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Commit cc6d659f authored by Po Yen, Chen's avatar Po Yen, Chen
Browse files

Re-format interface sources

parent 5a683756
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Showing with 279 additions and 302 deletions
example/ck_tile/18_paged_attention/include/paged_attention.hpp
View file @ cc6d659f
...@@ -5,31 +5,37 @@ ...@@ -5,31 +5,37 @@
#include <hip/hip_runtime.h> #include <hip/hip_runtime.h>
namespace native { namespace native {
enum class ScalarType { enum class ScalarType
{
Half, Half,
BFloat16, BFloat16,
}; };
inline std::ostream& operator<<(std::ostream& stream, ScalarType scalar_type) { inline std::ostream& operator<<(std::ostream& stream, ScalarType scalar_type)
switch(scalar_type) { {
switch(scalar_type)
{
case ScalarType::Half: stream << "Half"; break; case ScalarType::Half: stream << "Half"; break;
case ScalarType::BFloat16: stream << "BFloat16"; break; case ScalarType::BFloat16: stream << "BFloat16"; break;
} }
return stream; return stream;
} }
enum class Fp8KVCacheDataType { enum class Fp8KVCacheDataType
{
kAuto = 0, kAuto = 0,
kFp8E4M3 = 1, kFp8E4M3 = 1,
kFp8E5M2 = 2, kFp8E5M2 = 2,
}; };
struct paged_attention_traits { struct paged_attention_traits
{
ScalarType q_type; ScalarType q_type;
std::string kv_cache_dtype; std::string kv_cache_dtype;
}; };
struct paged_attention_args { struct paged_attention_args
{
int head_size; int head_size;
int num_seqs; int num_seqs;
...@@ -63,9 +69,7 @@ struct paged_attention_args { ...@@ -63,9 +69,7 @@ struct paged_attention_args {
int64_t partition_size; int64_t partition_size;
}; };
void paged_attention( 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);
); } // namespace native
} \ No newline at end of file
\ No newline at end of file
example/ck_tile/18_paged_attention/itfs/paged_attention.cpp
View file @ cc6d659f
...@@ -22,28 +22,55 @@ ...@@ -22,28 +22,55 @@
#include "kernel/paged_attention_kernel.hpp" #include "kernel/paged_attention_kernel.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, \
HEAD_SIZE, NTHR, GQA_RATIO> \ KVT, \
<<<grid, block, 0, stream>>>( \ KV_DTYPE, \
query_ptr, key_cache_ptr, value_cache_ptr, args.num_kv_heads, args.scale, \ OUTT, \
args.block_tables_ptr, args.context_lens_ptr, args.max_num_blocks_per_seq, \ BLOCK_SIZE, \
args.alibi_slopes_ptr, args.q_stride, args.kv_block_stride, args.kv_head_stride, \ HEAD_SIZE, \
args.exp_sums_ptr, args.max_logits_ptr, tmp_out_ptr, out_ptr, max_ctx_blocks, \ NTHR, \
args.k_scale, args.v_scale, args.fp8_out_scale_ptr); GQA_RATIO> \
<<<grid, block, 0, stream>>>(query_ptr, \
key_cache_ptr, \
value_cache_ptr, \
args.num_kv_heads, \
args.scale, \
args.block_tables_ptr, \
args.context_lens_ptr, \
args.max_num_blocks_per_seq, \
args.alibi_slopes_ptr, \
args.q_stride, \
args.kv_block_stride, \
args.kv_head_stride, \
args.exp_sums_ptr, \
args.max_logits_ptr, \
tmp_out_ptr, \
out_ptr, \
max_ctx_blocks, \
args.k_scale, \
args.v_scale, \
args.fp8_out_scale_ptr);
#define LAUNCH_CUSTOM_REDUCTION(NPAR_LOOPS) \ #define LAUNCH_CUSTOM_REDUCTION(NPAR_LOOPS) \
paged_attention_ll4mi_reduce_kernel<T, OUTT, HEAD_SIZE, HEAD_SIZE, \ paged_attention_ll4mi_reduce_kernel<T, OUTT, HEAD_SIZE, HEAD_SIZE, PARTITION_SIZE, NPAR_LOOPS> \
PARTITION_SIZE, NPAR_LOOPS> \ <<<reduce_grid, reduce_block, 0, stream>>>(out_ptr, \
<<<reduce_grid, reduce_block, 0, stream>>>( \ args.exp_sums_ptr, \
out_ptr, args.exp_sums_ptr, args.max_logits_ptr, tmp_out_ptr, \ args.max_logits_ptr, \
args.context_lens_ptr, max_num_partitions, args.fp8_out_scale_ptr); tmp_out_ptr, \
args.context_lens_ptr, \
max_num_partitions, \
args.fp8_out_scale_ptr);
namespace { namespace {
template <typename T, typename KVT, vllm::Fp8KVCacheDataType KV_DTYPE, template <typename T,
int BLOCK_SIZE, int HEAD_SIZE, typename OUTT, int PARTITION_SIZE> typename KVT,
void paged_attention_custom_launcher( vllm::Fp8KVCacheDataType KV_DTYPE,
const native::paged_attention_args& args, int BLOCK_SIZE,
hipStream_t stream) { int HEAD_SIZE,
typename OUTT,
int PARTITION_SIZE>
void paged_attention_custom_launcher(const native::paged_attention_args& args, 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);
T* query_ptr = reinterpret_cast<T*>(args.query_ptr); T* query_ptr = reinterpret_cast<T*>(args.query_ptr);
...@@ -52,8 +79,7 @@ void paged_attention_custom_launcher( ...@@ -52,8 +79,7 @@ void paged_attention_custom_launcher(
OUTT* out_ptr = reinterpret_cast<OUTT*>(args.out_ptr); OUTT* out_ptr = reinterpret_cast<OUTT*>(args.out_ptr);
const int max_ctx_blocks = DIVIDE_ROUND_UP(args.max_context_len, BLOCK_SIZE); const int max_ctx_blocks = DIVIDE_ROUND_UP(args.max_context_len, BLOCK_SIZE);
const int max_num_partitions = const int max_num_partitions = DIVIDE_ROUND_UP(args.max_context_len, PARTITION_SIZE);
DIVIDE_ROUND_UP(args.max_context_len, PARTITION_SIZE);
const int gqa_ratio = args.num_heads / args.num_kv_heads; const int gqa_ratio = args.num_heads / args.num_kv_heads;
assert(args.num_heads % args.num_kv_heads == 0); assert(args.num_heads % args.num_kv_heads == 0);
assert(args.head_size == HEAD_SIZE); assert(args.head_size == HEAD_SIZE);
...@@ -62,58 +88,25 @@ void paged_attention_custom_launcher( ...@@ -62,58 +88,25 @@ void paged_attention_custom_launcher(
dim3 grid(args.num_seqs, max_num_partitions, args.num_kv_heads); dim3 grid(args.num_seqs, max_num_partitions, args.num_kv_heads);
dim3 block(NTHR); dim3 block(NTHR);
switch (gqa_ratio) { switch(gqa_ratio)
case 1: {
LAUNCH_CUSTOM_ATTENTION(1); case 1: LAUNCH_CUSTOM_ATTENTION(1); break;
break; case 2: LAUNCH_CUSTOM_ATTENTION(2); break;
case 2: case 3: LAUNCH_CUSTOM_ATTENTION(3); break;
LAUNCH_CUSTOM_ATTENTION(2); case 4: LAUNCH_CUSTOM_ATTENTION(4); break;
break; case 5: LAUNCH_CUSTOM_ATTENTION(5); break;
case 3: case 6: LAUNCH_CUSTOM_ATTENTION(6); break;
LAUNCH_CUSTOM_ATTENTION(3); case 7: LAUNCH_CUSTOM_ATTENTION(7); break;
break; case 8: LAUNCH_CUSTOM_ATTENTION(8); break;
case 4: case 9: LAUNCH_CUSTOM_ATTENTION(9); break;
LAUNCH_CUSTOM_ATTENTION(4); case 10: LAUNCH_CUSTOM_ATTENTION(10); break;
break; case 11: LAUNCH_CUSTOM_ATTENTION(11); break;
case 5: case 12: LAUNCH_CUSTOM_ATTENTION(12); break;
LAUNCH_CUSTOM_ATTENTION(5); case 13: LAUNCH_CUSTOM_ATTENTION(13); break;
break; case 14: LAUNCH_CUSTOM_ATTENTION(14); break;
case 6: case 15: LAUNCH_CUSTOM_ATTENTION(15); break;
LAUNCH_CUSTOM_ATTENTION(6); case 16: LAUNCH_CUSTOM_ATTENTION(16); break;
break; default: TORCH_CHECK(false, "Unsupported gqa ratio: ", gqa_ratio); 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, // reduction kernel is only required if max_context_len > partition size,
...@@ -121,134 +114,117 @@ void paged_attention_custom_launcher( ...@@ -121,134 +114,117 @@ void paged_attention_custom_launcher(
// note there are cases with graphing where max_context_len is the max // 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 // supported by graphing, not the actual max among all the sequences: in that
// case reduction kernel will still run but return immediately // case reduction kernel will still run but return immediately
if (args.max_context_len > PARTITION_SIZE) { if(args.max_context_len > PARTITION_SIZE)
{
dim3 reduce_grid(args.num_heads, args.num_seqs); dim3 reduce_grid(args.num_heads, args.num_seqs);
dim3 reduce_block(args.head_size); dim3 reduce_block(args.head_size);
const int npar_loops = DIVIDE_ROUND_UP(max_num_partitions, WARP_SIZE); const int npar_loops = DIVIDE_ROUND_UP(max_num_partitions, WARP_SIZE);
// support upto 8*64*256=128K context length // support upto 8*64*256=128K context length
switch (npar_loops) { switch(npar_loops)
case 1: {
LAUNCH_CUSTOM_REDUCTION(1); case 1: LAUNCH_CUSTOM_REDUCTION(1); break;
break; case 2: LAUNCH_CUSTOM_REDUCTION(2); break;
case 2: case 3: LAUNCH_CUSTOM_REDUCTION(3); break;
LAUNCH_CUSTOM_REDUCTION(2); case 4: LAUNCH_CUSTOM_REDUCTION(4); break;
break; case 5: LAUNCH_CUSTOM_REDUCTION(5); break;
case 3: case 6: LAUNCH_CUSTOM_REDUCTION(6); break;
LAUNCH_CUSTOM_REDUCTION(3); case 7: LAUNCH_CUSTOM_REDUCTION(7); break;
break; case 8: LAUNCH_CUSTOM_REDUCTION(8); break;
case 4: default: TORCH_CHECK(false, "Unsupported npar_loops: ", npar_loops); break;
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;
} }
} }
} }
} } // namespace
#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, PSIZE>(args, \
paged_attention_custom_launcher<T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, \ stream);
PSIZE>(args, stream);
#define CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT) \
#define CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, \ switch(args.partition_size) \
OUTT) \ { \
switch (args.partition_size) { \ case 256: CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, 256); break; \
case 256: \ case 512: CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, 512); break; \
CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, 256); \ default: TORCH_CHECK(false, "Unsupported partition size: ", args.partition_size); break; \
break; \
case 512: \
CALL_CUSTOM_LAUNCHER(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, OUTT, 512); \
break; \
default: \
TORCH_CHECK(false, "Unsupported partition size: ", args.partition_size); \
break; \
} }
#if defined(__HIPCC__) && defined(__gfx90a__) #if defined(__HIPCC__) && defined(__gfx90a__)
#define CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE) \ #define CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE) \
if (args.fp8_out_scale_ptr) { \ if(args.fp8_out_scale_ptr) \
{ \
TORCH_CHECK(false, "fp8 out scale unsupported for gfx90a"); \ TORCH_CHECK(false, "fp8 out scale unsupported for gfx90a"); \
} else { \ } \
else \
{ \
CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, T); \ CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, T); \
} }
#else #else
#define CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE) \ #define CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE) \
if (args.fp8_out_scale_ptr) { \ if(args.fp8_out_scale_ptr) \
CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, \ { \
uint8_t); \ CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, uint8_t); \
} else { \ } \
else \
{ \
CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, T); \ CALL_CUSTOM_LAUNCHER_PSIZE(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, T); \
} }
#endif #endif
#define CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, HEAD_SIZE) \ #define CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, HEAD_SIZE) \
switch (args.block_size) { \ switch(args.block_size) \
case 16: \ { \
CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, 16, HEAD_SIZE); \ case 16: CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, 16, HEAD_SIZE); break; \
break; \ case 32: CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, 32, HEAD_SIZE); break; \
case 32: \ default: TORCH_CHECK(false, "Unsupported block size: ", args.block_size); break; \
CALL_CUSTOM_LAUNCHER_OUT(T, KVT, KV_DTYPE, 32, HEAD_SIZE); \
break; \
default: \
TORCH_CHECK(false, "Unsupported block size: ", args.block_size); \
break; \
} }
#define CALL_CUSTOM_LAUNCHER_BLK_HEAD(T, KVT, KV_DTYPE) \ #define CALL_CUSTOM_LAUNCHER_BLK_HEAD(T, KVT, KV_DTYPE) \
switch (args.head_size) { \ switch(args.head_size) \
case 64: \ { \
CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, 64); \ case 64: CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, 64); break; \
break; \ case 128: CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, 128); break; \
case 128: \ default: TORCH_CHECK(false, "Unsupported head size: ", args.head_size); break; \
CALL_CUSTOM_LAUNCHER_BLK(T, KVT, KV_DTYPE, 128); \
break; \
default: \
TORCH_CHECK(false, "Unsupported head size: ", args.head_size); \
break; \
} }
namespace native { namespace native {
void paged_attention( 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)
)
{ {
if (traits.kv_cache_dtype == "auto") { if(traits.kv_cache_dtype == "auto")
if (traits.q_type == ScalarType::Half) { {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, _Float16, if(traits.q_type == ScalarType::Half)
vllm::Fp8KVCacheDataType::kAuto); {
} else if (traits.q_type == ScalarType::BFloat16) { CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, _Float16, vllm::Fp8KVCacheDataType::kAuto);
CALL_CUSTOM_LAUNCHER_BLK_HEAD(__hip_bfloat16, __hip_bfloat16, }
vllm::Fp8KVCacheDataType::kAuto); else if(traits.q_type == ScalarType::BFloat16)
} else { {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(
__hip_bfloat16, __hip_bfloat16, vllm::Fp8KVCacheDataType::kAuto);
}
else
{
TORCH_CHECK(false, "Unsupported data type: ", traits.q_type); TORCH_CHECK(false, "Unsupported data type: ", traits.q_type);
} }
} else if (traits.kv_cache_dtype == "fp8" || traits.kv_cache_dtype == "fp8_e4m3") { }
if (traits.q_type == ScalarType::Half) { else if(traits.kv_cache_dtype == "fp8" || traits.kv_cache_dtype == "fp8_e4m3")
CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, uint8_t, {
vllm::Fp8KVCacheDataType::kFp8E4M3); if(traits.q_type == ScalarType::Half)
} else if (traits.q_type == ScalarType::BFloat16) { {
CALL_CUSTOM_LAUNCHER_BLK_HEAD(__hip_bfloat16, uint8_t, CALL_CUSTOM_LAUNCHER_BLK_HEAD(_Float16, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
vllm::Fp8KVCacheDataType::kFp8E4M3); }
} else { else if(traits.q_type == ScalarType::BFloat16)
{
CALL_CUSTOM_LAUNCHER_BLK_HEAD(
__hip_bfloat16, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
}
else
{
TORCH_CHECK(false, "Unsupported data type: ", traits.q_type); TORCH_CHECK(false, "Unsupported data type: ", traits.q_type);
} }
} else { }
else
{
TORCH_CHECK(false, "Unsupported KV cache dtype: ", traits.kv_cache_dtype); TORCH_CHECK(false, "Unsupported KV cache dtype: ", traits.kv_cache_dtype);
} }
} }
} } // namespace native
example/ck_tile/18_paged_attention/py_itfs/paged_attention.cu
View file @ cc6d659f
...@@ -25,27 +25,28 @@ void paged_attention( ...@@ -25,27 +25,28 @@ void paged_attention(
torch::Tensor& out, // [num_seqs, num_heads, head_size] torch::Tensor& out, // [num_seqs, num_heads, head_size]
torch::Tensor& exp_sums, // [num_seqs, num_heads, max_num_partitions] torch::Tensor& exp_sums, // [num_seqs, num_heads, max_num_partitions]
torch::Tensor& max_logits, // [num_seqs, num_heads, max_num_partitions] torch::Tensor& max_logits, // [num_seqs, num_heads, max_num_partitions]
torch::Tensor& torch::Tensor& tmp_out, // [num_seqs, num_heads, max_num_partitions, head_size]
tmp_out, // [num_seqs, num_heads, max_num_partitions, head_size]
torch::Tensor& query, // [num_seqs, num_heads, head_size] torch::Tensor& query, // [num_seqs, num_heads, head_size]
torch::Tensor& torch::Tensor& key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
key_cache, // [num_blocks, num_heads, head_size/x, block_size, x] torch::Tensor& value_cache, // [num_blocks, num_heads, head_size, block_size]
torch::Tensor& int64_t num_kv_heads,
value_cache, // [num_blocks, num_heads, head_size, block_size] double scale,
int64_t num_kv_heads, double scale,
torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq] torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
torch::Tensor& context_lens, // [num_seqs] torch::Tensor& context_lens, // [num_seqs]
int64_t block_size, int64_t max_context_len, int64_t block_size,
int64_t max_context_len,
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,
const c10::optional<torch::Tensor>& fp8_out_scale, int64_t partition_size) { double k_scale,
double v_scale,
const c10::optional<torch::Tensor>& fp8_out_scale,
int64_t partition_size)
{
native::paged_attention_traits traits; native::paged_attention_traits traits;
traits.q_type = ( traits.q_type = (query.dtype() == at::ScalarType::Half ? native::ScalarType::Half
query.dtype() == at::ScalarType::Half ? native::ScalarType::Half : native::ScalarType::BFloat16);
: native::ScalarType::BFloat16
);
traits.kv_cache_dtype = kv_cache_dtype; traits.kv_cache_dtype = kv_cache_dtype;
native::paged_attention_args args; native::paged_attention_args args;
...@@ -62,9 +63,7 @@ void paged_attention( ...@@ -62,9 +63,7 @@ void paged_attention(
// NOTE: alibi_slopes is optional. // NOTE: alibi_slopes is optional.
args.alibi_slopes_ptr = args.alibi_slopes_ptr =
alibi_slopes alibi_slopes ? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr()) : nullptr;
? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
: nullptr;
args.exp_sums_ptr = reinterpret_cast<float*>(exp_sums.data_ptr()); args.exp_sums_ptr = reinterpret_cast<float*>(exp_sums.data_ptr());
args.max_logits_ptr = reinterpret_cast<float*>(max_logits.data_ptr()); args.max_logits_ptr = reinterpret_cast<float*>(max_logits.data_ptr());
...@@ -77,9 +76,7 @@ void paged_attention( ...@@ -77,9 +76,7 @@ void paged_attention(
// NOTE: fp8_out_scale is optional. // NOTE: fp8_out_scale is optional.
args.fp8_out_scale_ptr = args.fp8_out_scale_ptr =
fp8_out_scale fp8_out_scale ? reinterpret_cast<const float*>(fp8_out_scale.value().data_ptr()) : nullptr;
? reinterpret_cast<const float*>(fp8_out_scale.value().data_ptr())
: nullptr;
args.out_ptr = out.data_ptr(); args.out_ptr = out.data_ptr();
args.block_size = block_size; args.block_size = block_size;
......
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