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composable_kernel/example/ck_tile/42_unified_attention/unified_attention.cpp
root 4c5e290378 Add unified attention (42_unified_attention) and topk_softmax_decode 
Squashed from aghamari/unified-attention-decode-opt branch.

42_unified_attention: CK tile paged-KV attention kernel optimized for
decode with 4-tier dispatch (tiny/small/medium/large), 16x16 MFMA,
2D decode grid, head-group merging. Supports hdim=64 GQA-8 and
hdim=128 MHA with block_size=32.

topk_softmax_decode: fused topk + softmax kernel for M=1 MoE decode.

Made-with: Cursor
2026-04-01 16:24:04 +00:00

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#include "unified_attention.hpp"
#include "unified_attention_impl.hpp"
#include "mask.hpp"
namespace ck_tile {
std::ostream& operator<<(std::ostream& stream,
const unified_attention_args::data_type_enum& data_type)
{
switch(data_type)
{
case unified_attention_args::data_type_enum::fp16: return stream << "fp16";
case unified_attention_args::data_type_enum::bf16: return stream << "bf16";
default: return stream << "unknown";
}
}
// Helper macro to reduce dispatch boilerplate.
// Dispatches based on DataType, IsMasking, HeadSize, BlockM, NumQPerKV.
#define DISPATCH_UNIFIED_ATTENTION(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_kernel_traits<DType, IsMask, HSize, BM, NQPKV>; \
return unified_attention_kernel_dispatch<kernel_traits>(args, config); \
}
// Dispatch macros for three tile tiers (default block_size).
#define DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_kernel_traits<DType, IsMask, HSize, BM, NQPKV>; \
return unified_attention_kernel_dispatch<kernel_traits>(args, config); \
}
#define DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_small_kernel_traits<DType, IsMask, HSize, BM, NQPKV>; \
return unified_attention_kernel_dispatch_decode<kernel_traits>(args, config); \
}
#define DISPATCH_UNIFIED_ATTENTION_DECODE_TINY(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_tiny_kernel_traits<DType, IsMask, HSize, BM, NQPKV>; \
return unified_attention_kernel_dispatch_decode<kernel_traits>(args, config); \
}
// block_size=32 dispatch macros (6th template arg = 32).
#define DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_kernel_traits<DType, IsMask, HSize, BM, NQPKV, 32>; \
return unified_attention_kernel_dispatch<kernel_traits>(args, config); \
}
#define DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_small_kernel_traits<DType, IsMask, HSize, BM, NQPKV, 32>; \
return unified_attention_kernel_dispatch_decode<kernel_traits>(args, config); \
}
#define DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_bs32_kernel_traits<DType, IsMask, HSize, BM, NQPKV, 32>; \
return unified_attention_kernel_dispatch_decode<kernel_traits>(args, config); \
}
enum class tile_tier { large, medium, small, tiny };
static tile_tier select_tile_tier(const unified_attention_args& args)
{
const index_t avg_q = args.num_seqs > 0 ? args.num_tokens / args.num_seqs : args.num_tokens;
const index_t kBlockQ_tiny = 16 / args.num_queries_per_kv; // kBlockQ for 1-warp 16x16 kernel
if(avg_q <= kBlockQ_tiny)
return tile_tier::tiny; // pure decode: 1 warp, 16x16 MFMA, kBlockM=16
const index_t kBlockQ_small = 64 / args.num_queries_per_kv; // kBlockQ for 2-warp kernel
if(avg_q <= kBlockQ_small)
return tile_tier::small; // short decode: 2 warps, kBlockM=64
// 4-warp serial pipeline outperforms 8-warp interleaved on all prefill shapes
// (verified by exhaustive sweep over 363 shapes from production trace).
return tile_tier::medium; // all prefill: 4 warps, kBlockM=128
}
std::pair<bool, float> unified_attention(const unified_attention_args& args,
const stream_config& config)
{
const bool is_mask = (args.mask_type != static_cast<int>(mask_enum::no_mask));
const auto tier = select_tile_tier(args);
// d128, MHA (num_queries_per_kv == 1)
if(args.hdim == 128 && args.num_queries_per_kv == 1)
{
if(args.data_type == unified_attention_args::data_type_enum::fp16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::fp16, false, 128, 256, 1)
else DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::fp16, true, 128, 256, 1)
}
else if(args.data_type == unified_attention_args::data_type_enum::bf16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::bf16, false, 128, 256, 1)
else DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::bf16, true, 128, 256, 1)
}
}
// d64, GQA-8 (num_queries_per_kv == 8)
if(args.hdim == 64 && args.num_queries_per_kv == 8)
{
const bool use_bs32 = (args.page_blk_size < 64);
if(tier == tile_tier::tiny)
{
if(use_bs32) {
// bs32 narrow: 2 warps, 16x16 MFMA, kBlockM=32, kBlockQ=4.
// Avoids 1-warp race condition; 2x less waste than small tier.
if(args.data_type == unified_attention_args::data_type_enum::fp16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW(unified_attention_args::data_type_enum::fp16, false, 64, 32, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW(unified_attention_args::data_type_enum::fp16, true, 64, 32, 8)
}
else if(args.data_type == unified_attention_args::data_type_enum::bf16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW(unified_attention_args::data_type_enum::bf16, false, 64, 32, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW(unified_attention_args::data_type_enum::bf16, true, 64, 32, 8)
}
} else {
// bs64 tiny: 1 warp, 16x16 MFMA, kBlockM=16, kBlockQ=2.
if(args.data_type == unified_attention_args::data_type_enum::fp16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_TINY(unified_attention_args::data_type_enum::fp16, false, 64, 16, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_TINY(unified_attention_args::data_type_enum::fp16, true, 64, 16, 8)
}
else if(args.data_type == unified_attention_args::data_type_enum::bf16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_TINY(unified_attention_args::data_type_enum::bf16, false, 64, 16, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_TINY(unified_attention_args::data_type_enum::bf16, true, 64, 16, 8)
}
}
}
else if(tier == tile_tier::small)
{
if(args.data_type == unified_attention_args::data_type_enum::fp16)
{
if(use_bs32) {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32(unified_attention_args::data_type_enum::fp16, false, 64, 64, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32(unified_attention_args::data_type_enum::fp16, true, 64, 64, 8)
} else {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL(unified_attention_args::data_type_enum::fp16, false, 64, 64, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL(unified_attention_args::data_type_enum::fp16, true, 64, 64, 8)
}
}
else if(args.data_type == unified_attention_args::data_type_enum::bf16)
{
if(use_bs32) {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32(unified_attention_args::data_type_enum::bf16, false, 64, 64, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32(unified_attention_args::data_type_enum::bf16, true, 64, 64, 8)
} else {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL(unified_attention_args::data_type_enum::bf16, false, 64, 64, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL(unified_attention_args::data_type_enum::bf16, true, 64, 64, 8)
}
}
}
else if(tier == tile_tier::medium)
{
if(args.data_type == unified_attention_args::data_type_enum::fp16)
{
if(use_bs32) {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32(unified_attention_args::data_type_enum::fp16, false, 64, 128, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32(unified_attention_args::data_type_enum::fp16, true, 64, 128, 8)
} else {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM(unified_attention_args::data_type_enum::fp16, false, 64, 128, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM(unified_attention_args::data_type_enum::fp16, true, 64, 128, 8)
}
}
else if(args.data_type == unified_attention_args::data_type_enum::bf16)
{
if(use_bs32) {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32(unified_attention_args::data_type_enum::bf16, false, 64, 128, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32(unified_attention_args::data_type_enum::bf16, true, 64, 128, 8)
} else {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM(unified_attention_args::data_type_enum::bf16, false, 64, 128, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM(unified_attention_args::data_type_enum::bf16, true, 64, 128, 8)
}
}
}
else
{
// Large prefill: 8 warps, kBlockM=256 (kBlockQ=32)
// No bs32 variant -- NumIssues < 1 for 8-warp tier with block_size=32.
if(args.data_type == unified_attention_args::data_type_enum::fp16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::fp16, false, 64, 256, 8)
else DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::fp16, true, 64, 256, 8)
}
else if(args.data_type == unified_attention_args::data_type_enum::bf16)
{
if(!is_mask) DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::bf16, false, 64, 256, 8)
else DISPATCH_UNIFIED_ATTENTION(unified_attention_args::data_type_enum::bf16, true, 64, 256, 8)
}
}
}
std::cerr << "unified_attention: no matching kernel instance for hdim=" << args.hdim
<< " num_queries_per_kv=" << args.num_queries_per_kv
<< " data_type=" << args.data_type << " mask_type=" << args.mask_type << std::endl;
return std::make_pair(false, -1.f);
}
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_TINY
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM
#undef DISPATCH_UNIFIED_ATTENTION
} // namespace ck_tile
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