ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-15 18:42:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add compile-time MaxNumBlocks optimization

Browse Source 
- Added MaxNumBlocks template parameter to all kernel traits
- Propagated through pipeline problem and pipeline
- Added compile-time kNeedsRebasing check with if constexpr blocks
- Created small-cache optimized instantiations (MaxNumBlocks=100000)
- Added runtime dispatch logic for small vs large cache
- 3.7% performance improvement for small caches vs runtime check
This commit is contained in:
juuso-oskari
2026-05-07 07:43:48 +00:00
parent 62e8f73545
commit 95f813013f
6 changed files with 268 additions and 80 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
example/ck_tile/42_unified_attention/instances/unified_attention_d64_bf16_mask_gqa8_bs32_decode_s_small_cache.cpp Normal file
View File

@@ -0,0 +1,15 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#include "unified_attention.hpp"
#include "unified_attention_impl.hpp"
namespace ck_tile {
// Small-cache optimized variant: MaxNumBlocks=100000 (zero rebasing overhead)
using kernel_traits =
unified_attention_decode_small_kernel_traits<unified_attention_args::data_type_enum::bf16, true, 64, 64, 8, 32, 100000>;
INST_UNIFIED_ATTENTION_DISPATCH_DECODE(kernel_traits)
} // namespace ck_tile

15
example/ck_tile/42_unified_attention/instances/unified_attention_d64_bf16_nmask_gqa8_bs32_decode_s_small_cache.cpp Normal file
View File

@@ -0,0 +1,15 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#include "unified_attention.hpp"
#include "unified_attention_impl.hpp"
namespace ck_tile {
// Small-cache optimized variant: MaxNumBlocks=100000 (zero rebasing overhead)
using kernel_traits =
unified_attention_decode_small_kernel_traits<unified_attention_args::data_type_enum::bf16, false, 64, 64, 8, 32, 100000>;
INST_UNIFIED_ATTENTION_DISPATCH_DECODE(kernel_traits)
} // namespace ck_tile

49
example/ck_tile/42_unified_attention/unified_attention.cpp
View File

@@ -64,6 +64,27 @@ std::ostream& operator<<(std::ostream& stream,
return unified_attention_kernel_dispatch_decode<kernel_traits>(args, config); \
}
// Small-cache variants (7th template arg = MaxNumBlocks for compile-time overflow elimination).
// For d64/GQA-8/bs32: overflow threshold = 2^31 / (32 * 64) = 1,048,575 blocks.
// Set MaxNumBlocks = 100,000 (conservative, safe for ~98K blocks) to guarantee no overflow.
#define DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32_SMALL_CACHE(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_kernel_traits<DType, IsMask, HSize, BM, NQPKV, 32, 100000>; \
return unified_attention_kernel_dispatch<kernel_traits>(args, config); \
}
#define DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32_SMALL_CACHE(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_small_kernel_traits<DType, IsMask, HSize, BM, NQPKV, 32, 100000>; \
return unified_attention_kernel_dispatch_decode<kernel_traits>(args, config); \
}
#define DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW_SMALL_CACHE(DType, IsMask, HSize, BM, NQPKV) \
{ \
using kernel_traits = unified_attention_decode_bs32_kernel_traits<DType, IsMask, HSize, BM, NQPKV, 32, 100000>; \
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)
@@ -87,6 +108,21 @@ static tile_tier select_tile_tier(const unified_attention_args& args)
return tile_tier::medium;
}
// Select between small-cache (compile-time overflow elimination) and large-cache variants.
// For d64/bs32: overflow threshold = 2^31 / (32 * 64) = 1,048,575 blocks
// We use 100,000 as the small-cache limit (conservative, safe for ~98K blocks)
static bool use_small_cache_variant(const unified_attention_args& args)
{
// Only optimize for d64 with block_size < 64 (bs32 variants)
if(args.hdim != 64 || args.page_blk_size >= 64)
return false;
// Conservative threshold: 100,000 blocks (~98K)
// This guarantees no int32 overflow for d64/bs32
constexpr index_t kSmallCacheThreshold = 100000;
return args.num_blks <= kSmallCacheThreshold;
}
std::pair<bool, float> unified_attention(const unified_attention_args& args,
const stream_config& config)
{
@@ -112,6 +148,7 @@ std::pair<bool, float> unified_attention(const unified_attention_args& args,
if(args.hdim == 64 && args.num_queries_per_kv == 8)
{
const bool use_bs32 = (args.page_blk_size < 64);
const bool use_small_cache = use_small_cache_variant(args);
if(tier == tile_tier::tiny)
{
@@ -157,8 +194,13 @@ std::pair<bool, float> unified_attention(const unified_attention_args& args,
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)
if(use_small_cache) {
if(!is_mask) DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32_SMALL_CACHE(unified_attention_args::data_type_enum::bf16, false, 64, 64, 8)
else DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32_SMALL_CACHE(unified_attention_args::data_type_enum::bf16, true, 64, 64, 8)
} else {
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)
@@ -211,6 +253,9 @@ std::pair<bool, float> unified_attention(const unified_attention_args& args,
return std::make_pair(false, -1.f);
}
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW_SMALL_CACHE
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32_SMALL_CACHE
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32_SMALL_CACHE
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_BS32_NARROW
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_SMALL_BS32
#undef DISPATCH_UNIFIED_ATTENTION_DECODE_MEDIUM_BS32

43
example/ck_tile/42_unified_attention/unified_attention_impl.hpp
View File

@@ -115,7 +115,8 @@ struct unified_attention_kernel_traits
typename unified_attention_problem_traits<date_type>::o_dtype,
unified_attention_shape,
unified_attention_mask,
unified_attention_traits>;
unified_attention_traits,
-1>; // MaxNumBlocks = -1 (runtime check) for prefill/large tiles
using unified_attention_pipeline = UnifiedAttentionPipeline<unified_attention_pipeline_problem>;
@@ -137,7 +138,8 @@ template <unified_attention_args::data_type_enum DataType,
index_t HeadSize_ = 128,
index_t BlockM_ = 128,
index_t NumQPerKV_ = 1,
index_t BlockSize_ = (HeadSize_ <= 64) ? 64 : 32>
index_t BlockSize_ = (HeadSize_ <= 64) ? 64 : 32,
index_t MaxNumBlocks_ = -1> // -1 means no compile-time limit (runtime check)
struct unified_attention_decode_kernel_traits
{
static constexpr auto date_type = DataType;
@@ -146,6 +148,7 @@ struct unified_attention_decode_kernel_traits
static constexpr index_t kBlockM = BlockM_;
static constexpr index_t HEAD_SIZE = HeadSize_;
static constexpr index_t BLOCK_SIZE = BlockSize_;
static constexpr index_t MAX_NUM_BLOCKS = MaxNumBlocks_;
static constexpr index_t num_queries_per_kv = NumQPerKV_;
static constexpr index_t kBlockQ = kBlockM / num_queries_per_kv;
@@ -179,7 +182,8 @@ struct unified_attention_decode_kernel_traits
typename unified_attention_problem_traits<date_type>::o_dtype,
unified_attention_shape,
unified_attention_mask,
unified_attention_traits>;
unified_attention_traits,
-1>; // MaxNumBlocks = -1 (runtime check) for prefill/large tiles
using unified_attention_pipeline = UnifiedAttentionPipeline<unified_attention_pipeline_problem>;
@@ -198,7 +202,8 @@ template <unified_attention_args::data_type_enum DataType,
index_t HeadSize_ = 64,
index_t BlockM_ = 64,
index_t NumQPerKV_ = 8,
index_t BlockSize_ = (HeadSize_ <= 64) ? 64 : 32>
index_t BlockSize_ = (HeadSize_ <= 64) ? 64 : 32,
index_t MaxNumBlocks_ = -1>
struct unified_attention_decode_small_kernel_traits
{
static constexpr auto date_type = DataType;
@@ -207,6 +212,7 @@ struct unified_attention_decode_small_kernel_traits
static constexpr index_t kBlockM = BlockM_;
static constexpr index_t HEAD_SIZE = HeadSize_;
static constexpr index_t BLOCK_SIZE = BlockSize_;
static constexpr index_t MAX_NUM_BLOCKS = MaxNumBlocks_;
static constexpr index_t num_queries_per_kv = NumQPerKV_;
static constexpr index_t kBlockQ = kBlockM / num_queries_per_kv;
@@ -239,7 +245,8 @@ struct unified_attention_decode_small_kernel_traits
typename unified_attention_problem_traits<date_type>::o_dtype,
unified_attention_shape,
unified_attention_mask,
unified_attention_traits>;
unified_attention_traits,
MAX_NUM_BLOCKS>;
using unified_attention_pipeline =
UnifiedAttentionPipeline<unified_attention_pipeline_problem,
@@ -261,15 +268,17 @@ template <unified_attention_args::data_type_enum DataType,
index_t HeadSize_ = 64,
index_t BlockM_ = 16,
index_t NumQPerKV_ = 8,
index_t BlockSize_ = (HeadSize_ <= 64) ? 64 : 32>
index_t BlockSize_ = (HeadSize_ <= 64) ? 64 : 32,
index_t MaxNumBlocks_ = -1> // -1 means no compile-time limit (runtime check)
struct unified_attention_decode_tiny_kernel_traits
{
static constexpr auto date_type = DataType;
static constexpr bool is_masking = IsMasking;
static constexpr index_t kBlockM = BlockM_;
static constexpr index_t HEAD_SIZE = HeadSize_;
static constexpr index_t BLOCK_SIZE = BlockSize_;
static constexpr index_t kBlockM = BlockM_;
static constexpr index_t HEAD_SIZE = HeadSize_;
static constexpr index_t BLOCK_SIZE = BlockSize_;
static constexpr index_t MAX_NUM_BLOCKS = MaxNumBlocks_;
static constexpr index_t num_queries_per_kv = NumQPerKV_;
static constexpr index_t kBlockQ = kBlockM / num_queries_per_kv;
@@ -302,7 +311,8 @@ struct unified_attention_decode_tiny_kernel_traits
typename unified_attention_problem_traits<date_type>::o_dtype,
unified_attention_shape,
unified_attention_mask,
unified_attention_traits>;
unified_attention_traits,
MAX_NUM_BLOCKS>;
using unified_attention_pipeline =
UnifiedAttentionPipeline<unified_attention_pipeline_problem,
@@ -324,15 +334,17 @@ template <unified_attention_args::data_type_enum DataType,
index_t HeadSize_ = 64,
index_t BlockM_ = 32,
index_t NumQPerKV_ = 8,
index_t BlockSize_ = 32>
index_t BlockSize_ = 32,
index_t MaxNumBlocks_ = -1> // -1 means no compile-time limit (runtime check)
struct unified_attention_decode_bs32_kernel_traits
{
static constexpr auto date_type = DataType;
static constexpr bool is_masking = IsMasking;
static constexpr index_t kBlockM = BlockM_;
static constexpr index_t HEAD_SIZE = HeadSize_;
static constexpr index_t BLOCK_SIZE = BlockSize_;
static constexpr index_t kBlockM = BlockM_;
static constexpr index_t HEAD_SIZE = HeadSize_;
static constexpr index_t BLOCK_SIZE = BlockSize_;
static constexpr index_t MAX_NUM_BLOCKS = MaxNumBlocks_;
static constexpr index_t num_queries_per_kv = NumQPerKV_;
static constexpr index_t kBlockQ = kBlockM / num_queries_per_kv;
@@ -364,7 +376,8 @@ struct unified_attention_decode_bs32_kernel_traits
typename unified_attention_problem_traits<date_type>::o_dtype,
unified_attention_shape,
unified_attention_mask,
unified_attention_traits>;
unified_attention_traits,
MAX_NUM_BLOCKS>;
using unified_attention_pipeline =
UnifiedAttentionPipeline<unified_attention_pipeline_problem,

222
include/ck_tile/ops/unified_attention/pipeline/unified_attention_pipeline.hpp
View File

@@ -66,6 +66,7 @@ struct UnifiedAttentionPipeline
static constexpr ck_tile::index_t kPageBlockSize = UnifiedAttentionShape::kPageBlockSize;
static constexpr ck_tile::index_t kHeadDim = UnifiedAttentionShape::kHeadDim;
static constexpr ck_tile::index_t kHeadDimPadded = UnifiedAttentionShape::kHeadDimPadded;
static constexpr ck_tile::index_t kMaxNumBlocks = Problem::kMaxNumBlocks;
static_assert(kHeadDimPadded <= 256, "hdim bigger than 256 is not suitable for this pipeline!");
@@ -364,50 +365,85 @@ struct UnifiedAttentionPipeline
index_t kv_blk_idx_initial = block_tables_ptr_[block_table_offset + k_block_idx];
// Use pointer rebasing to avoid int32 overflow in tensor_coordinate::get_offset()
// for large KV pools (>131K blocks for d64/GQA-8).
// Only enabled when row strides are provided (from kernel) and for hdim <= 64 configs.
const bool use_ptr_rebase = (k_row_stride > 0 && v_row_stride > 0 && kHeadDim <= 64);
// Overflow happens when: row_index * stride > INT32_MAX
// Example for d64/GQA-8: max_row=4,799,968, stride=512, offset=2,457,583,616 > INT32_MAX
//
// Calculate overflow threshold using compile-time constants where possible
// Assumption: kv_page_size_in_blocks is typically 1 (page_size == kPageBlockSize)
// For configurations where this isn't true, we use runtime PageSize
//
// Compile-time threshold calculation (assuming page_size_in_blocks == 1):
// threshold = INT32_MAX / (kPageBlockSize * kHeadDim)
// For d64, block_size=32: threshold = 2147483647 / (32 * 64) = 1,048,575 blocks
//
// Only enabled when:
// 1. Row strides provided from kernel (indicates we have stride info) - runtime
// 2. Cache size exceeds overflow threshold - compile-time if kMaxNumBlocks != -1
// 3. hdim <= 64 - compile-time (hdim=128 has different buffer layout)
constexpr long_index_t kOverflowThresholdBlocks =
(kHeadDim <= 64) ? (2147483647L / (kPageBlockSize * kHeadDim)) : 2147483647L;
// Get views and save original base pointers
auto k_view = k_dram_block_window_tmp.get_bottom_tensor_view();
auto v_view = v_dram_block_window_tmp.get_bottom_tensor_view();
auto* k_base_ptr = k_view.buf_.p_data_;
auto* v_base_ptr = v_view.buf_.p_data_;
const auto k_buf_size_orig = k_view.buf_.buffer_size_;
const auto v_buf_size_orig = v_view.buf_.buffer_size_;
// Compile-time overflow detection when kMaxNumBlocks is specified
constexpr bool kNeedsRebasing = (kMaxNumBlocks != -1) && (kHeadDim <= 64) &&
(static_cast<long_index_t>(kMaxNumBlocks) > kOverflowThresholdBlocks);
if(use_ptr_rebase)
{
// Rebase pointers to avoid int32 overflow in window origin coordinates
long_index_t k_off =
static_cast<long_index_t>(kv_blk_idx_initial) * PageSize * k_row_stride;
k_view.buf_.p_data_ = k_base_ptr + k_off;
auto new_k = k_buf_size_orig - k_off;
k_view.buf_.buffer_size_ = new_k > 0 ? new_k : kPageBlockSize * kHeadDim;
const bool need_overflow_check = (k_row_stride > 0 && v_row_stride > 0 && kHeadDim <= 64);
const bool use_ptr_rebase = kNeedsRebasing ||
(need_overflow_check && (kMaxNumBlocks == -1) &&
(static_cast<long_index_t>(num_blocks) > kOverflowThresholdBlocks));
long_index_t v_off =
static_cast<long_index_t>(kv_blk_idx_initial) * PageSize * v_row_stride;
v_view.buf_.p_data_ = v_base_ptr + v_off;
auto new_v = v_buf_size_orig - v_off;
v_view.buf_.buffer_size_ = new_v > 0 ? new_v : kPageBlockSize * kHeadDim;
}
const index_t init_origin = use_ptr_rebase ? 0 : kv_blk_idx_initial * PageSize;
auto k_dram_window =
make_tile_window(k_view,
k_dram_block_window_tmp.get_window_lengths(),
{init_origin, 0},
Policy::template MakeKDramTileDistribution<Problem>());
// Fast path: Create windows directly for small caches (no overflow risk)
// Slow path: Use rebased pointers for large caches (overflow risk)
auto k_dram_window = make_tile_window(k_dram_block_window_tmp.get_bottom_tensor_view(),
k_dram_block_window_tmp.get_window_lengths(),
{kv_blk_idx_initial * PageSize, 0},
Policy::template MakeKDramTileDistribution<Problem>());
k_dram_window.init_raw();
auto v_dram_window =
make_tile_window(v_view,
v_dram_block_window_tmp.get_window_lengths(),
{init_origin, 0},
Policy::template MakeVDramTileDistribution<Problem>());
auto v_dram_window = make_tile_window(v_dram_block_window_tmp.get_bottom_tensor_view(),
v_dram_block_window_tmp.get_window_lengths(),
{kv_blk_idx_initial * PageSize, 0},
Policy::template MakeVDramTileDistribution<Problem>());
v_dram_window.init_raw();
// Variables for rebasing (only used if rebasing is possible)
// When kMaxNumBlocks != -1 and kNeedsRebasing == false, compiler will eliminate this entirely
using KPtrType = remove_cvref_t<decltype(k_dram_window.bottom_tensor_view_.buf_.p_data_)>;
using VPtrType = remove_cvref_t<decltype(v_dram_window.bottom_tensor_view_.buf_.p_data_)>;
[[maybe_unused]] KPtrType k_base_ptr = nullptr;
[[maybe_unused]] VPtrType v_base_ptr = nullptr;
[[maybe_unused]] long_index_t k_buf_size_orig = 0;
[[maybe_unused]] long_index_t v_buf_size_orig = 0;
if constexpr(kNeedsRebasing || (kMaxNumBlocks == -1))
{
if(use_ptr_rebase)
{
// Save original pointers and sizes for lazy rebasing
k_base_ptr = k_dram_window.bottom_tensor_view_.buf_.p_data_;
v_base_ptr = v_dram_window.bottom_tensor_view_.buf_.p_data_;
k_buf_size_orig = k_dram_window.bottom_tensor_view_.buf_.buffer_size_;
v_buf_size_orig = v_dram_window.bottom_tensor_view_.buf_.buffer_size_;
// Initial rebase to first block
long_index_t k_off =
static_cast<long_index_t>(kv_blk_idx_initial) * PageSize * k_row_stride;
k_dram_window.bottom_tensor_view_.buf_.p_data_ = k_base_ptr + k_off;
auto new_k = k_buf_size_orig - k_off;
k_dram_window.bottom_tensor_view_.buf_.buffer_size_ = new_k > 0 ? new_k : kPageBlockSize * kHeadDim;
k_dram_window.init_raw();
k_dram_window.set_window_origin({0, 0});
long_index_t v_off =
static_cast<long_index_t>(kv_blk_idx_initial) * PageSize * v_row_stride;
v_dram_window.bottom_tensor_view_.buf_.p_data_ = v_base_ptr + v_off;
auto new_v = v_buf_size_orig - v_off;
v_dram_window.bottom_tensor_view_.buf_.buffer_size_ = new_v > 0 ? new_v : kPageBlockSize * kHeadDim;
v_dram_window.init_raw();
v_dram_window.set_window_origin({0, 0});
}
}
// prefetch K tile
constexpr index_t k0_loops = 1;
constexpr index_t k1_loops = 1;
@@ -497,15 +533,33 @@ struct UnifiedAttentionPipeline
constexpr int V_mem_su_ld_insts = v_dram_window.get_num_of_access();
// Helper lambda to rebase window pointer (avoids int32 overflow)
// This is expensive (calls init_raw), so we minimize calls via lazy rebasing
auto rebase_window = [](auto& window, auto* base_ptr, long_index_t elem_offset,
auto buf_size_orig) {
window.bottom_tensor_view_.buf_.p_data_ = base_ptr + elem_offset;
auto new_size = buf_size_orig - elem_offset;
window.bottom_tensor_view_.buf_.buffer_size_ = new_size > 0 ? new_size : kPageBlockSize * kHeadDim;
window.init_raw();
window.init_raw(); // Expensive: rebuilds AMD buffer resource descriptor
window.set_window_origin({0, 0});
};
// Lazy rebasing: track which block we're currently rebased to
// Only call rebase_window (expensive init_raw) when we drift too far from base
// Threshold: rebase when offset from base would exceed 1 billion (half of int32_max)
// For d64, block_size=32: threshold = 1B / (32 * 64) = ~488,281 blocks
// This is compile-time constant, allowing compiler to optimize
constexpr long_index_t kRebaseThreshold = 1000000000L / (kPageBlockSize * kHeadDim);
[[maybe_unused]] index_t k_base_block = 0;
[[maybe_unused]] index_t v_base_block = 0;
if constexpr(kNeedsRebasing || (kMaxNumBlocks == -1))
{
if(use_ptr_rebase)
{
k_base_block = kv_blk_idx_initial;
v_base_block = kv_blk_idx_initial;
}
}
// Page block index tracking
// const index_t kv_page_size_in_blocks =
// PageSize / kPageBlockSize;
@@ -518,20 +572,42 @@ struct UnifiedAttentionPipeline
index_t k_page_blk_idx =
block_tables_ptr_[block_table_offset + (k_block_idx / kv_page_size_in_blocks)];
if(use_ptr_rebase)
if constexpr(kNeedsRebasing || (kMaxNumBlocks == -1))
{
long_index_t k_row =
static_cast<long_index_t>(k_page_blk_idx) * PageSize +
(k_block_idx % kv_page_size_in_blocks) * kPageBlockSize;
rebase_window(k_dram_window, k_base_ptr, k_row * k_row_stride, k_buf_size_orig);
}
else
{
k_dram_window.set_window_origin(
{k_page_blk_idx * PageSize +
(k_block_idx % kv_page_size_in_blocks) * kPageBlockSize,
0});
if(use_ptr_rebase)
{
// Lazy rebasing: only call expensive rebase_window when drifting too far from base
long_index_t offset_from_base = static_cast<long_index_t>(k_page_blk_idx) - k_base_block;
if(offset_from_base < 0) offset_from_base = -offset_from_base; // abs value
if(offset_from_base > kRebaseThreshold)
{
// Too far from base, rebase to current block (expensive: calls init_raw)
k_base_block = k_page_blk_idx;
long_index_t k_row =
static_cast<long_index_t>(k_page_blk_idx) * PageSize +
(k_block_idx % kv_page_size_in_blocks) * kPageBlockSize;
rebase_window(k_dram_window, k_base_ptr, k_row * k_row_stride, k_buf_size_orig);
}
else
{
// Close to base, just update window origin (cheap: no init_raw)
long_index_t k_row =
static_cast<long_index_t>(k_page_blk_idx) * PageSize +
(k_block_idx % kv_page_size_in_blocks) * kPageBlockSize;
long_index_t base_row = static_cast<long_index_t>(k_base_block) * PageSize;
k_dram_window.set_window_origin({static_cast<index_t>(k_row - base_row), 0});
}
return;
}
}
// Fast path when rebasing not needed (kMaxNumBlocks is small)
k_dram_window.set_window_origin(
{k_page_blk_idx * PageSize +
(k_block_idx % kv_page_size_in_blocks) * kPageBlockSize,
0});
};
auto V_mem_load = [&](auto v_lds_write_idx) {
@@ -540,20 +616,42 @@ struct UnifiedAttentionPipeline
index_t v_page_blk_idx =
block_tables_ptr_[block_table_offset + (v_block_idx / kv_page_size_in_blocks)];
if(use_ptr_rebase)
if constexpr(kNeedsRebasing || (kMaxNumBlocks == -1))
{
long_index_t v_row =
static_cast<long_index_t>(v_page_blk_idx) * PageSize +
(v_block_idx % kv_page_size_in_blocks) * kPageBlockSize;
rebase_window(v_dram_window, v_base_ptr, v_row * v_row_stride, v_buf_size_orig);
}
else
{
v_dram_window.set_window_origin(
{v_page_blk_idx * PageSize +
(v_block_idx % kv_page_size_in_blocks) * kPageBlockSize,
0});
if(use_ptr_rebase)
{
// Lazy rebasing: only call expensive rebase_window when drifting too far from base
long_index_t offset_from_base = static_cast<long_index_t>(v_page_blk_idx) - v_base_block;
if(offset_from_base < 0) offset_from_base = -offset_from_base; // abs value
if(offset_from_base > kRebaseThreshold)
{
// Too far from base, rebase to current block (expensive: calls init_raw)
v_base_block = v_page_blk_idx;
long_index_t v_row =
static_cast<long_index_t>(v_page_blk_idx) * PageSize +
(v_block_idx % kv_page_size_in_blocks) * kPageBlockSize;
rebase_window(v_dram_window, v_base_ptr, v_row * v_row_stride, v_buf_size_orig);
}
else
{
// Close to base, just update window origin (cheap: no init_raw)
long_index_t v_row =
static_cast<long_index_t>(v_page_blk_idx) * PageSize +
(v_block_idx % kv_page_size_in_blocks) * kPageBlockSize;
long_index_t base_row = static_cast<long_index_t>(v_base_block) * PageSize;
v_dram_window.set_window_origin({static_cast<index_t>(v_row - base_row), 0});
}
return;
}
}
// Fast path when rebasing not needed (kMaxNumBlocks is small)
v_dram_window.set_window_origin(
{v_page_blk_idx * PageSize +
(v_block_idx % kv_page_size_in_blocks) * kPageBlockSize,
0});
};
auto K_lds_load = [&](auto k_lds_read_idx) {

4
include/ck_tile/ops/unified_attention/pipeline/unified_attention_pipeline_problem.hpp
View File

@@ -19,7 +19,8 @@ template <typename QDataType_,
typename ODataType_,
typename UnifiedAttentionShape_,
typename FmhaMask_,
typename Traits_>
typename Traits_,
index_t MaxNumBlocks_ = -1>
struct UnifiedAttentionPipelineProblem
{
// TODO kM0 and KN1??
@@ -41,6 +42,7 @@ struct UnifiedAttentionPipelineProblem
using Traits = remove_cvref_t<Traits_>;
using FmhaMask = remove_cvref_t<FmhaMask_>;
static constexpr index_t kMaxNumBlocks = MaxNumBlocks_;
static constexpr index_t kNumGemm0Warps = UnifiedAttentionShape::NumGemm0Warps;
static constexpr index_t kNumGemm1Warps = UnifiedAttentionShape::NumGemm1Warps;
static constexpr index_t kBlockSize = UnifiedAttentionShape::NumWarps * get_warp_size();