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Commit b618806b authored by Po Yen, Chen's avatar Po Yen, Chen
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Reuse C++ paged-attention interface

parent 686448c9
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Showing with 84 additions and 319 deletions
example/ck_tile/18_paged_attention/CMakeLists.txt
View file @ b618806b
...@@ -45,20 +45,15 @@ find_package(Torch REQUIRED) ...@@ -45,20 +45,15 @@ find_package(Torch REQUIRED)
add_executable(${EXAMPLE_NAME} EXCLUDE_FROM_ALL add_executable(${EXAMPLE_NAME} EXCLUDE_FROM_ALL
main.cpp main.cpp
itfs/paged_attention.cpp itfs/paged_attention.cpp
# py_itfs/paged_attention.cu
) )
target_include_directories(${EXAMPLE_NAME} target_include_directories(${EXAMPLE_NAME}
AFTER PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include) SYSTEM AFTER
target_include_directories(${EXAMPLE_NAME} PRIVATE ${TORCH_INCLUDE_DIRS}
SYSTEM PRIVATE ${TORCH_INCLUDE_DIRS}) PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include # ignore compilation warnings in kernel implementation
)
target_link_libraries(${EXAMPLE_NAME} "${TORCH_LIBRARIES}") target_link_libraries(${EXAMPLE_NAME} "${TORCH_LIBRARIES}")
target_compile_definitions(${EXAMPLE_NAME} PRIVATE USE_ROCM) target_compile_definitions(${EXAMPLE_NAME} PRIVATE USE_ROCM)
target_compile_options(${EXAMPLE_NAME} PRIVATE target_compile_options(${EXAMPLE_NAME}
${TORCH_CXX_FLAGS} PRIVATE ${TORCH_CXX_FLAGS}
-Wno-undefined-reinterpret-cast
-Wno-unused-variable
-Wno-unused-parameter
-Wno-old-style-cast
-Wno-deprecated-copy
-Wno-shadow
-Wno-conditional-uninitialized
) )
\ No newline at end of file
example/ck_tile/18_paged_attention/include/paged_attention.hpp
View file @ b618806b
#pragma once #pragma once
#include <hip/hip_runtime.h>
#include <iostream> #include <iostream>
#include <hip/hip_runtime.h>
namespace native {
enum class ScalarType { enum class ScalarType {
Half, Half,
BFloat16, BFloat16,
...@@ -50,7 +52,7 @@ struct paged_attention_args { ...@@ -50,7 +52,7 @@ struct paged_attention_args {
void* value_cache_ptr; void* value_cache_ptr;
int* block_tables_ptr; int* block_tables_ptr;
int* context_lens_ptr; int* context_lens_ptr;
float* fp8_out_scale_ptr; const float* fp8_out_scale_ptr;
void* out_ptr; void* out_ptr;
int64_t block_size; int64_t block_size;
...@@ -61,8 +63,9 @@ struct paged_attention_args { ...@@ -61,8 +63,9 @@ struct paged_attention_args {
int64_t partition_size; int64_t partition_size;
}; };
void paged_attention_api( void paged_attention(
const paged_attention_traits& traits, const paged_attention_traits& traits,
const paged_attention_args& args, const paged_attention_args& args,
hipStream_t stream hipStream_t stream
); );
}
\ No newline at end of file
example/ck_tile/18_paged_attention/itfs/paged_attention.cpp
View file @ b618806b
...@@ -16,9 +16,10 @@ ...@@ -16,9 +16,10 @@
#include <torch/torch.h> #include <torch/torch.h>
#include <hip/hip_runtime.h>
#include "paged_attention.hpp" #include "paged_attention.hpp"
#include "kernel/paged_attention_kernel.hpp" #include "kernel/paged_attention_kernel.hpp"
#include "ck_tile/host/hip_check_error.hpp"
#define LAUNCH_CUSTOM_ATTENTION(GQA_RATIO) \ #define LAUNCH_CUSTOM_ATTENTION(GQA_RATIO) \
paged_attention_ll4mi_QKV_kernel<T, KVT, KV_DTYPE, OUTT, BLOCK_SIZE, \ paged_attention_ll4mi_QKV_kernel<T, KVT, KV_DTYPE, OUTT, BLOCK_SIZE, \
...@@ -37,11 +38,11 @@ ...@@ -37,11 +38,11 @@
out_ptr, args.exp_sums_ptr, args.max_logits_ptr, tmp_out_ptr, \ out_ptr, args.exp_sums_ptr, args.max_logits_ptr, tmp_out_ptr, \
args.context_lens_ptr, max_num_partitions, args.fp8_out_scale_ptr); args.context_lens_ptr, max_num_partitions, args.fp8_out_scale_ptr);
namespace {
template <typename T, typename KVT, vllm::Fp8KVCacheDataType KV_DTYPE, template <typename T, typename KVT, vllm::Fp8KVCacheDataType KV_DTYPE,
int BLOCK_SIZE, int HEAD_SIZE, typename OUTT, int PARTITION_SIZE> int BLOCK_SIZE, int HEAD_SIZE, typename OUTT, int PARTITION_SIZE>
void paged_attention_custom_launcher( void paged_attention_custom_launcher(
const paged_attention_traits& traits, const native::paged_attention_args& args,
const paged_attention_args& args,
hipStream_t stream) { hipStream_t stream) {
T* tmp_out_ptr = reinterpret_cast<T*>(args.tmp_out_ptr); T* tmp_out_ptr = reinterpret_cast<T*>(args.tmp_out_ptr);
...@@ -156,11 +157,12 @@ void paged_attention_custom_launcher( ...@@ -156,11 +157,12 @@ void paged_attention_custom_launcher(
} }
} }
} }
}
#define CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, \ #define CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, \
PSIZE) \ PSIZE) \
paged_attention_custom_launcher<T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, \ paged_attention_custom_launcher<T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, \
PSIZE>(traits, args, stream); PSIZE>(args, stream);
#define CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, \ #define CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, \
OUTT) \ OUTT) \
...@@ -218,59 +220,13 @@ void paged_attention_custom_launcher( ...@@ -218,59 +220,13 @@ void paged_attention_custom_launcher(
break; \ break; \
} }
/* namespace native {
void paged_attention( void paged_attention(
torch::Tensor& out, // [num_seqs, num_heads, head_size]
torch::Tensor& exp_sums, // [num_seqs, num_heads, max_num_partitions]
torch::Tensor& max_logits, // [num_seqs, num_heads, max_num_partitions]
torch::Tensor&
tmp_out, // [num_seqs, num_heads, max_num_partitions, head_size]
torch::Tensor& query, // [num_seqs, num_heads, head_size]
torch::Tensor&
key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
torch::Tensor&
value_cache, // [num_blocks, num_heads, head_size, block_size]
int64_t num_kv_heads, double scale,
torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
torch::Tensor& context_lens, // [num_seqs]
int64_t block_size, int64_t max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype, double k_scale, double v_scale,
const c10::optional<torch::Tensor>& fp8_out_scale, int64_t partition_size) {
const int head_size = query.size(2);
if (kv_cache_dtype == "auto") {
if (query.dtype() == at::ScalarType::Half) {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, _Float16,
vllm::Fp8KVCacheDataType::kAuto);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(__hip_bfloat16, __hip_bfloat16,
vllm::Fp8KVCacheDataType::kAuto);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
} else if (kv_cache_dtype == "fp8" || kv_cache_dtype == "fp8_e4m3") {
if (query.dtype() == at::ScalarType::Half) {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, uint8_t,
vllm::Fp8KVCacheDataType::kFp8E4M3);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(__hip_bfloat16, uint8_t,
vllm::Fp8KVCacheDataType::kFp8E4M3);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
} else {
TORCH_CHECK(false, "Unsupported KV cache dtype: ", kv_cache_dtype);
}
}
*/
void paged_attention_api(
const paged_attention_traits& traits, const paged_attention_traits& traits,
const paged_attention_args& args, const paged_attention_args& args,
hipStream_t stream hipStream_t stream
) )
{ {
const int head_size = args.head_size;
if (traits.kv_cache_dtype == "auto") { if (traits.kv_cache_dtype == "auto") {
if (traits.q_type == ScalarType::Half) { if (traits.q_type == ScalarType::Half) {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, _Float16, CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, _Float16,
...@@ -295,3 +251,4 @@ void paged_attention_api( ...@@ -295,3 +251,4 @@ void paged_attention_api(
TORCH_CHECK(false, "Unsupported KV cache dtype: ", traits.kv_cache_dtype); TORCH_CHECK(false, "Unsupported KV cache dtype: ", traits.kv_cache_dtype);
} }
} }
}
example/ck_tile/18_paged_attention/py_itfs/paged_attention.cu
View file @ b618806b
...@@ -13,237 +13,13 @@ ...@@ -13,237 +13,13 @@
* See the License for the specific language governing permissions and * See the License for the specific language governing permissions and
* limitations under the License. * limitations under the License.
*/ */
#include <torch/torch.h>
#include "kernel/attention_kernel.hpp" #include <hip/hip_runtime.h>
#define LAUNCH_CUSTOM_ATTENTION(GQA_RATIO) \ #include "ck_tile/host/hip_check_error.hpp"
paged_attention_ll4mi_QKV_kernel<T, KVT, KV_DTYPE, OUTT, BLOCK_SIZE, \
HEAD_SIZE, NTHR, GQA_RATIO> \
<<<grid, block, 0, stream>>>( \
query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale, \
block_tables_ptr, context_lens_ptr, max_num_blocks_per_seq, \
alibi_slopes_ptr, q_stride, kv_block_stride, kv_head_stride, \
exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr, max_ctx_blocks, \
k_scale, v_scale, fp8_out_scale_ptr);
#define LAUNCH_CUSTOM_REDUCTION(NPAR_LOOPS) \ #include "paged_attention.hpp"
paged_attention_ll4mi_reduce_kernel<T, OUTT, HEAD_SIZE, HEAD_SIZE, \
PARTITION_SIZE, NPAR_LOOPS> \
<<<reduce_grid, reduce_block, 0, stream>>>( \
out_ptr, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, \
context_lens_ptr, max_num_partitions, fp8_out_scale_ptr);
template <typename T, typename KVT, vllm::Fp8KVCacheDataType KV_DTYPE,
int BLOCK_SIZE, int HEAD_SIZE, typename OUTT, int PARTITION_SIZE>
void paged_attention_custom_launcher(
torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
torch::Tensor& value_cache, const int num_kv_heads, float scale,
torch::Tensor& block_tables, torch::Tensor& context_lens,
int max_context_len, const c10::optional<torch::Tensor>& alibi_slopes,
float k_scale, float v_scale,
const c10::optional<torch::Tensor>& fp8_out_scale) {
int num_seqs = query.size(0);
int num_heads = query.size(1);
int head_size = query.size(2);
int max_num_blocks_per_seq = block_tables.size(1);
int q_stride = query.stride(0);
int kv_block_stride = key_cache.stride(0);
int kv_head_stride = key_cache.stride(1);
// NOTE: alibi_slopes is optional.
const float* alibi_slopes_ptr =
alibi_slopes
? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
: nullptr;
float* exp_sums_ptr = reinterpret_cast<float*>(exp_sums.data_ptr());
float* max_logits_ptr = reinterpret_cast<float*>(max_logits.data_ptr());
T* tmp_out_ptr = reinterpret_cast<T*>(tmp_out.data_ptr());
T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
KVT* key_cache_ptr = reinterpret_cast<KVT*>(key_cache.data_ptr());
KVT* value_cache_ptr = reinterpret_cast<KVT*>(value_cache.data_ptr());
int* block_tables_ptr = block_tables.data_ptr<int>();
int* context_lens_ptr = context_lens.data_ptr<int>();
// NOTE: fp8_out_scale is optional.
const float* fp8_out_scale_ptr =
fp8_out_scale
? reinterpret_cast<const float*>(fp8_out_scale.value().data_ptr())
: nullptr;
OUTT* out_ptr = reinterpret_cast<OUTT*>(out.data_ptr());
const int max_ctx_blocks = DIVIDE_ROUND_UP(max_context_len, BLOCK_SIZE);
const int max_num_partitions =
DIVIDE_ROUND_UP(max_context_len, PARTITION_SIZE);
const int gqa_ratio = num_heads / num_kv_heads;
assert(num_heads % num_kv_heads == 0);
assert(head_size == HEAD_SIZE);
constexpr int NTHR = PARTITION_SIZE;
dim3 grid(num_seqs, max_num_partitions, num_kv_heads);
dim3 block(NTHR);
const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
switch (gqa_ratio) {
case 1:
LAUNCH_CUSTOM_ATTENTION(1);
break;
case 2:
LAUNCH_CUSTOM_ATTENTION(2);
break;
case 3:
LAUNCH_CUSTOM_ATTENTION(3);
break;
case 4:
LAUNCH_CUSTOM_ATTENTION(4);
break;
case 5:
LAUNCH_CUSTOM_ATTENTION(5);
break;
case 6:
LAUNCH_CUSTOM_ATTENTION(6);
break;
case 7:
LAUNCH_CUSTOM_ATTENTION(7);
break;
case 8:
LAUNCH_CUSTOM_ATTENTION(8);
break;
case 9:
LAUNCH_CUSTOM_ATTENTION(9);
break;
case 10:
LAUNCH_CUSTOM_ATTENTION(10);
break;
case 11:
LAUNCH_CUSTOM_ATTENTION(11);
break;
case 12:
LAUNCH_CUSTOM_ATTENTION(12);
break;
case 13:
LAUNCH_CUSTOM_ATTENTION(13);
break;
case 14:
LAUNCH_CUSTOM_ATTENTION(14);
break;
case 15:
LAUNCH_CUSTOM_ATTENTION(15);
break;
case 16:
LAUNCH_CUSTOM_ATTENTION(16);
break;
default:
TORCH_CHECK(false, "Unsupported gqa ratio: ", gqa_ratio);
break;
}
// reduction kernel is only required if max_context_len > partition size,
// otherwise main kernel writes directly to final output
// note there are cases with graphing where max_context_len is the max
// supported by graphing, not the actual max among all the sequences: in that
// case reduction kernel will still run but return immediately
if (max_context_len > PARTITION_SIZE) {
dim3 reduce_grid(num_heads, num_seqs);
dim3 reduce_block(head_size);
const int npar_loops = DIVIDE_ROUND_UP(max_num_partitions, WARP_SIZE);
// support upto 8*64*256=128K context length
switch (npar_loops) {
case 1:
LAUNCH_CUSTOM_REDUCTION(1);
break;
case 2:
LAUNCH_CUSTOM_REDUCTION(2);
break;
case 3:
LAUNCH_CUSTOM_REDUCTION(3);
break;
case 4:
LAUNCH_CUSTOM_REDUCTION(4);
break;
case 5:
LAUNCH_CUSTOM_REDUCTION(5);
break;
case 6:
LAUNCH_CUSTOM_REDUCTION(6);
break;
case 7:
LAUNCH_CUSTOM_REDUCTION(7);
break;
case 8:
LAUNCH_CUSTOM_REDUCTION(8);
break;
default:
TORCH_CHECK(false, "Unsupported npar_loops: ", npar_loops);
break;
}
}
}
#define CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, \
PSIZE) \
paged_attention_custom_launcher<T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, \
PSIZE>( \
out, exp_sums, max_logits, tmp_out, query, key_cache, value_cache, \
num_kv_heads, scale, block_tables, context_lens, max_context_len, \
alibi_slopes, k_scale, v_scale, fp8_out_scale);
#define CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, \
OUTT) \
switch (partition_size) { \
case 256: \
CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, 256); \
break; \
case 512: \
CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, 512); \
break; \
default: \
TORCH_CHECK(false, "Unsupported partition size: ", partition_size); \
break; \
}
#if defined(__HIPCC__) && defined(__gfx90a__)
#define CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE) \
if (fp8_out_scale) { \
TORCH_CHECK(false, "fp8 out scale unsupported for gfx90a"); \
} else { \
CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, T); \
}
#else
#define CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE) \
if (fp8_out_scale) { \
CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, \
uint8_t); \
} else { \
CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, T); \
}
#endif
#define CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, HEAD_SIZE) \
switch (block_size) { \
case 16: \
CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, 16, HEAD_SIZE); \
break; \
case 32: \
CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, 32, HEAD_SIZE); \
break; \
default: \
TORCH_CHECK(false, "Unsupported block size: ", block_size); \
break; \
}
#define CALL_CUSTOM_LAUNCHER_BLK_HEAD(T, KVT, KV_DTYPE) \
switch (head_size) { \
case 64: \
CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, 64); \
break; \
case 128: \
CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, 128); \
break; \
default: \
TORCH_CHECK(false, "Unsupported head size: ", head_size); \
break; \
}
void paged_attention( void paged_attention(
torch::Tensor& out, // [num_seqs, num_heads, head_size] torch::Tensor& out, // [num_seqs, num_heads, head_size]
...@@ -263,28 +39,62 @@ void paged_attention( ...@@ -263,28 +39,62 @@ void paged_attention(
const c10::optional<torch::Tensor>& alibi_slopes, const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype, double k_scale, double v_scale, const std::string& kv_cache_dtype, double k_scale, double v_scale,
const c10::optional<torch::Tensor>& fp8_out_scale, int64_t partition_size) { const c10::optional<torch::Tensor>& fp8_out_scale, int64_t partition_size) {
const int head_size = query.size(2);
if (kv_cache_dtype == "auto") { native::paged_attention_traits traits;
if (query.dtype() == at::ScalarType::Half) {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, _Float16, traits.q_type = (
vllm::Fp8KVCacheDataType::kAuto); query.dtype() == at::ScalarType::Half ? native::ScalarType::Half
} else if (query.dtype() == at::ScalarType::BFloat16) { : native::ScalarType::BFloat16
CALL_CUSTOM_LAUNCHER_BLK_HEAD(__hip_bfloat16, __hip_bfloat16, );
vllm::Fp8KVCacheDataType::kAuto); traits.kv_cache_dtype = kv_cache_dtype;
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype()); native::paged_attention_args args;
}
} else if (kv_cache_dtype == "fp8" || kv_cache_dtype == "fp8_e4m3") { args.head_size = query.size(2);
if (query.dtype() == at::ScalarType::Half) {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, uint8_t, args.num_seqs = query.size(0);
vllm::Fp8KVCacheDataType::kFp8E4M3); args.num_heads = query.size(1);
} else if (query.dtype() == at::ScalarType::BFloat16) { args.head_size = query.size(2);
CALL_CUSTOM_LAUNCHER_BLK_HEAD(__hip_bfloat16, uint8_t, args.max_num_blocks_per_seq = block_tables.size(1);
vllm::Fp8KVCacheDataType::kFp8E4M3); args.q_stride = query.stride(0);
} else { args.kv_block_stride = key_cache.stride(0);
TORCH_CHECK(false, "Unsupported data type: ", query.dtype()); args.kv_head_stride = key_cache.stride(1);
}
} else { // NOTE: alibi_slopes is optional.
TORCH_CHECK(false, "Unsupported KV cache dtype: ", kv_cache_dtype); args.alibi_slopes_ptr =
} alibi_slopes
} ? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
\ No newline at end of file : nullptr;
args.exp_sums_ptr = reinterpret_cast<float*>(exp_sums.data_ptr());
args.max_logits_ptr = reinterpret_cast<float*>(max_logits.data_ptr());
args.tmp_out_ptr = tmp_out.data_ptr();
args.query_ptr = query.data_ptr();
args.key_cache_ptr = key_cache.data_ptr();
args.value_cache_ptr = value_cache.data_ptr();
args.block_tables_ptr = block_tables.data_ptr<int>();
args.context_lens_ptr = context_lens.data_ptr<int>();
// NOTE: fp8_out_scale is optional.
args.fp8_out_scale_ptr =
fp8_out_scale
? reinterpret_cast<const float*>(fp8_out_scale.value().data_ptr())
: nullptr;
args.out_ptr = out.data_ptr();
args.block_size = block_size;
args.max_context_len = max_context_len;
args.num_kv_heads = num_kv_heads;
args.partition_size = partition_size;
args.scale = scale;
args.k_scale = k_scale;
args.v_scale = v_scale;
hipStream_t stream = nullptr;
HIP_CHECK_ERROR(hipStreamCreate(&stream));
native::paged_attention(traits, args, stream);
HIP_CHECK_ERROR(hipStreamDestroy(stream));
}
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