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[CPU] Add FP8 Bmm support (#9744)

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Co-authored-by: Fan Yin <1106310035@qq.com>
This commit is contained in:
blzheng
2026-03-19 13:19:48 +08:00
committed by GitHub
parent c2b01bd2fc
commit cd22aa27a9
14 changed files with 584 additions and 83 deletions
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102
sgl-kernel/csrc/cpu/bmm.cpp
View File

@@ -4,11 +4,11 @@
namespace {
template <typename scalar_t>
template <typename scalar_t, typename packed_t>
void bmm_kernel_impl(
scalar_t* __restrict__ out,
const scalar_t* __restrict__ mat1,
const scalar_t* __restrict__ mat2,
const packed_t* __restrict__ mat2,
int64_t B,
int64_t M,
int64_t N,
@@ -67,6 +67,67 @@ void bmm_kernel_impl(
});
}
template <>
void bmm_kernel_impl(
at::BFloat16* __restrict__ out,
const at::BFloat16* __restrict__ mat1,
const at::Float8_e4m3fn* __restrict__ mat2,
int64_t B,
int64_t M,
int64_t N,
int64_t K,
int64_t mat1_strideB,
int64_t mat1_strideM,
int64_t out_strideB,
int64_t out_strideM,
float scale) {
constexpr int64_t BLOCK_M = block_size_m();
constexpr int64_t BLOCK_N = block_size_n();
const int64_t MB = div_up(M, BLOCK_M);
const int64_t NB = div_up(N, BLOCK_N);
// mat2 contiguous in [B, N, K]
int64_t mat2_strideB = N * K;
int64_t mat2_strideN = K;
const bool use_brgemm = can_use_brgemm<at::BFloat16>(M);
// parallel on [B, MB, NB]
parallel_2d(B * MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
// for brgemm, use float32 for accumulate
alignas(64) float Ctmp[BLOCK_M * BLOCK_N];
// for brgemm when mat2 is float8_e4m3
alignas(64) at::BFloat16 Btmp[BLOCK_N * BLOCK_K];
loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
int64_t bs = mb / MB;
int64_t mb_start = (mb % MB) * BLOCK_M;
int64_t mb_size = std::min(M - mb_start, BLOCK_M);
int64_t nb_start = nb * BLOCK_N;
int64_t nb_size = std::min(N - nb_start, BLOCK_N);
tinygemm_kernel(
/* A */ mat1 + bs * mat1_strideB + mb_start * mat1_strideM,
/* B */ mat2 + bs * mat2_strideB + nb_start * mat2_strideN /* nb * BLOCK_N * K */,
/* C */ out + bs * out_strideB + mb_start * out_strideM + nb_start,
/* Btmp*/ Btmp,
/* Ctmp*/ Ctmp,
/*scale*/ scale,
/* M */ mb_size,
/* N */ nb_size,
/* K */ K,
/* lda */ mat1_strideM,
/* ldb */ nb_size,
/* ldc */ out_strideM,
/* brg */ use_brgemm);
});
if (use_brgemm) {
at::native::cpublas::brgemm_release();
}
});
}
} // anonymous namespace
// mat1 : [B, M, K]
@@ -94,7 +155,7 @@ void bmm_cpu(
int64_t N = mat2.size(1);
int64_t K = mat1.size(2);
TORCH_CHECK(!scale.has_value(), "bmm: do not support fp8 weight for now.")
const bool use_fp8_w8a16 = scale.has_value();
TORCH_CHECK(N % 32 == 0, "tinygemm requires N to be 32x.");
int64_t mat1_strideB = mat1.stride(0);
@@ -105,12 +166,32 @@ void bmm_cpu(
// check shapes
TORCH_CHECK(mat2.size(0) == B && mat2.size(2) == K, "bmm: mat2 shape mismatch!");
TORCH_CHECK(out.size(0) == B && out.size(1) == M, "bmm: out shape mismatch!");
if (!use_fp8_w8a16) {
AT_DISPATCH_REDUCED_FLOATING_TYPES(mat1.scalar_type(), "bmm_kernel_impl", [&] {
bmm_kernel_impl<scalar_t, scalar_t>(
out.data_ptr<scalar_t>(),
mat1.data_ptr<scalar_t>(),
packed_w.data_ptr<scalar_t>(),
B,
M,
N,
K,
mat1_strideB,
mat1_strideM,
out_strideB,
out_strideM);
});
} else { // fp8 bmm
float scale_val = 0.f;
AT_DISPATCH_REDUCED_FLOATING_TYPES(mat1.scalar_type(), "bmm_kernel_impl", [&] {
bmm_kernel_impl<scalar_t>(
out.data_ptr<scalar_t>(),
mat1.data_ptr<scalar_t>(),
packed_w.data_ptr<scalar_t>(),
auto scale_tensor = scale.value();
TORCH_CHECK(scale_tensor.ndimension() == 0, "bmm: expect scale to be 0-dim tensor.");
scale_val = scale_tensor.item<float>();
bmm_kernel_impl<at::BFloat16, at::Float8_e4m3fn>(
out.data_ptr<at::BFloat16>(),
mat1.data_ptr<at::BFloat16>(),
packed_w.data_ptr<at::Float8_e4m3fn>(),
B,
M,
N,
@@ -118,6 +199,7 @@ void bmm_cpu(
mat1_strideB,
mat1_strideM,
out_strideB,
out_strideM);
});
out_strideM,
scale_val);
}
}

18
sgl-kernel/csrc/cpu/gemm.h
View File

@@ -232,6 +232,7 @@ void tinygemm_kernel(
int64_t ldc,
bool brg);
// block quantization
template <typename scalar_t>
void tinygemm_kernel(
const scalar_t* __restrict__ A,
@@ -250,6 +251,23 @@ void tinygemm_kernel(
int64_t block_size_K,
bool do_unpack = true);
// per tensor quantization
template <typename scalar_t>
void tinygemm_kernel(
const scalar_t* __restrict__ A,
const at::Float8_e4m3fn* __restrict__ B,
scalar_t* __restrict__ C,
scalar_t* __restrict__ Btmp,
float* __restrict__ Ctmp,
float scale,
int64_t M,
int64_t N,
int64_t K,
int64_t lda,
int64_t ldb,
int64_t ldc,
bool brg);
template <typename scalar_t>
void tinygemm_kernel(
scalar_t* C,

311
sgl-kernel/csrc/cpu/gemm_fp8.cpp
View File

@@ -42,6 +42,25 @@ inline void copy_add_stub(
out[d] = static_cast<scalar_t>(input[d] + bias[d]);
}
}
template <typename scalar_t>
inline void copy_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int size, float scale) {
using bVec = at::vec::Vectorized<scalar_t>;
using fVec = at::vec::Vectorized<float>;
constexpr int kVecSize = bVec::size();
const fVec vscale = fVec(scale);
int d;
#pragma GCC unroll 4
for (d = 0; d <= size - kVecSize; d += kVecSize) {
fVec data0 = fVec::loadu(input + d) * vscale;
fVec data1 = fVec::loadu(input + d + fVec::size()) * vscale;
bVec out_vec = convert_from_float_ext<scalar_t>(data0, data1);
out_vec.store(out + d);
}
for (; d < size; ++d) {
out[d] = static_cast<scalar_t>(input[d] * scale);
}
}
inline void unpack_B(
at::BFloat16* __restrict__ Btmp,
@@ -100,6 +119,41 @@ inline void unpack_B(
#endif
}
inline void unpack_B(
at::BFloat16* __restrict__ Btmp,
const at::Float8_e4m3fn* __restrict__ packed_B,
int N,
int K,
int ldb,
int ldb_tmp) {
#if defined(CPU_CAPABILITY_AVX512)
// [K/2, N, 2]
const int K2 = K >> 1;
const int ldb2 = ldb; // ldb * 2 >> 1;
const uint16_t* b_ptr = reinterpret_cast<const uint16_t*>(packed_B);
// prefetch distance
constexpr int PREFETCH_SIZE_K = 64;
#pragma GCC unroll 4
for (int k = 0; k < K2; ++k) {
__m512i b8 = _mm512_loadu_si512(b_ptr + k * ldb2);
if constexpr (PREFETCH_SIZE_K > 0) {
_mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2, _MM_HINT_T0);
}
__m256i b8_0 = _mm512_extracti32x8_epi32(b8, 0);
__m256i b8_1 = _mm512_extracti32x8_epi32(b8, 1);
__m512bh bf16_0 = CVT_FP8_TO_BF16(b8_0);
__m512bh bf16_1 = CVT_FP8_TO_BF16(b8_1);
_mm512_storeu_si512(Btmp + k * ldb_tmp * 2 + 0, (__m512i)bf16_0);
_mm512_storeu_si512(Btmp + k * ldb_tmp * 2 + 32, (__m512i)bf16_1);
}
#else
TORCH_CHECK(false, "unpack_B: scalar path not implemented!");
#endif
}
template <typename scalar_t, typename packed_t, bool has_bias, int BLOCK_M, int BLOCK_N>
struct tinygemm_kernel_nn {
static inline void apply(
@@ -117,6 +171,20 @@ struct tinygemm_kernel_nn {
}
};
template <typename scalar_t, int BLOCK_M, int BLOCK_N>
struct tinygemm_kernel_nn2 {
static inline void apply(
const scalar_t* __restrict__ A,
const at::Float8_e4m3fn* __restrict__ B,
scalar_t* __restrict__ C,
float scale,
int K,
int lda,
int ldb,
int ldc) {
TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
}
};
#if defined(CPU_CAPABILITY_AVX512)
template <bool has_bias, int BLOCK_M, int BLOCK_N>
struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BLOCK_N> {
@@ -221,6 +289,76 @@ struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BL
Unroll<ROWS * COLS>{}(storec);
}
};
template <int BLOCK_M, int BLOCK_N>
struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
static inline void apply(
const at::BFloat16* __restrict__ A,
const at::Float8_e4m3fn* __restrict__ B,
at::BFloat16* __restrict__ C,
float scale,
int K,
int lda,
int ldb,
int ldc) {
constexpr int ROWS = BLOCK_M;
constexpr int COLS = BLOCK_N / 16;
// prefetch distance
constexpr int PREFETCH_SIZE_K = 64;
__m512bh va;
__m512bh vb[COLS];
__m512 vc[ROWS * COLS];
const __m512 vscale = _mm512_set1_ps(scale);
auto loadc = [&](auto i) { vc[i] = _mm512_setzero_ps(); };
Unroll<ROWS * COLS>{}(loadc);
const int K2 = K >> 1;
const int lda2 = lda >> 1;
const int ldb2 = ldb; // ldb * 2 >> 1;
const float* a_ptr = reinterpret_cast<const float*>(A);
const uint16_t* b_ptr = reinterpret_cast<const uint16_t*>(B);
auto compute = [&](auto i, int k) {
constexpr int row = i / COLS;
constexpr int col = i % COLS;
if constexpr (col == 0) {
va = (__m512bh)(_mm512_set1_ps(a_ptr[row * lda2 + k]));
}
if constexpr (row == 0) {
if constexpr (col % 2 == 0) {
__m512i b8 = _mm512_loadu_si512(b_ptr + k * ldb2 + col * 16);
if constexpr (PREFETCH_SIZE_K > 0) {
_mm_prefetch(b_ptr + (k + PREFETCH_SIZE_K) * ldb2 + col * 16, _MM_HINT_T0);
}
vb[col + 0] = CVT_FP8_TO_BF16(_mm512_extracti32x8_epi32(b8, 0));
vb[col + 1] = CVT_FP8_TO_BF16(_mm512_extracti32x8_epi32(b8, 1));
}
}
vc[i] = _mm512_dpbf16_ps(vc[i], va, vb[col]);
};
for (int k = 0; k < K2; ++k) {
Unroll<ROWS * COLS>{}(compute, k);
}
auto storec = [&](auto i) {
constexpr int row = i / COLS;
constexpr int col = i % COLS;
// for COLS = 2, 4 use 512bit store
if constexpr (col % 2 == 0) {
__m512 vc0 = _mm512_mul_ps(vc[row * COLS + col + 0], vscale);
__m512 vc1 = _mm512_mul_ps(vc[row * COLS + col + 1], vscale);
_mm512_storeu_si512(
reinterpret_cast<__m512i*>((C + row * ldc + col * 16)), (__m512i)(_mm512_cvtne2ps_pbh(vc1, vc0)));
}
};
Unroll<ROWS * COLS>{}(storec);
}
};
#endif
#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE) \
@@ -236,6 +374,10 @@ struct tinygemm_kernel_nn<at::BFloat16, at::Float8_e4m3fn, has_bias, BLOCK_M, BL
ldc, \
block_size_K);
#define LAUNCH_TINYGEMM_KERNEL_NN2(MB_SIZE, NB_SIZE) \
tinygemm_kernel_nn2<scalar_t, MB_SIZE, NB_SIZE>::apply( \
A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, scale, K, lda, ldb, ldc);
template <typename scalar_t, typename packed_t, bool has_bias>
struct brgemm {
static inline void apply(
@@ -256,6 +398,8 @@ struct brgemm {
TORCH_CHECK(false, "struct brgemm: primary template not implemented!");
}
};
template <typename scalar_t>
struct brgemm2 {};
template <bool has_bias>
struct brgemm<at::BFloat16, at::Float8_e4m3fn, has_bias> {
@@ -301,6 +445,42 @@ struct brgemm<at::BFloat16, at::Float8_e4m3fn, has_bias> {
}
};
template <>
struct brgemm2<at::BFloat16> {
static inline void apply(
const at::BFloat16* __restrict__ A,
const at::Float8_e4m3fn* __restrict__ B,
at::BFloat16* __restrict__ C,
at::BFloat16* __restrict__ Btmp,
float* __restrict__ Ctmp,
float scale,
int M,
int N,
int K,
int lda,
int ldb,
int ldc) {
constexpr int BLOCK_N = block_size_n();
// [BLOCK_K, BLOCK_N] -> [BLOCK_K / 2, BLOCK_N * 2]
const int ldb_tmp = block_size_n();
// accumulate across K per BLOCK_K
for (int k = 0; k < K; k += BLOCK_K) {
int kb_size = std::min(BLOCK_K, K - k);
unpack_B(Btmp, B + k * ldb, N, kb_size, ldb, ldb_tmp);
const bool add_C = (k != 0);
at::native::cpublas::brgemm(M, N, kb_size, lda, ldb_tmp, BLOCK_N, add_C, A + k, Btmp, Ctmp);
}
// copy from Ctmp to C and mul scale
for (int m = 0; m < M; ++m) {
copy_mul_stub(C + m * ldc, Ctmp + m * BLOCK_N, N, scale);
}
}
};
template <typename scalar_t, bool has_bias>
void tinygemm_kernel(
const scalar_t* __restrict__ A,
@@ -356,7 +536,103 @@ void tinygemm_kernel(
}
}
}
template <typename scalar_t>
void tinygemm_kernel2(
const scalar_t* __restrict__ A,
const at::Float8_e4m3fn* __restrict__ B,
scalar_t* __restrict__ C,
scalar_t* __restrict__ Btmp,
float* __restrict__ Ctmp,
float scale,
int64_t M,
int64_t N,
int64_t K,
int64_t lda,
int64_t ldb,
int64_t ldc,
bool brg) {
if (brg) {
brgemm2<scalar_t>::apply(A, B, C, Btmp, Ctmp, scale, M, N, K, lda, ldb, ldc);
return;
}
// pattern: 1-8-8
if (M == 1) {
constexpr int64_t BLOCK_N = 128;
const int64_t NB = div_up(N, BLOCK_N);
int64_t mb_start = 0;
for (int64_t nb = 0; nb < NB; ++nb) {
int64_t nb_start = nb * BLOCK_N;
int64_t nb_size = std::min(BLOCK_N, N - nb_start);
switch (nb_size >> 4) {
case 2:
LAUNCH_TINYGEMM_KERNEL_NN2(1, 32);
break;
case 4:
LAUNCH_TINYGEMM_KERNEL_NN2(1, 64);
break;
case 6:
LAUNCH_TINYGEMM_KERNEL_NN2(1, 96);
break;
case 8:
LAUNCH_TINYGEMM_KERNEL_NN2(1, 128);
break;
default:
TORCH_CHECK(false, "Unexpected block size, 1x", "nb_size");
}
}
return;
}
// pattern: 1-4-16
constexpr int64_t BLOCK_M = 4;
constexpr int64_t BLOCK_N = 64;
const int64_t MB = div_up(M, BLOCK_M);
const int64_t NB = div_up(N, BLOCK_N);
for (int64_t mb = 0; mb < MB; ++mb) {
int64_t mb_start = mb * BLOCK_M;
int64_t mb_size = std::min(BLOCK_M, M - mb_start);
for (int64_t nb = 0; nb < NB; ++nb) {
int64_t nb_start = nb * BLOCK_N;
int64_t nb_size = std::min(BLOCK_N, N - nb_start);
switch (mb_size << 4 | nb_size >> 4) {
// mb_size = 1
case 0x12:
LAUNCH_TINYGEMM_KERNEL_NN2(1, 32);
break;
case 0x14:
LAUNCH_TINYGEMM_KERNEL_NN2(1, 64);
break;
// mb_size = 2
case 0x22:
LAUNCH_TINYGEMM_KERNEL_NN2(2, 32);
break;
case 0x24:
LAUNCH_TINYGEMM_KERNEL_NN2(2, 64);
break;
// mb_size = 3
case 0x32:
LAUNCH_TINYGEMM_KERNEL_NN2(3, 32);
break;
case 0x34:
LAUNCH_TINYGEMM_KERNEL_NN2(3, 64);
break;
// mb_size = 4
case 0x42:
LAUNCH_TINYGEMM_KERNEL_NN2(4, 32);
break;
case 0x44:
LAUNCH_TINYGEMM_KERNEL_NN2(4, 64);
break;
default:
TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
}
}
}
}
template <typename scalar_t>
void fp8_scaled_mm_kernel_impl(
scalar_t* __restrict__ out,
@@ -450,7 +726,23 @@ void tinygemm_kernel(
tinygemm_kernel<scalar_t, false>(
A, B, C, Btmp, Ctmp, scale, nullptr, M, N, K, lda, ldb, ldc, brg, block_size_K, do_unpack);
}
template <typename scalar_t>
void tinygemm_kernel(
const scalar_t* __restrict__ A,
const at::Float8_e4m3fn* __restrict__ B,
scalar_t* __restrict__ C,
scalar_t* __restrict__ Btmp,
float* __restrict__ Ctmp,
float scale,
int64_t M,
int64_t N,
int64_t K,
int64_t lda,
int64_t ldb,
int64_t ldc,
bool brg) {
tinygemm_kernel2<scalar_t>(A, B, C, Btmp, Ctmp, scale, M, N, K, lda, ldb, ldc, brg);
}
#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE) \
template void tinygemm_kernel<TYPE>( \
const TYPE* __restrict__ A, \
@@ -469,8 +761,25 @@ void tinygemm_kernel(
int64_t block_size_K, \
bool do_unpack)
#define INSTANTIATE_TINYGEMM_TEMPLATE2(TYPE) \
template void tinygemm_kernel<TYPE>( \
const TYPE* __restrict__ A, \
const at::Float8_e4m3fn* __restrict__ B, \
TYPE* __restrict__ C, \
TYPE* __restrict__ Btmp, \
float* __restrict__ Ctmp, \
float scale, \
int64_t M, \
int64_t N, \
int64_t K, \
int64_t lda, \
int64_t ldb, \
int64_t ldc, \
bool brg)
INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
INSTANTIATE_TINYGEMM_TEMPLATE2(at::BFloat16);
at::Tensor fp8_scaled_mm_cpu(
at::Tensor& mat1,

6
sgl-kernel/csrc/cpu/qkv_proj.cpp
View File

@@ -434,6 +434,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> qkv_proj_with_rope(
std::optional<at::Tensor> q_a_proj_scale,
std::optional<at::Tensor> q_b_proj_scale,
std::optional<at::Tensor> kv_a_proj_scale,
std::optional<at::Tensor> w_scale,
bool is_vnni,
std::optional<std::vector<int64_t>> block_size) {
RECORD_FUNCTION(
@@ -601,10 +602,9 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> qkv_proj_with_rope(
qb.as_strided_({num_seqs, num_heads, qk_head_dim}, {num_heads * qk_head_dim, qk_head_dim, 1});
// stage 4: bmm
std::optional<at::Tensor> scale;
auto q_nope = qb.narrow(2, 0, qk_nope_head_dim).transpose_(0, 1);
auto q_nope_out = q_input.narrow(2, 0, kv_lora_rank).transpose_(0, 1);
bmm_cpu(q_nope_out, q_nope, w_kc, is_vnni, scale);
bmm_cpu(q_nope_out, q_nope, w_kc, is_vnni, w_scale);
// stage 5: rope
AT_DISPATCH_REDUCED_FLOATING_TYPES(st, "rotary_emb_kernel_impl", [&] {
@@ -643,6 +643,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> qkv_proj_with_rope_fused_weight(
bool use_fp8_w8a16,
std::optional<at::Tensor> qkv_a_proj_scale,
std::optional<at::Tensor> q_b_proj_scale,
std::optional<at::Tensor> w_scale,
bool is_vnni,
std::optional<std::vector<int64_t>> block_size,
int64_t q_lora_rank,
@@ -696,6 +697,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> qkv_proj_with_rope_fused_weight(
q_a_proj_s,
q_b_proj_scale,
kv_a_proj_s,
w_scale,
is_vnni,
block_size);
}

8
sgl-kernel/csrc/cpu/torch_extension_cpu.cpp
View File

@@ -245,6 +245,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> qkv_proj_with_rope(
std::optional<at::Tensor> q_a_proj_scale,
std::optional<at::Tensor> q_b_proj_scale,
std::optional<at::Tensor> kv_a_proj_scale,
std::optional<at::Tensor> w_scale,
bool is_vnni,
std::optional<std::vector<int64_t>> block_size);
@@ -262,6 +263,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> qkv_proj_with_rope_fused_weight(
bool use_fp8_w8a16,
std::optional<at::Tensor> qkv_a_proj_scale,
std::optional<at::Tensor> q_b_proj_scale,
std::optional<at::Tensor> w_scale,
bool is_vnni,
std::optional<std::vector<int64_t>> block_size,
int64_t q_lora_rank,
@@ -515,14 +517,14 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
"qkv_proj_with_rope(Tensor hidden_states, Tensor q_a_proj_weight, Tensor q_b_proj_weight, Tensor "
"kv_a_proj_weight, Tensor w_kc, Tensor q_a_layernorm_weight, Tensor kv_a_layernorm_weight, Tensor positions, "
"Tensor cos_sin_cache, float eps, bool use_int8_w8a8, bool use_fp8_w8a16, Tensor? q_a_proj_scale, Tensor? "
"q_b_proj_scale, Tensor? "
"kv_a_proj_scale, bool is_vnni, int[]? block_size) -> (Tensor, Tensor, Tensor)");
"q_b_proj_scale, Tensor? kv_a_proj_scale, Tensor? w_scale, "
"bool is_vnni, int[]? block_size) -> (Tensor, Tensor, Tensor)");
m.impl("qkv_proj_with_rope", torch::kCPU, &qkv_proj_with_rope);
m.def(
"qkv_proj_with_rope_fused_weight(Tensor hidden_states, Tensor qkv_a_proj_weight, Tensor q_b_proj_weight, "
"Tensor w_kc, Tensor q_a_layernorm_weight, Tensor kv_a_layernorm_weight, Tensor positions, "
"Tensor cos_sin_cache, float eps, bool use_int8_w8a8, bool use_fp8_w8a16, Tensor? qkv_a_proj_scale, Tensor? "
"q_b_proj_scale,"
"q_b_proj_scale, Tensor? w_scale,"
"bool is_vnni, int[]? block_size, int q_lora_rank, int kv_lora_rank,"
"int qk_rope_head_dim) -> (Tensor, Tensor, Tensor)");
m.impl("qkv_proj_with_rope_fused_weight", torch::kCPU, &qkv_proj_with_rope_fused_weight);