargs, grids] = fmha_bwd_dot_do_o_create_kargs_and_grids<k_>(a);
|
||||
constexpr dim3 blocks = k_::BlockSize();
|
||||
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
|
||||
ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs)(ck_tile::stream_config{{s.stream_id_}});
|
||||
}}
|
||||
|
||||
template<>
|
||||
std::string fmha_bwd_dot_do_o_get_name_<dot_do_o_trait_{F_idx}>()
|
||||
{{
|
||||
using k_ = fmha_bwd_dot_do_o_kernel_{F_idx};
|
||||
return k_::GetName();
|
||||
}}
|
||||
"""
|
||||
|
||||
@dataclass
|
||||
class FmhaBwdOGradDotOKernel:
|
||||
F_idx : int # this is not a tunable, but a counter to differentiate symbol
|
||||
F_hdim : int # hdim
|
||||
F_dtype : str # data type
|
||||
F_spad : str # true/false
|
||||
F_dvpad : str #
|
||||
F_mode : str # value from MODE_MAP
|
||||
F_occupancy : int
|
||||
|
||||
@property
|
||||
def template(self) -> str:
|
||||
return FMHA_BWD_KERNEL_HEADER + \
|
||||
FMHA_BWD_DOT_DO_O_KERNEL_BODY.format(
|
||||
F_idx = self.F_idx,
|
||||
F_hdim = self.F_hdim,
|
||||
F_dtype = DTYPE_MAP[self.F_dtype],
|
||||
F_spad = BOOL_MAP[self.F_spad],
|
||||
F_dvpad = BOOL_MAP[self.F_dvpad],
|
||||
F_mode = MODE_MAP[self.F_mode],
|
||||
F_occupancy = self.F_occupancy)
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
def pad_name() -> str:
|
||||
n = ''
|
||||
if self.F_spad == 't': n += 's'
|
||||
if self.F_dvpad == 't' : n += 'dv'
|
||||
if n != '' : n = 'p' + n
|
||||
return n
|
||||
pn = pad_name()
|
||||
n = f"fmha_bwd_dot_do_o_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_o{self.F_occupancy}"
|
||||
if pn != '' : n += f'_{pn}'
|
||||
return n
|
||||
|
||||
@property
|
||||
def filename(self) -> str:
|
||||
return self.name + ".cpp"
|
||||
|
||||
def get_bwd_dot_do_o_blobs() -> List[FmhaBwdOGradDotOKernel]:
|
||||
# TODO: we don't support tuning yet, so pick up one value for pad/occupancy
|
||||
# support this in future
|
||||
def get_occupancy(dtype, hdim):
|
||||
return 2
|
||||
|
||||
gen = list()
|
||||
|
||||
for dtype in DTYPE_MAP.keys():
|
||||
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
|
||||
if d == None:
|
||||
continue
|
||||
for hdim_str, mode, spad, dvpad in itertools.product(d.keys(), MODE_MAP.keys(), ["t", "f"], ["t", "f"]):
|
||||
hdim = int(hdim_str)
|
||||
if (mode == "group" and spad == "f"):
|
||||
continue
|
||||
k = FmhaBwdOGradDotOKernel(F_idx=0, F_hdim=hdim, F_dtype=dtype,
|
||||
F_spad=spad, F_dvpad=dvpad, F_mode=mode,
|
||||
F_occupancy=get_occupancy(dtype, hdim))
|
||||
gen.append(k)
|
||||
|
||||
return gen
|
||||
|
||||
def write_single_bwd_dq_dk_dv_kernel(kernel: FmhaBwdDQDKDVKernel, autogen_dir: Path) -> None:
|
||||
(autogen_dir / kernel.filename).write_text(kernel.template)
|
||||
|
||||
def write_single_bwd_dot_do_o_kernel(kernel: FmhaBwdOGradDotOKernel, autogen_dir: Path) -> None:
|
||||
(autogen_dir / kernel.filename).write_text(kernel.template)
|
||||
|
||||
def write_bwd_api(api_pool : FmhaBwdApiPool, autogen_dir: Path) -> None:
|
||||
(autogen_dir / FMHA_BWD_API_FILENAME).write_text(api_pool.api)
|
||||
|
||||
def write_blobs(output_dir : Path, kernel_filter : Optional[str], receipt, mask_impl) -> None:
|
||||
kernels = get_bwd_dot_do_o_blobs()
|
||||
for kernel in kernels:
|
||||
write_single_bwd_dot_do_o_kernel(kernel, output_dir)
|
||||
api_pool, kernels = get_bwd_dq_dk_dv_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
write_single_bwd_dq_dk_dv_kernel(kernel, output_dir)
|
||||
write_bwd_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_bwd_dot_do_o_blobs()
|
||||
for kernel in kernels:
|
||||
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
|
||||
_, kernels = get_bwd_dq_dk_dv_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_BWD_API_FILENAME) + "\n")
|
||||
498
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
Normal file
498
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
Normal file
@@ -0,0 +1,498 @@
|
||||
# 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 *
|
||||
|
||||
|
||||
DTYPE_BITS = {
|
||||
"fp32": 32,
|
||||
"fp16": 16,
|
||||
"bf16": 16,
|
||||
"fp8" : 8,
|
||||
"bf8" : 8
|
||||
}
|
||||
|
||||
TILE_PARTITIONER_MAP = {
|
||||
"shb" : "ck_tile::FmhaFwdTilePartitioner_SHB",
|
||||
"hbs" : "ck_tile::FmhaFwdTilePartitioner_HBS",
|
||||
}
|
||||
|
||||
FMHA_FWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.\n
|
||||
// auto generated by generate.py
|
||||
#include "fmha_fwd.hpp"
|
||||
"""
|
||||
|
||||
FMHA_FWD_KERNEL_BODY="""
|
||||
using fmha_dtype_{F_idx} = {F_dtype};
|
||||
|
||||
using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}>;
|
||||
using fmha_block_warps_{F_idx} = ck_tile::sequence<{F_rm}, {F_rn}, {F_rk}>;
|
||||
using fmha_warp_tile_{F_idx} = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
|
||||
|
||||
using fmha_shape_{F_idx} = ck_tile::TileFmhaShape<fmha_block_tile_{F_idx},
|
||||
fmha_block_warps_{F_idx},
|
||||
fmha_warp_tile_{F_idx},
|
||||
fmha_block_warps_{F_idx},
|
||||
fmha_warp_tile_{F_idx},
|
||||
{F_vlayout}>;
|
||||
|
||||
using fmha_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
|
||||
{F_skpad},
|
||||
{F_dpad},
|
||||
{F_dvpad},
|
||||
{F_bias},
|
||||
false,
|
||||
{F_lse},
|
||||
{F_dropout},
|
||||
{F_squant},
|
||||
{F_occupancy}>;
|
||||
using fmha_mask_{F_idx} = {F_mask};
|
||||
|
||||
using fmha_pipeline_problem_{F_idx} = ck_tile::BlockFmhaPipelineProblem<
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::KDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::VDataType,
|
||||
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,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
|
||||
fmha_shape_{F_idx},
|
||||
{F_mode},
|
||||
fmha_mask_{F_idx},
|
||||
fmha_trait_{F_idx}>;
|
||||
|
||||
using fmha_pipeline_{F_idx} = {F_pipeline}<
|
||||
fmha_pipeline_problem_{F_idx}>;
|
||||
|
||||
using fmha_epilogue_{F_idx} =
|
||||
ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType,
|
||||
typename FmhaFwdTypeConfig<{F_dtype}>::ODataType,
|
||||
{F_spad}, {F_dvpad}>>;
|
||||
|
||||
using fmha_kernel_{F_idx} =
|
||||
ck_tile::FmhaFwdKernel<{F_tile_partitioner}<fmha_shape_{F_idx}>,
|
||||
fmha_pipeline_{F_idx},
|
||||
fmha_epilogue_{F_idx}>;
|
||||
|
||||
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}>;
|
||||
|
||||
#include <iostream>
|
||||
|
||||
template<>
|
||||
float fmha_fwd_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_args a)
|
||||
{{
|
||||
using k_ = fmha_kernel_{F_idx};
|
||||
if(s.log_level_ > 0)
|
||||
std::cout << ", " << k_::GetName() << std::flush;
|
||||
auto [kargs, grids] = fmha_fwd_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_API_FILENAME="fmha_fwd_api.cpp"
|
||||
FMHA_FWD_API="""
|
||||
float fmha_fwd(fmha_fwd_traits t, fmha_fwd_args a, const ck_tile::stream_config& s){{
|
||||
float r = -1;
|
||||
{F_dispatch}
|
||||
return r;
|
||||
}}
|
||||
"""
|
||||
|
||||
FMHA_FWD_API_PER_DTYPE=""" {F_if}(t.data_type.compare(\"{F_dtype}\") == 0){{
|
||||
{F_hdim_case}
|
||||
}}
|
||||
"""
|
||||
FMHA_FWD_API_PER_HDIM_CASE=""" {F_if} (t.hdim_q <= {F_hdim} && t.hdim_v <= {F_hdim}) {{
|
||||
{F_inner_dispatch}
|
||||
}}
|
||||
"""
|
||||
|
||||
FMHA_FWD_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 trait_ = 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}>;
|
||||
return fmha_fwd_<trait_>(s, a);
|
||||
}}
|
||||
"""
|
||||
|
||||
@dataclass
|
||||
class FmhaFwdApiTrait:
|
||||
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 #
|
||||
dropout : str
|
||||
squant : str #
|
||||
spad : str
|
||||
skpad : str
|
||||
dpad : str
|
||||
dvpad : 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.dropout}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}'
|
||||
|
||||
@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 FmhaFwdPipeline:
|
||||
tag : str
|
||||
|
||||
F_vlayout : str # row/col
|
||||
F_spad : str # true/false
|
||||
F_skpad : str #
|
||||
F_dpad : str #
|
||||
F_dvpad : str #
|
||||
F_bias : str # true/false
|
||||
F_lse : str #
|
||||
F_dropout : str #
|
||||
F_squant : str #
|
||||
F_mask : str # value from MASK_MAP
|
||||
|
||||
@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'{self.tag}_v{self.F_vlayout[0]}'
|
||||
if pn != '' : n += f'_{pn}'
|
||||
if self.F_bias != 'no' : n += f'_{self.F_bias}'
|
||||
if self.F_mask[0:2] == 's_':
|
||||
if self.F_mask == 's_mask': n += f'_mask'
|
||||
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'
|
||||
return n
|
||||
|
||||
class FmhaFwdApiPool:
|
||||
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_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_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])
|
||||
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_API.format(F_dispatch = per_dtypes)
|
||||
|
||||
@dataclass
|
||||
class FmhaFwdTileSize:
|
||||
F_bm0 : int # tile size along q seqlen (block size)
|
||||
F_bn0 : int # tile size along k seqlen
|
||||
F_bk0 : int # tile size along qk gemm unroll
|
||||
F_bn1 : int # tile size along v head_dim
|
||||
F_bk1 : int # tile size along kv gemm unroll
|
||||
F_bk0blen : int # total length of K0, used for pipeline that need load Q at once (or repeately load Q as a whole tile)
|
||||
F_rm : int # number of warps along q seqlen (block warps)
|
||||
F_rn : int # number of warps along k seqlen(not used)
|
||||
F_rk : int # number of warps along gemm-k(not used)
|
||||
F_wm : int # warp size along m (warp size)
|
||||
F_wn : int # warp size along n
|
||||
F_wk : int # warp size along k
|
||||
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_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bn1}x{self.F_bk1}x{self.F_bk0blen}" +\
|
||||
f"_r{self.F_rm}x{self.F_rn}x{self.F_rk}_w{self.F_wm}x{self.F_wn}x{self.F_wk}" +\
|
||||
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
|
||||
|
||||
@dataclass
|
||||
class FmhaFwdKernel:
|
||||
F_idx : int # this is not a tunable, but a counter to differentiate symbol
|
||||
F_hdim : int # hdim
|
||||
F_dtype : str # data type
|
||||
F_mode : str # value from MODE_MAP
|
||||
F_tile : FmhaFwdTileSize
|
||||
F_pipeline : FmhaFwdPipeline
|
||||
mask_impl : str
|
||||
|
||||
def get_tp(self) -> str:
|
||||
if self.F_mode == 'group':
|
||||
return 'hbs'
|
||||
else:
|
||||
return 'shb'
|
||||
|
||||
@property
|
||||
def template(self) -> str:
|
||||
kernel_body = str()
|
||||
return FMHA_FWD_KERNEL_HEADER + \
|
||||
FMHA_FWD_KERNEL_BODY.format(
|
||||
F_idx = self.F_idx,
|
||||
F_hdim = self.F_hdim,
|
||||
F_dtype = DTYPE_MAP[self.F_dtype],
|
||||
F_bm0 = self.F_tile.F_bm0,
|
||||
F_bn0 = self.F_tile.F_bn0,
|
||||
F_bk0 = self.F_tile.F_bk0,
|
||||
F_bn1 = self.F_tile.F_bn1,
|
||||
F_bk1 = self.F_tile.F_bk1,
|
||||
F_bk0blen = self.F_tile.F_bk0blen,
|
||||
F_rm = self.F_tile.F_rm,
|
||||
F_rn = self.F_tile.F_rn,
|
||||
F_rk = self.F_tile.F_rk,
|
||||
F_wm = self.F_tile.F_wm,
|
||||
F_wn = self.F_tile.F_wn,
|
||||
F_wk = self.F_tile.F_wk,
|
||||
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_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_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],
|
||||
F_mode = MODE_MAP[self.F_mode],
|
||||
F_pipeline = PIPELINE_MAP[self.F_pipeline.tag],
|
||||
F_tile_partitioner = TILE_PARTITIONER_MAP[self.get_tp()])
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
# TODO: we don't encode idx here
|
||||
return f"fmha_fwd_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_{self.get_tp()}_" + \
|
||||
self.F_tile.name + '_' + self.F_pipeline.name
|
||||
|
||||
@property
|
||||
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]:
|
||||
if dtype == 'fp16' or dtype == 'bf16':
|
||||
return {
|
||||
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 32, 32, 16, -1),
|
||||
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 32, 32, 16, -1),
|
||||
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 16, -1),
|
||||
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 16, -1),
|
||||
}
|
||||
elif dtype == 'fp8' or dtype == 'bf8':
|
||||
return {
|
||||
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 32, 32, 32, -1),
|
||||
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 32, -1),
|
||||
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 32, -1)
|
||||
}
|
||||
else:
|
||||
return None
|
||||
|
||||
def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdApiPool, List[FmhaFwdKernel]]:
|
||||
# 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[FmhaFwdPipeline]:
|
||||
# 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']:
|
||||
for mask, bias, lse, dropout in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"]):
|
||||
if hdim == 256:
|
||||
# if True:
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, dropout, squant, mask))
|
||||
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
else:
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(FmhaFwdPipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
if receipt == 1:
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'row', 't', 't', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, dropout, squant, mask)) # TODO: cover arbitraty hdim
|
||||
elif dtype in ['fp8', 'bf8']:
|
||||
# no need lse/dropout kernels
|
||||
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
|
||||
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', 'f', squant, mask))
|
||||
else:
|
||||
assert False
|
||||
return pipelines
|
||||
|
||||
gen = list()
|
||||
api_pool = FmhaFwdApiPool(mask_impl)
|
||||
|
||||
for dtype in DTYPE_MAP.keys():
|
||||
d = get_fmha_fwd_tile_dict_from_dtype(dtype)
|
||||
if d == None:
|
||||
continue
|
||||
#for hdim_str, mode, mask, bias, lse in itertools.product(d.keys(), MODE_MAP.keys(), MASK_MAP.keys(), ["t", "f"], ["t", "f"]):
|
||||
for hdim_str, mode in itertools.product(d.keys(), MODE_MAP.keys()):
|
||||
tile = d[hdim_str]
|
||||
hdim = int(hdim_str)
|
||||
for pipeline in get_pipelines(dtype, hdim):
|
||||
if mode == "group":
|
||||
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
|
||||
k = FmhaFwdKernel(F_idx=0,
|
||||
F_hdim=hdim,
|
||||
F_dtype=dtype,
|
||||
F_mode=mode,
|
||||
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'
|
||||
cond &= pipeline.F_bias in ['no', 'alibi']
|
||||
cond &= pipeline.F_squant == 'f'
|
||||
if not cond:
|
||||
continue
|
||||
api_pool.register_traits(k.api_trait())
|
||||
gen.append(k)
|
||||
|
||||
return (api_pool, gen)
|
||||
|
||||
def write_single_fwd_kernel(kernel: FmhaFwdKernel, autogen_dir: Path) -> None:
|
||||
(autogen_dir / kernel.filename).write_text(kernel.template)
|
||||
|
||||
def write_fwd_api(api_pool : FmhaFwdApiPool, autogen_dir: Path) -> None:
|
||||
(autogen_dir / FMHA_FWD_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_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
write_single_fwd_kernel(kernel, output_dir)
|
||||
write_fwd_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_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_API_FILENAME) + "\n")
|
||||
671
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
Normal file
671
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
Normal file
@@ -0,0 +1,671 @@
|
||||
# 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, Union
|
||||
|
||||
from codegen.cmake_config import *
|
||||
from codegen.cpp_symbol_map import *
|
||||
|
||||
from codegen.ops.fmha_fwd import (
|
||||
FmhaFwdTileSize,
|
||||
FmhaFwdApiTrait,
|
||||
FMHA_FWD_KERNEL_HEADER,
|
||||
FMHA_FWD_API_PER_DTYPE,
|
||||
FMHA_FWD_API_PER_HDIM_CASE,
|
||||
)
|
||||
|
||||
|
||||
FMHA_FWD_SPLITKV_PIPELINE_MAP = {
|
||||
"qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS",
|
||||
"qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync",
|
||||
}
|
||||
|
||||
FMHA_FWD_SPLITKV_KERNEL_BODY="""
|
||||
using fmha_dtype_{F_idx} = {F_dtype};
|
||||
using fmha_mask_{F_idx} = {F_mask};
|
||||
|
||||
namespace {{
|
||||
template <bool kHasUnevenSplits>
|
||||
struct kernel_runner {{
|
||||
using fmha_block_tile = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}>;
|
||||
using fmha_block_warps = ck_tile::sequence<{F_rm}, {F_rn}, {F_rk}>;
|
||||
using fmha_warp_tile = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
|
||||
|
||||
using fmha_shape = ck_tile::TileFmhaShape<fmha_block_tile,
|
||||
fmha_block_warps,
|
||||
fmha_warp_tile,
|
||||
fmha_block_warps,
|
||||
fmha_warp_tile,
|
||||
{F_vlayout}>;
|
||||
|
||||
using fmha_trait = ck_tile::TileFmhaFwdSplitKVTraits<{F_spad},
|
||||
{F_skpad},
|
||||
{F_dpad},
|
||||
{F_dvpad},
|
||||
{F_bias},
|
||||
false,
|
||||
{F_lse},
|
||||
{F_dropout},
|
||||
{F_squant},
|
||||
kHasUnevenSplits,
|
||||
{F_occupancy}>;
|
||||
|
||||
using fmha_pipeline_problem = ck_tile::BlockFmhaFwdSplitKVPipelineProblem<
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::KDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::VDataType,
|
||||
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,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
|
||||
fmha_shape,
|
||||
{F_mode},
|
||||
fmha_mask_{F_idx},
|
||||
fmha_trait>;
|
||||
|
||||
using fmha_pipeline = {F_pipeline}<
|
||||
fmha_pipeline_problem>;
|
||||
|
||||
using fmha_epilogue =
|
||||
ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType,
|
||||
typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType,
|
||||
{F_spad}, {F_dvpad}>>;
|
||||
|
||||
using fmha_kernel =
|
||||
ck_tile::FmhaFwdSplitKVKernel<ck_tile::FmhaFwdSplitKVTilePartitioner<fmha_shape>,
|
||||
fmha_pipeline,
|
||||
fmha_epilogue>;
|
||||
|
||||
static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
|
||||
{{
|
||||
using k_ = fmha_kernel;
|
||||
auto [kargs, grids] = fmha_fwd_splitkv_create_kargs_and_grids<k_>(a);
|
||||
constexpr dim3 blocks = k_::BlockSize();
|
||||
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
|
||||
ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs)(ck_tile::stream_config{{s.stream_id_}});
|
||||
}}
|
||||
}};
|
||||
}}
|
||||
|
||||
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}>;
|
||||
|
||||
#include <iostream>
|
||||
|
||||
template<>
|
||||
void fmha_fwd_splitkv_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_args a)
|
||||
{{
|
||||
if constexpr({F_mode} == false) {{ // batch mode
|
||||
if (a.seqlen_k % (a.num_splits * {F_bn0}) == 0) {{
|
||||
kernel_runner<false>::run(s, a);
|
||||
}} else {{
|
||||
kernel_runner<true>::run(s, a);
|
||||
}}
|
||||
}} else {{
|
||||
kernel_runner<true>::run(s, a);
|
||||
}}
|
||||
}}
|
||||
|
||||
template<>
|
||||
std::string fmha_fwd_splitkv_get_name_<trait_{F_idx}>()
|
||||
{{
|
||||
using k_ = kernel_runner<true>::fmha_kernel; /// FIXME: choose real kernel type
|
||||
return k_::GetName();
|
||||
}}
|
||||
"""
|
||||
|
||||
FMHA_FWD_SPLITKV_COMBINE_KERNEL_BODY="""
|
||||
using fmha_dtype_{F_idx} = {F_dtype};
|
||||
|
||||
namespace {{
|
||||
template <ck_tile::index_t kLogMaxSplits>
|
||||
struct kernel_runner {{
|
||||
using fmha_trait = ck_tile::TileFmhaFwdSplitKVCombineTraits<{F_spad},
|
||||
{F_dvpad},
|
||||
{F_lse},
|
||||
{F_squant},
|
||||
kLogMaxSplits,
|
||||
{F_occupancy}>;
|
||||
|
||||
using fmha_pipeline_problem = ck_tile::BlockFmhaSplitKVCombinePipelineProblem<
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::LSEDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::OaccDataType,
|
||||
typename FmhaFwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
|
||||
{F_hdim},
|
||||
{F_bm0},
|
||||
{F_bn1},
|
||||
{F_mode},
|
||||
fmha_trait>;
|
||||
|
||||
using fmha_pipeline = ck_tile::BlockFmhaFwdSplitKVCombinePipeline<
|
||||
fmha_pipeline_problem>;
|
||||
|
||||
using fmha_epilogue =
|
||||
ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType,
|
||||
typename FmhaFwdTypeConfig<{F_dtype}>::ODataType,
|
||||
{F_spad}, {F_dvpad}>>;
|
||||
|
||||
using fmha_kernel =
|
||||
ck_tile::FmhaFwdSplitKVCombineKernel<ck_tile::FmhaFwdSplitKVCombineTilePartitioner<{F_bm0}, {F_bn1}>,
|
||||
fmha_pipeline,
|
||||
fmha_epilogue>;
|
||||
|
||||
static void run(const ck_tile::stream_config& s, fmha_fwd_args a)
|
||||
{{
|
||||
using k_ = fmha_kernel;
|
||||
auto [kargs, grids] = fmha_fwd_splitkv_combine_create_kargs_and_grids<k_>(a);
|
||||
constexpr dim3 blocks = k_::BlockSize();
|
||||
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
|
||||
ck_tile::make_kernel<blocks.x, kBlockPerCu>(k_{{}}, grids, blocks, 0, kargs)(ck_tile::stream_config{{s.stream_id_}});
|
||||
}}
|
||||
}};
|
||||
}}
|
||||
|
||||
using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn1},
|
||||
{F_lse}, {F_squant}, {F_spad}, {F_dvpad}>;
|
||||
|
||||
#include <iostream>
|
||||
|
||||
template<>
|
||||
void fmha_fwd_splitkv_combine_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_args a)
|
||||
{{
|
||||
if (a.num_splits <= 16) {{
|
||||
kernel_runner<4>::run(s, a);
|
||||
}} else if (a.num_splits <= 32) {{
|
||||
kernel_runner<5>::run(s, a);
|
||||
}} else if (a.num_splits <= 64) {{
|
||||
kernel_runner<6>::run(s, a);
|
||||
}} else if (a.num_splits <= 128) {{
|
||||
kernel_runner<7>::run(s, a);
|
||||
}}
|
||||
}}
|
||||
|
||||
template<>
|
||||
std::string fmha_fwd_splitkv_combine_get_name_<trait_{F_idx}>()
|
||||
{{
|
||||
using k_ = kernel_runner<6>::fmha_kernel; /// FIXME: choose real kernel type
|
||||
return k_::GetName();
|
||||
}}
|
||||
"""
|
||||
|
||||
FMHA_FWD_SPLITKV_API_FILENAME="fmha_fwd_splitkv_api.cpp"
|
||||
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)
|
||||
{{
|
||||
if(s.log_level_ > 0)
|
||||
std::cout
|
||||
<< ", " << fmha_fwd_splitkv_get_name_<fmha_fwd_splitkv_traits_>()
|
||||
<< ", " << fmha_fwd_splitkv_combine_get_name_<fmha_fwd_splitkv_combine_traits_>()
|
||||
<< std::flush;
|
||||
|
||||
return ck_tile::launch_kernel(s,
|
||||
[=](const ck_tile::stream_config& s_){{ fmha_fwd_splitkv_oneshot_<fmha_fwd_splitkv_traits_>(s_, a); }},
|
||||
[=](const ck_tile::stream_config& s_){{ fmha_fwd_splitkv_combine_oneshot_<fmha_fwd_splitkv_combine_traits_>(s_, a); }}
|
||||
);
|
||||
}}
|
||||
|
||||
float fmha_fwd_splitkv(fmha_fwd_traits t, fmha_fwd_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}>;
|
||||
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 FmhaFwdSplitKVPipeline:
|
||||
tag : str
|
||||
|
||||
F_vlayout : str # row/col
|
||||
F_spad : str # true/false
|
||||
F_skpad : str #
|
||||
F_dpad : str #
|
||||
F_dvpad : str #
|
||||
F_bias : str # true/false
|
||||
F_lse : str #
|
||||
F_dropout : str #
|
||||
F_squant : str #
|
||||
F_mask : str # value from MASK_MAP
|
||||
|
||||
@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'{self.tag}_v{self.F_vlayout[0]}'
|
||||
if pn != '' : n += f'_{pn}'
|
||||
if self.F_bias != 'no' : n += f'_{self.F_bias}'
|
||||
if self.F_mask[0:2] == 's_':
|
||||
if self.F_mask == 's_mask': n += f'_mask'
|
||||
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'
|
||||
return n
|
||||
|
||||
@dataclass
|
||||
class FmhaFwdSplitKVCombinePipeline:
|
||||
tag : str
|
||||
|
||||
F_spad : str # true/false
|
||||
F_dvpad : str #
|
||||
F_lse : str #
|
||||
F_squant : str #
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
def pad_name() -> str:
|
||||
n = ''
|
||||
if self.F_spad == 't': n += 's'
|
||||
if self.F_dvpad == 't' : n += 'dv'
|
||||
if n != '' : n = 'p' + n
|
||||
return n
|
||||
pn = pad_name()
|
||||
n = f'{self.tag}'
|
||||
if pn != '' : n += f'_{pn}'
|
||||
if self.F_lse == 't' : n += '_lse'
|
||||
if self.F_squant == 't' : n += '_squant'
|
||||
return n
|
||||
|
||||
class FmhaFwdSplitKVApiPool:
|
||||
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_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_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])
|
||||
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_SPLITKV_API.format(F_dispatch = per_dtypes)
|
||||
|
||||
@dataclass
|
||||
class FmhaFwdSplitKVCombineTileSize:
|
||||
F_bm0 : int # tile size along q seqlen
|
||||
F_bn1 : int # tile size along v head_dim
|
||||
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_bm0}x{self.F_bn1}" +\
|
||||
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
|
||||
|
||||
@dataclass
|
||||
class FmhaFwdSplitKVKernel:
|
||||
F_idx : int # this is not a tunable, but a counter to differentiate symbol
|
||||
F_hdim : int # hdim
|
||||
F_dtype : str # data type
|
||||
F_mode : str # value from MODE_MAP
|
||||
F_tile : FmhaFwdTileSize
|
||||
F_pipeline : FmhaFwdSplitKVPipeline
|
||||
mask_impl : str
|
||||
|
||||
@property
|
||||
def template(self) -> str:
|
||||
kernel_body = str()
|
||||
return FMHA_FWD_KERNEL_HEADER + \
|
||||
FMHA_FWD_SPLITKV_KERNEL_BODY.format(
|
||||
F_idx = self.F_idx,
|
||||
F_hdim = self.F_hdim,
|
||||
F_dtype = DTYPE_MAP[self.F_dtype],
|
||||
F_bm0 = self.F_tile.F_bm0,
|
||||
F_bn0 = self.F_tile.F_bn0,
|
||||
F_bk0 = self.F_tile.F_bk0,
|
||||
F_bn1 = self.F_tile.F_bn1,
|
||||
F_bk1 = self.F_tile.F_bk1,
|
||||
F_bk0blen = self.F_tile.F_bk0blen,
|
||||
F_rm = self.F_tile.F_rm,
|
||||
F_rn = self.F_tile.F_rn,
|
||||
F_rk = self.F_tile.F_rk,
|
||||
F_wm = self.F_tile.F_wm,
|
||||
F_wn = self.F_tile.F_wn,
|
||||
F_wk = self.F_tile.F_wk,
|
||||
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_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_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],
|
||||
F_mode = MODE_MAP[self.F_mode],
|
||||
F_pipeline = FMHA_FWD_SPLITKV_PIPELINE_MAP[self.F_pipeline.tag])
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
# TODO: we don't encode idx here
|
||||
return f"fmha_fwd_splitkv_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_" + \
|
||||
self.F_tile.name + '_' + self.F_pipeline.name
|
||||
|
||||
@property
|
||||
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)
|
||||
|
||||
@dataclass
|
||||
class FmhaFwdSplitKVCombineKernel:
|
||||
F_idx : int # this is not a tunable, but a counter to differentiate symbol
|
||||
F_hdim : int # hdim
|
||||
F_dtype : str # data type
|
||||
F_mode : str # value from MODE_MAP
|
||||
F_tile : FmhaFwdSplitKVCombineTileSize
|
||||
F_pipeline : FmhaFwdSplitKVCombinePipeline
|
||||
|
||||
@property
|
||||
def template(self) -> str:
|
||||
kernel_body = str()
|
||||
return FMHA_FWD_KERNEL_HEADER + \
|
||||
FMHA_FWD_SPLITKV_COMBINE_KERNEL_BODY.format(
|
||||
F_idx = self.F_idx,
|
||||
F_hdim = self.F_hdim,
|
||||
F_dtype = DTYPE_MAP[self.F_dtype],
|
||||
F_bm0 = self.F_tile.F_bm0,
|
||||
F_bn1 = self.F_tile.F_bn1,
|
||||
F_spad = BOOL_MAP[self.F_pipeline.F_spad],
|
||||
F_dvpad = BOOL_MAP[self.F_pipeline.F_dvpad],
|
||||
F_lse = BOOL_MAP[self.F_pipeline.F_lse],
|
||||
F_squant = BOOL_MAP[self.F_pipeline.F_squant],
|
||||
F_occupancy = self.F_tile.F_occupancy,
|
||||
F_mode = MODE_MAP[self.F_mode])
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
# TODO: we don't encode idx here
|
||||
return f"fmha_fwd_splitkv_combine_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_" + \
|
||||
self.F_tile.name + '_' + self.F_pipeline.name
|
||||
|
||||
@property
|
||||
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]:
|
||||
if dtype == 'fp16' or dtype == 'bf16':
|
||||
return {
|
||||
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 32, 32, 16, -1),
|
||||
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 32, 32, 16, -1),
|
||||
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 16, -1),
|
||||
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 16, -1),
|
||||
}
|
||||
elif dtype == 'fp8' or dtype == 'bf8':
|
||||
return {
|
||||
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 32, 32, 32, -1),
|
||||
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 32, -1),
|
||||
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 32, -1)
|
||||
}
|
||||
else:
|
||||
return None
|
||||
|
||||
def get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
|
||||
if dtype == 'fp16' or dtype == 'bf16':
|
||||
return {
|
||||
'32' : FmhaFwdSplitKVCombineTileSize(64, 32, -1),
|
||||
'64' : FmhaFwdSplitKVCombineTileSize(64, 64, -1),
|
||||
'128' : FmhaFwdSplitKVCombineTileSize(64, 128, -1),
|
||||
'256' : FmhaFwdSplitKVCombineTileSize(64, 256, -1),
|
||||
}
|
||||
elif dtype == 'fp8' or dtype == 'bf8':
|
||||
return {
|
||||
'64' : FmhaFwdSplitKVCombineTileSize(64, 64, -1),
|
||||
'128' : FmhaFwdSplitKVCombineTileSize(64, 128, -1),
|
||||
'256' : FmhaFwdSplitKVCombineTileSize(64, 256, -1),
|
||||
}
|
||||
else:
|
||||
return None
|
||||
|
||||
def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdSplitKVApiPool, List[FmhaFwdSplitKVKernel]]:
|
||||
Pipeline = FmhaFwdSplitKVPipeline
|
||||
Kernel = FmhaFwdSplitKVKernel
|
||||
|
||||
# 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[FmhaFwdSplitKVPipeline]:
|
||||
# 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']:
|
||||
# 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:
|
||||
# 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', 't', 't', 't', 't', bias, lse, dropout, squant, mask))
|
||||
pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, dropout, squant, 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))
|
||||
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
|
||||
elif dtype in ['fp8', 'bf8']:
|
||||
# no need lse/dropout 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))
|
||||
else:
|
||||
assert False
|
||||
return pipelines
|
||||
|
||||
gen = list()
|
||||
api_pool = FmhaFwdSplitKVApiPool(mask_impl)
|
||||
|
||||
for dtype in DTYPE_MAP.keys():
|
||||
d = get_fmha_fwd_tile_dict_from_dtype(dtype)
|
||||
if d == None:
|
||||
continue
|
||||
#for hdim_str, mode, mask, bias, lse in itertools.product(d.keys(), MODE_MAP.keys(), MASK_MAP.keys(), ["t", "f"], ["t", "f"]):
|
||||
for hdim_str, mode in itertools.product(d.keys(), MODE_MAP.keys()):
|
||||
tile = d[hdim_str]
|
||||
hdim = int(hdim_str)
|
||||
for pipeline in get_pipelines(dtype, hdim):
|
||||
if mode == "group":
|
||||
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
|
||||
k = Kernel(F_idx=0,
|
||||
F_hdim=hdim,
|
||||
F_dtype=dtype,
|
||||
F_mode=mode,
|
||||
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'
|
||||
cond &= pipeline.F_bias in ['no', 'alibi']
|
||||
cond &= pipeline.F_squant == 'f'
|
||||
if not cond:
|
||||
continue
|
||||
api_pool.register_traits(k.api_trait())
|
||||
gen.append(k)
|
||||
|
||||
return (api_pool, gen)
|
||||
|
||||
def get_fwd_splitkv_combine_blobs(kernel_filter : Optional[str], receipt) -> List[FmhaFwdSplitKVCombineKernel]:
|
||||
Pipeline = FmhaFwdSplitKVCombinePipeline
|
||||
Kernel = FmhaFwdSplitKVCombineKernel
|
||||
|
||||
# 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[FmhaFwdSplitKVCombinePipeline]:
|
||||
# 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']:
|
||||
for spad, dvpad, lse in itertools.product(["t", "f"], ["t", "f"], ["t", "f"]):
|
||||
pipelines.append(Pipeline('unused', spad, dvpad, lse, squant))
|
||||
elif dtype in ['fp8', 'bf8']:
|
||||
# no need lse kernels
|
||||
pipelines.append(Pipeline('unused', 'f', 'f', 'f', squant))
|
||||
else:
|
||||
assert False
|
||||
return pipelines
|
||||
|
||||
gen = list()
|
||||
|
||||
for dtype in DTYPE_MAP.keys():
|
||||
d = get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype)
|
||||
if d == None:
|
||||
continue
|
||||
#for hdim_str, mode, mask, bias, lse in itertools.product(d.keys(), MODE_MAP.keys(), MASK_MAP.keys(), ["t", "f"], ["t", "f"]):
|
||||
for hdim_str, mode in itertools.product(d.keys(), MODE_MAP.keys()):
|
||||
tile = d[hdim_str]
|
||||
hdim = int(hdim_str)
|
||||
for pipeline in get_pipelines(dtype, hdim):
|
||||
if mode == "group":
|
||||
if pipeline.F_spad != 't':
|
||||
# in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not
|
||||
continue
|
||||
k = Kernel(F_idx=0,
|
||||
F_hdim=hdim,
|
||||
F_dtype=dtype,
|
||||
F_mode=mode,
|
||||
F_tile=tile,
|
||||
F_pipeline=pipeline)
|
||||
if kernel_filter != None:
|
||||
if not fnmatch.fnmatch(k.name, kernel_filter):
|
||||
continue
|
||||
gen.append(k)
|
||||
|
||||
return gen
|
||||
|
||||
def write_single_kernel(kernel: Union[FmhaFwdSplitKVKernel, FmhaFwdSplitKVCombineKernel], autogen_dir: Path) -> None:
|
||||
(autogen_dir / kernel.filename).write_text(kernel.template)
|
||||
|
||||
def write_fwd_splitkv_api(api_pool : FmhaFwdSplitKVApiPool, autogen_dir: Path) -> None:
|
||||
file_path = autogen_dir / FMHA_FWD_SPLITKV_API_FILENAME
|
||||
file_path.write_text(api_pool.api)
|
||||
|
||||
def write_blobs(output_dir : Path, kernel_filter : Optional[str], receipt, mask_impl) -> None:
|
||||
kernels = get_fwd_splitkv_combine_blobs(kernel_filter, receipt)
|
||||
for kernel in kernels:
|
||||
write_single_kernel(kernel, output_dir)
|
||||
api_pool, kernels = get_fwd_splitkv_blobs(kernel_filter, receipt, mask_impl)
|
||||
for kernel in kernels:
|
||||
write_single_kernel(kernel, output_dir)
|
||||
write_fwd_splitkv_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_splitkv_combine_blobs(kernel_filter, receipt)
|
||||
for kernel in kernels:
|
||||
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
|
||||
_, kernels = get_fwd_splitkv_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_SPLITKV_API_FILENAME) + "\n")
|
||||
@@ -114,6 +114,9 @@ auto create_args(int argc, char* argv[])
|
||||
.insert("drop_seed", "1", "seed for random number generator")
|
||||
.insert("drop_offset", "0", "offset for random number generator")
|
||||
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
|
||||
.insert("num_splits",
|
||||
"1",
|
||||
"# of splits for key/value. 0 to determine actual number by heuristic")
|
||||
.insert("warmup", "5", "number of iterations before benchmark the kernel")
|
||||
.insert("repeat", "20", "number of iterations to benchmark the kernel");
|
||||
|
||||
@@ -155,6 +158,106 @@ auto get_elimit<ck_tile::fp8_t>(std::string init_method)
|
||||
}
|
||||
}
|
||||
|
||||
int num_splits_heuristic(int batch_nhead_mblocks, int num_SMs, int num_n_blocks, int max_splits)
|
||||
{
|
||||
// If we have enough to almost fill the SMs, then just use 1 split
|
||||
if(batch_nhead_mblocks >= 0.8f * num_SMs)
|
||||
{
|
||||
return 1;
|
||||
}
|
||||
max_splits = std::min({max_splits, num_SMs, num_n_blocks});
|
||||
float max_efficiency = 0.f;
|
||||
std::vector<float> efficiency;
|
||||
efficiency.reserve(max_splits);
|
||||
auto ceildiv = [](int a, int b) { return (a + b - 1) / b; };
|
||||
// Some splits are not eligible. For example, if we have 64 blocks and choose 11 splits,
|
||||
// we'll have 6 * 10 + 4 blocks. If we choose 12 splits, we'll have 6 * 11 + (-2) blocks
|
||||
// (i.e. it's 11 splits anyway).
|
||||
// So we check if the number of blocks per split is the same as the previous num_splits.
|
||||
auto is_split_eligible = [&ceildiv, &num_n_blocks](int num_splits) {
|
||||
return num_splits == 1 ||
|
||||
ceildiv(num_n_blocks, num_splits) != ceildiv(num_n_blocks, num_splits - 1);
|
||||
};
|
||||
for(int num_splits = 1; num_splits <= max_splits; num_splits++)
|
||||
{
|
||||
if(!is_split_eligible(num_splits))
|
||||
{
|
||||
efficiency.push_back(0.f);
|
||||
}
|
||||
else
|
||||
{
|
||||
float n_waves = float(batch_nhead_mblocks * num_splits) / num_SMs;
|
||||
float eff = n_waves / ceil(n_waves);
|
||||
// printf("num_splits = %d, eff = %f\n", num_splits, eff);
|
||||
if(eff > max_efficiency)
|
||||
{
|
||||
max_efficiency = eff;
|
||||
}
|
||||
efficiency.push_back(eff);
|
||||
}
|
||||
}
|
||||
for(int num_splits = 1; num_splits <= max_splits; num_splits++)
|
||||
{
|
||||
if(!is_split_eligible(num_splits))
|
||||
{
|
||||
continue;
|
||||
}
|
||||
if(efficiency[num_splits - 1] >= 0.85 * max_efficiency)
|
||||
{
|
||||
// printf("num_splits chosen = %d\n", num_splits);
|
||||
return num_splits;
|
||||
}
|
||||
}
|
||||
return 1;
|
||||
}
|
||||
|
||||
int override_num_splits_if_necessary(
|
||||
int batch, int nhead, int max_seqlen_q, int hdim_v, float p_drop, int num_splits)
|
||||
{
|
||||
int device;
|
||||
auto status = hipGetDevice(&device);
|
||||
if(status != hipSuccess)
|
||||
{
|
||||
return num_splits;
|
||||
}
|
||||
|
||||
hipDeviceProp_t props{};
|
||||
status = hipGetDeviceProperties(&props, device);
|
||||
if(status != hipSuccess)
|
||||
{
|
||||
return num_splits;
|
||||
}
|
||||
|
||||
// tile size should match the generate.py
|
||||
const int kM0 = 64;
|
||||
const int kN1 = hdim_v;
|
||||
|
||||
const int num_m_blocks = ck_tile::integer_divide_ceil(max_seqlen_q, kM0);
|
||||
const int num_n_blocks = ck_tile::integer_divide_ceil(hdim_v, kN1);
|
||||
|
||||
if(num_splits < 1 && p_drop == 0.0f)
|
||||
{
|
||||
return num_splits_heuristic(
|
||||
batch * nhead * num_m_blocks, props.multiProcessorCount * 2, num_n_blocks, 128);
|
||||
}
|
||||
|
||||
return num_splits;
|
||||
}
|
||||
|
||||
float fmha_fwd_dispatch(fmha_fwd_traits traits,
|
||||
fmha_fwd_args args,
|
||||
const ck_tile::stream_config& config)
|
||||
{
|
||||
if(1 < args.num_splits)
|
||||
{
|
||||
return fmha_fwd_splitkv(traits, args, config);
|
||||
}
|
||||
else
|
||||
{
|
||||
return fmha_fwd(traits, args, config);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename DataType>
|
||||
bool run(const ck_tile::ArgParser& arg_parser)
|
||||
{
|
||||
@@ -260,6 +363,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
seed.reset();
|
||||
}
|
||||
|
||||
int num_splits = arg_parser.get_int("num_splits");
|
||||
|
||||
int stream_warmup = arg_parser.get_int("warmup");
|
||||
int stream_repeat = arg_parser.get_int("repeat");
|
||||
bool kname = arg_parser.get_bool("kname");
|
||||
@@ -320,6 +425,18 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
}
|
||||
}
|
||||
|
||||
// legalize num_splits according to other options
|
||||
if(num_splits < 1)
|
||||
{
|
||||
num_splits = override_num_splits_if_necessary(
|
||||
batch, nhead, max_seqlen_q, hdim_v, p_drop, num_splits);
|
||||
}
|
||||
if(128 < num_splits)
|
||||
{
|
||||
std::cerr << "num_splits greater than 128 is not supported" << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
auto get_lengths = [&](bool permute,
|
||||
ck_tile::index_t b /*batch*/,
|
||||
ck_tile::index_t h /*nhead*/,
|
||||
@@ -361,7 +478,15 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
: std::array<ck_tile::index_t, 2>{batch, nhead})
|
||||
: std::array<ck_tile::index_t, 2>{1, 1});
|
||||
|
||||
// self define lse data layout as [shape_batch, nhead, shape_seqlen_q]
|
||||
ck_tile::HostTensor<LSEDataType> lse_acc_host(
|
||||
1 < num_splits ? std::array<ck_tile::index_t, 4>{num_splits, batch, nhead, max_seqlen_q}
|
||||
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
|
||||
ck_tile::HostTensor<OaccDataType> o_acc_host(
|
||||
1 < num_splits
|
||||
? std::array<ck_tile::index_t, 5>{num_splits, batch, nhead, max_seqlen_q, hdim_v}
|
||||
: std::array<ck_tile::index_t, 5>{1, 1, 1, 1, 1});
|
||||
|
||||
// self define lse data layout as [batch, nhead, max_seqlen_q]
|
||||
ck_tile::HostTensor<LSEDataType> lse_host(
|
||||
lse ? std::array<ck_tile::index_t, 3>{batch, nhead, max_seqlen_q}
|
||||
: std::array<ck_tile::index_t, 3>{1, 1, 1} /* dummy shape for simplifying code */);
|
||||
@@ -443,6 +568,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
ck_tile::DeviceMem k_buf(k_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem v_buf(v_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem bias_buf(bias_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem lse_acc_buf(lse_acc_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem o_acc_buf(o_acc_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem lse_buf(lse_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
|
||||
@@ -479,7 +606,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
: (std::string("(") + std::to_string(seqlen_kpads[0]) + ")"))
|
||||
<< ", d:" << hdim_q << "/" << hdim_v << ", scale_s:" << scale_s << ", bias:" << bias
|
||||
<< ", p_drop:" << p_drop << ", lse:" << lse << ", squant:" << squant
|
||||
<< ", mask:" << mask << ", v:" << vlayout << std::flush;
|
||||
<< ", mask:" << mask << ", v:" << vlayout;
|
||||
if(1 < num_splits)
|
||||
{
|
||||
std::cout << ", num_splits:" << num_splits;
|
||||
}
|
||||
std::cout << std::flush;
|
||||
|
||||
auto fmha_traits = fmha_fwd_traits{hdim_q,
|
||||
hdim_v,
|
||||
@@ -523,6 +655,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
}();
|
||||
const ck_tile::index_t stride_bias = (i_perm ? shape_seqlen_k : 1 * shape_seqlen_k);
|
||||
const ck_tile::index_t stride_randval = (max_seqlen_k);
|
||||
const ck_tile::index_t stride_o_acc = hdim_v;
|
||||
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
|
||||
// setup nhead_stride_* arguments
|
||||
const ck_tile::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
|
||||
@@ -537,6 +670,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
(i_perm ? 0 * shape_seqlen_q * shape_seqlen_k : 0 * shape_seqlen_k);
|
||||
const ck_tile::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
|
||||
const ck_tile::index_t nhead_stride_lse = max_seqlen_q;
|
||||
const ck_tile::index_t nhead_stride_lse_acc = max_seqlen_q;
|
||||
const ck_tile::index_t nhead_stride_o_acc = (max_seqlen_q * hdim_v);
|
||||
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
|
||||
// setup batch_stride_* arguments
|
||||
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
|
||||
@@ -545,7 +680,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
const ck_tile::index_t batch_stride_bias = (0 * nhead * shape_seqlen_q * shape_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
|
||||
const ck_tile::index_t batch_stride_lse = (nhead * max_seqlen_q);
|
||||
const ck_tile::index_t batch_stride_lse_acc = (nhead * max_seqlen_q);
|
||||
const ck_tile::index_t batch_stride_o_acc = (nhead * max_seqlen_q * hdim_v);
|
||||
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
|
||||
// setup split_stride_* arguments (only used in split-kv kernel)
|
||||
const ck_tile::index_t split_stride_lse_acc = (batch * nhead * max_seqlen_q);
|
||||
const ck_tile::index_t split_stride_o_acc = (batch * nhead * max_seqlen_q * hdim_v);
|
||||
|
||||
return fmha_fwd_args{q_buf.GetDeviceBuffer(),
|
||||
k_buf.GetDeviceBuffer(),
|
||||
@@ -553,6 +693,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
bias.type == bias_enum::alibi ? alibi_slope_buf.GetDeviceBuffer()
|
||||
: bias_buf.GetDeviceBuffer(),
|
||||
randval_buf.GetDeviceBuffer(),
|
||||
lse_acc_buf.GetDeviceBuffer(),
|
||||
o_acc_buf.GetDeviceBuffer(),
|
||||
lse_buf.GetDeviceBuffer(),
|
||||
o_buf.GetDeviceBuffer(),
|
||||
seqstart_q.GetDeviceBuffer(),
|
||||
@@ -566,6 +708,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
hdim_v,
|
||||
nhead,
|
||||
nhead_k,
|
||||
num_splits,
|
||||
scale_s,
|
||||
scale_p,
|
||||
scale_o,
|
||||
@@ -575,6 +718,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
bias.type == bias_enum::alibi ? (bias.rank_info == 0 ? 0 : nhead)
|
||||
: stride_bias,
|
||||
stride_randval,
|
||||
stride_o_acc,
|
||||
stride_o,
|
||||
nhead_stride_q,
|
||||
nhead_stride_k,
|
||||
@@ -582,6 +726,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
nhead_stride_bias,
|
||||
nhead_stride_randval,
|
||||
nhead_stride_lse,
|
||||
nhead_stride_lse_acc,
|
||||
nhead_stride_o_acc,
|
||||
nhead_stride_o,
|
||||
batch_stride_q,
|
||||
batch_stride_k,
|
||||
@@ -589,7 +735,11 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
batch_stride_bias,
|
||||
batch_stride_randval,
|
||||
batch_stride_lse,
|
||||
batch_stride_lse_acc,
|
||||
batch_stride_o_acc,
|
||||
batch_stride_o,
|
||||
split_stride_lse_acc,
|
||||
split_stride_o_acc,
|
||||
mask.left,
|
||||
mask.right,
|
||||
static_cast<ck_tile::index_t>(mask.type),
|
||||
@@ -598,7 +748,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
{drop_seed, drop_offset}};
|
||||
}();
|
||||
|
||||
float ave_time = fmha_fwd(fmha_traits, fmha_args, stream_config);
|
||||
float ave_time = fmha_fwd_dispatch(fmha_traits, fmha_args, stream_config);
|
||||
|
||||
if(ave_time < 0)
|
||||
{
|
||||
@@ -849,14 +999,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
|
||||
lse_host_result.ForEach(
|
||||
[&](auto& self, auto idx) { self(idx) = lse_host(wb, idx[0], idx[1]); });
|
||||
|
||||
bool lse_pass = ck_tile::check_err(lse_host_result,
|
||||
lse_host_ref,
|
||||
"LSE Error: Incorrect results!",
|
||||
rtol,
|
||||
atol,
|
||||
/* allow_infinity_ref = */ true);
|
||||
cur_pass = ck_tile::check_err(lse_host_result,
|
||||
lse_host_ref,
|
||||
"LSE Error: Incorrect results!",
|
||||
rtol,
|
||||
atol,
|
||||
/* allow_infinity_ref = */ true);
|
||||
|
||||
pass &= lse_pass;
|
||||
pass &= cur_pass;
|
||||
if(!cur_pass)
|
||||
{
|
||||
std::cerr << "LSE mismatch found at batch: " << wb << std::endl
|
||||
|
||||
@@ -93,6 +93,8 @@ struct fmha_fwd_args
|
||||
const void* v_ptr;
|
||||
const void* bias_ptr; // bias or alibi_slope pointer
|
||||
void* rand_val_ptr;
|
||||
void* lse_acc_ptr;
|
||||
void* o_acc_ptr;
|
||||
void* lse_ptr;
|
||||
void* o_ptr;
|
||||
const void* seqstart_q_ptr;
|
||||
@@ -106,6 +108,7 @@ struct fmha_fwd_args
|
||||
ck_tile::index_t hdim_v;
|
||||
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;
|
||||
@@ -114,6 +117,7 @@ struct fmha_fwd_args
|
||||
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;
|
||||
@@ -121,6 +125,8 @@ struct fmha_fwd_args
|
||||
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;
|
||||
ck_tile::index_t nhead_stride_o;
|
||||
ck_tile::index_t batch_stride_q;
|
||||
ck_tile::index_t batch_stride_k;
|
||||
@@ -128,7 +134,11 @@ struct fmha_fwd_args
|
||||
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;
|
||||
@@ -234,6 +244,176 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
|
||||
return ck_tile::make_tuple(kargs, grids);
|
||||
}
|
||||
|
||||
template <typename Kernel>
|
||||
auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
|
||||
{
|
||||
assert(args.nhead_q % args.nhead_k == 0);
|
||||
auto kargs = [&] {
|
||||
// create group mode kernel arguments
|
||||
if constexpr(Kernel::kIsGroupMode)
|
||||
{
|
||||
return Kernel::MakeKargs(args.q_ptr,
|
||||
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,
|
||||
args.hdim_q,
|
||||
args.hdim_v,
|
||||
args.nhead_q,
|
||||
args.nhead_q / args.nhead_k,
|
||||
args.num_splits,
|
||||
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_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);
|
||||
}
|
||||
else
|
||||
{ // create batch mode kernel arguments
|
||||
return Kernel::MakeKargs(args.q_ptr,
|
||||
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.hdim_q,
|
||||
args.hdim_v,
|
||||
args.nhead_q,
|
||||
args.nhead_q / args.nhead_k,
|
||||
args.num_splits,
|
||||
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);
|
||||
}
|
||||
}();
|
||||
|
||||
dim3 grids =
|
||||
Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v, args.num_splits);
|
||||
|
||||
return ck_tile::make_tuple(kargs, grids);
|
||||
}
|
||||
|
||||
template <typename Kernel>
|
||||
auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_args args)
|
||||
{
|
||||
assert(args.nhead_q % args.nhead_k == 0);
|
||||
auto kargs = [&] {
|
||||
// create group mode kernel argumentszs
|
||||
if constexpr(Kernel::kIsGroupMode)
|
||||
{
|
||||
return Kernel::MakeKargs(args.lse_acc_ptr,
|
||||
args.o_acc_ptr,
|
||||
args.lse_ptr,
|
||||
args.o_ptr,
|
||||
args.batch,
|
||||
args.max_seqlen_q,
|
||||
args.seqstart_q_ptr,
|
||||
args.hdim_v,
|
||||
args.num_splits,
|
||||
args.scale_o,
|
||||
args.stride_o_acc,
|
||||
args.stride_o,
|
||||
args.nhead_stride_lse_acc,
|
||||
args.nhead_stride_o_acc,
|
||||
args.nhead_stride_lse,
|
||||
args.nhead_stride_o,
|
||||
args.batch_stride_lse_acc,
|
||||
args.batch_stride_o_acc,
|
||||
args.batch_stride_lse,
|
||||
args.split_stride_lse_acc,
|
||||
args.split_stride_o_acc);
|
||||
}
|
||||
else
|
||||
{ // create batch mode kernel arguments
|
||||
return Kernel::MakeKargs(args.lse_acc_ptr,
|
||||
args.o_acc_ptr,
|
||||
args.lse_ptr,
|
||||
args.o_ptr,
|
||||
args.batch,
|
||||
args.max_seqlen_q,
|
||||
args.seqlen_q,
|
||||
args.hdim_v,
|
||||
args.num_splits,
|
||||
args.scale_o,
|
||||
args.stride_o_acc,
|
||||
args.stride_o,
|
||||
args.nhead_stride_lse_acc,
|
||||
args.nhead_stride_o_acc,
|
||||
args.nhead_stride_lse,
|
||||
args.nhead_stride_o,
|
||||
args.batch_stride_lse_acc,
|
||||
args.batch_stride_o_acc,
|
||||
args.batch_stride_lse,
|
||||
args.batch_stride_o,
|
||||
args.split_stride_lse_acc,
|
||||
args.split_stride_o_acc);
|
||||
}
|
||||
}();
|
||||
|
||||
dim3 grids = Kernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q, args.hdim_v);
|
||||
|
||||
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_,
|
||||
@@ -282,6 +462,40 @@ struct fmha_fwd_traits_
|
||||
template <typename Traits_>
|
||||
float fmha_fwd_(const ck_tile::stream_config&, fmha_fwd_args);
|
||||
|
||||
template <typename Traits_>
|
||||
void fmha_fwd_splitkv_oneshot_(const ck_tile::stream_config&, fmha_fwd_args);
|
||||
|
||||
template <typename Traits_>
|
||||
std::string fmha_fwd_splitkv_get_name_();
|
||||
|
||||
template <ck_tile::index_t HDim_,
|
||||
typename DataType_,
|
||||
bool kIsGroupMode_,
|
||||
ck_tile::index_t kM0_,
|
||||
ck_tile::index_t kN1_,
|
||||
bool kStoreLse_,
|
||||
bool kDoFp8StaticQuant_,
|
||||
bool kPadS_,
|
||||
bool kPadDv_>
|
||||
struct fmha_fwd_splitkv_combine_traits_
|
||||
{
|
||||
static constexpr ck_tile::index_t HDim = HDim_;
|
||||
using DataType = ck_tile::remove_cvref_t<DataType_>;
|
||||
static constexpr bool kIsGroupMode = kIsGroupMode_;
|
||||
static constexpr ck_tile::index_t kM0 = kM0_;
|
||||
static constexpr ck_tile::index_t kN1 = kN1_;
|
||||
static constexpr bool kStoreLse = kStoreLse_;
|
||||
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
|
||||
static constexpr bool kPadS = kPadS_;
|
||||
static constexpr bool kPadDv = kPadDv_;
|
||||
};
|
||||
|
||||
template <typename Traits_>
|
||||
void fmha_fwd_splitkv_combine_oneshot_(const ck_tile::stream_config&, fmha_fwd_args);
|
||||
|
||||
template <typename Traits_>
|
||||
std::string fmha_fwd_splitkv_combine_get_name_();
|
||||
|
||||
// This is the public API, will be generated by script
|
||||
struct fmha_fwd_traits
|
||||
{
|
||||
@@ -298,3 +512,4 @@ struct fmha_fwd_traits
|
||||
// TODO: padding check is inside this api
|
||||
};
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||||
float fmha_fwd(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
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||||
float fmha_fwd_splitkv(fmha_fwd_traits, fmha_fwd_args, const ck_tile::stream_config&);
|
||||
|
||||
File diff suppressed because it is too large
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