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Unverified Commit 3ded6235 authored by Chunyuan WU's avatar Chunyuan WU Committed by GitHub
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Add fp8 fused_experts kernel for CPU in sgl-kernel and add UT (#6404)

parent 4ba1eea8
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Showing with 752 additions and 157 deletions
sgl-kernel/csrc/cpu/gemm.h
View file @ 3ded6235
......@@ -85,6 +85,32 @@ void fused_experts_int8_kernel_impl(
int64_t topk,
int64_t num_tokens_post_pad);
// moe implementations for fp8 w8a16
template <typename scalar_t>
void fused_experts_fp8_kernel_impl(
scalar_t* __restrict__ output,
scalar_t* __restrict__ ic0,
scalar_t* __restrict__ ic1,
scalar_t* __restrict__ ic2,
scalar_t* __restrict__ A_tmp,
const scalar_t* __restrict__ input,
const at::Float8_e4m3fn* __restrict__ packed_w1,
const at::Float8_e4m3fn* __restrict__ packed_w2,
const float* __restrict__ w1s,
const float* __restrict__ w2s,
int64_t block_size_N,
int64_t block_size_K,
const float* __restrict__ topk_weights,
const int32_t* __restrict__ sorted_ids,
const int32_t* __restrict__ expert_ids,
const int32_t* __restrict__ offsets,
int64_t M,
int64_t N,
int64_t K,
int64_t E,
int64_t topk,
int64_t num_tokens_post_pad);
// shared expert implementation for int8 w8a8
template <typename scalar_t>
void shared_expert_int8_kernel_impl(
......
sgl-kernel/csrc/cpu/moe.cpp
View file @ 3ded6235
......@@ -932,6 +932,40 @@ void shared_expert_kernel_impl(
} // anonymous namespace
// common checks
static inline void check_moe_scales(
bool use_int8_w8a8,
bool use_fp8_w8a16,
const std::optional<at::Tensor>& w1_scale,
const std::optional<at::Tensor>& w2_scale,
const std::optional<std::vector<int64_t>> block_size,
const std::optional<at::Tensor>& a1_scale,
const std::optional<at::Tensor>& a2_scale) {
if (use_int8_w8a8) {
TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for int8 w8a8.");
TORCH_CHECK(w2_scale.has_value(), "missing w2_scale for int8 w8a8.");
TORCH_CHECK(!a1_scale.has_value(), "static quantization for activation not supported.");
TORCH_CHECK(!a2_scale.has_value(), "static quantization for activation not supported.");
}
if (use_fp8_w8a16) {
TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for fp8 w8a16.");
TORCH_CHECK(w2_scale.has_value(), "missing w2_scale for fp8 w8a16.");
TORCH_CHECK(block_size.has_value(), "missing block_size for fp8 w8a16.");
TORCH_CHECK(block_size.value().size() == 2, "expect block_size.size() to be 2.");
}
}
#define CHECK_MOE_SCALES_FP8(DIM0, DIM1) \
auto w1s = w1_scale.value(); \
auto w2s = w2_scale.value(); \
auto block_size_val = block_size.value(); \
int64_t block_size_N = block_size_val[0]; \
int64_t block_size_K = block_size_val[1]; \
TORCH_CHECK(w1s.size(DIM0) == 2 * N / block_size_N); \
TORCH_CHECK(w1s.size(DIM1) == K / block_size_K); \
TORCH_CHECK(w2s.size(DIM0) == K / block_size_N); \
TORCH_CHECK(w2s.size(DIM1) == N / block_size_K)
// hidden_states: [M, K]
// w1: [E, 2N, K]
// w2: [E, K, N]
......@@ -946,8 +980,10 @@ at::Tensor fused_experts_cpu(
at::Tensor& topk_ids,
bool inplace,
bool use_int8_w8a8,
bool use_fp8_w8a16,
const std::optional<at::Tensor>& w1_scale,
const std::optional<at::Tensor>& w2_scale,
const std::optional<std::vector<int64_t>> block_size,
const std::optional<at::Tensor>& a1_scale,
const std::optional<at::Tensor>& a2_scale,
bool is_vnni) {
......@@ -990,12 +1026,8 @@ at::Tensor fused_experts_cpu(
CHECK_EQ(packed_w1.size(2), packed_K);
CHECK_EQ(packed_w2.size(2), packed_N);
if (use_int8_w8a8) {
TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for int8 w8a8.");
TORCH_CHECK(w2_scale.has_value(), "missing w2_scale for int8 w8a8.");
TORCH_CHECK(!a1_scale.has_value(), "static quantization for activation not supported.");
TORCH_CHECK(!a2_scale.has_value(), "static quantization for activation not supported.");
}
// check scales
check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size, a1_scale, a2_scale);
at::Tensor out_hidden_states = inplace ? hidden_states : at::empty_like(hidden_states);
......@@ -1047,6 +1079,9 @@ at::Tensor fused_experts_cpu(
// 5. Aq_tmp : [M, K] or [M * topk, N]
// 6. As_tmp : [M * topk]
//
// for fp8 w8a16:
// 7. intermediate_cache1 : [M * topk, 2N]
//
int64_t buffer_size_nbytes = M * topk * N * 2 + M * topk * K * 2 +
num_threads * BLOCK_M * K * (use_int8_w8a8 ? 1 : 2) +
num_threads * 2 * BLOCK_M * BLOCK_N * sizeof(float);
......@@ -1054,6 +1089,9 @@ at::Tensor fused_experts_cpu(
if (use_int8_w8a8) {
buffer_size_nbytes += std::max(M * K, M * topk * N) + M * topk * sizeof(float);
}
if (use_fp8_w8a16) {
buffer_size_nbytes += M * topk * 2 * N * 2;
}
auto buffer2 = at::empty({buffer_size_nbytes}, hidden_states.options().dtype(at::kChar));
......@@ -1095,6 +1133,35 @@ at::Tensor fused_experts_cpu(
E,
topk,
num_tokens_post_pad);
} else if (use_fp8_w8a16) {
// here we just ignore C_tmp as it is not used
scalar_t* __restrict__ A_tmp = (scalar_t*)((void*)(intermediate_cache2 + M * topk * K));
scalar_t* __restrict__ intermediate_cache0 = (scalar_t*)((void*)(A_tmp + num_threads * BLOCK_M * K));
CHECK_MOE_SCALES_FP8(1, 2);
fused_experts_fp8_kernel_impl(
out_hidden_states.data_ptr<scalar_t>(),
intermediate_cache0,
intermediate_cache1,
intermediate_cache2,
A_tmp,
hidden_states.data_ptr<scalar_t>(),
packed_w1.data_ptr<at::Float8_e4m3fn>(),
packed_w2.data_ptr<at::Float8_e4m3fn>(),
w1s.data_ptr<float>(),
w2s.data_ptr<float>(),
block_size_N,
block_size_K,
topk_weights.data_ptr<float>(),
sorted_ids,
expert_ids,
offsets,
M,
N,
K,
E,
topk,
num_tokens_post_pad);
} else {
scalar_t* __restrict__ A_tmp = intermediate_cache2 + M * topk * K;
float* __restrict__ C_tmp = (float*)((void*)(A_tmp + num_threads * BLOCK_M * K));
......@@ -1176,17 +1243,8 @@ at::Tensor shared_expert_cpu(
CHECK_EQ(packed_w1.size(1), packed_K);
CHECK_EQ(packed_w2.size(1), packed_N);
if (use_int8_w8a8) {
TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for int8 w8a8.");
TORCH_CHECK(w2_scale.has_value(), "missing w2_scale for int8 w8a8.");
TORCH_CHECK(!a1_scale.has_value(), "static quantization for activation not supported.");
TORCH_CHECK(!a2_scale.has_value(), "static quantization for activation not supported.");
}
if (use_fp8_w8a16) {
TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for fp8 w8a16.");
TORCH_CHECK(w2_scale.has_value(), "missing w2_scale for fp8 w8a16.");
TORCH_CHECK(block_size.has_value(), "missing block_size for fp8 w8a16.");
}
// check scales
check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size, a1_scale, a2_scale);
at::Tensor out_hidden_states = inplace ? hidden_states : at::empty_like(hidden_states);
......@@ -1244,17 +1302,7 @@ at::Tensor shared_expert_cpu(
} else if (use_fp8_w8a16) {
scalar_t* __restrict__ intermediate_cache0 = (scalar_t*)((void*)(C_tmp + num_threads * 2 * BLOCK_M * BLOCK_N));
auto w1s = w1_scale.value();
auto w2s = w2_scale.value();
auto block_size_val = block_size.value();
TORCH_CHECK(block_size_val.size() == 2, "shared_expert: expect block_size.size() to be 2.");
int64_t block_size_N = block_size_val[0];
int64_t block_size_K = block_size_val[1];
TORCH_CHECK(w1s.size(0) == 2 * N / block_size_N);
TORCH_CHECK(w1s.size(1) == K / block_size_K);
TORCH_CHECK(w2s.size(0) == K / block_size_N);
TORCH_CHECK(w2s.size(1) == N / block_size_K);
CHECK_MOE_SCALES_FP8(0, 1);
shared_expert_fp8_kernel_impl<scalar_t>(
out_hidden_states.data_ptr<scalar_t>(),
intermediate_cache0,
......
sgl-kernel/csrc/cpu/moe_fp8.cpp
View file @ 3ded6235
......@@ -4,6 +4,76 @@
namespace {
template <typename scalar_t>
inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
using Vec = at::vec::Vectorized<scalar_t>;
// no remainder
#pragma GCC unroll 4
for (int64_t d = 0; d < size; d += Vec::size()) {
Vec data = Vec::loadu(input + d);
data.store(out + d);
}
}
template <typename scalar_t>
inline void copy_mul_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, float weight, int64_t size) {
using bVec = at::vec::Vectorized<scalar_t>;
using fVec = at::vec::Vectorized<float>;
constexpr int kVecSize = bVec::size();
const fVec weight_vec = fVec(weight);
int64_t d;
#pragma GCC unroll 4
for (d = 0; d <= size - kVecSize; d += kVecSize) {
bVec x = bVec::loadu(input + d);
fVec x0, x1;
std::tie(x0, x1) = at::vec::convert_to_float(x);
x0 = x0 * weight_vec;
x1 = x1 * weight_vec;
bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
out_vec.store(out + d);
}
for (; d < size; ++d) {
out[d] = static_cast<scalar_t>(input[d] * weight);
}
}
// acc from [topk, K] to [K]
template <typename scalar_t>
inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
using bVec = at::vec::Vectorized<scalar_t>;
using fVec = at::vec::Vectorized<float>;
constexpr int kVecSize = bVec::size();
if (topk == 1) {
// do copy for topk = 1
copy_stub(out, input, K);
} else {
// do sum for topk != 1
int64_t d;
#pragma GCC unroll 4
for (d = 0; d <= K - kVecSize; d += kVecSize) {
fVec sum_fvec0 = fVec(0.f);
fVec sum_fvec1 = fVec(0.f);
for (int t = 0; t < topk; ++t) {
bVec x_bvec = bVec::loadu(input + t * K + d);
fVec x_fvec0, x_fvec1;
std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
sum_fvec0 += x_fvec0;
sum_fvec1 += x_fvec1;
}
bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
out_bvec.store(out + d);
}
for (; d < K; ++d) {
float sum_val = 0.f;
for (int t = 0; t < topk; ++t) {
sum_val += static_cast<float>(input[t * K + d]);
}
out[d] = static_cast<scalar_t>(sum_val);
}
}
}
// out = input + input2 * scale
template <typename scalar_t>
inline void add_mul_stub(
......@@ -65,6 +135,215 @@ inline void silu_and_mul_stub(
} // anonymous namespace
template <typename scalar_t>
void fused_experts_fp8_kernel_impl(
scalar_t* __restrict__ output,
scalar_t* __restrict__ ic0,
scalar_t* __restrict__ ic1,
scalar_t* __restrict__ ic2,
scalar_t* __restrict__ A_tmp,
const scalar_t* __restrict__ input,
const at::Float8_e4m3fn* __restrict__ packed_w1,
const at::Float8_e4m3fn* __restrict__ packed_w2,
const float* __restrict__ w1s,
const float* __restrict__ w2s,
int64_t block_size_N,
int64_t block_size_K,
const float* __restrict__ topk_weights,
const int32_t* __restrict__ sorted_ids,
const int32_t* __restrict__ expert_ids,
const int32_t* __restrict__ offsets,
int64_t M,
int64_t N,
int64_t K,
int64_t E,
int64_t topk,
int64_t num_tokens_post_pad) {
constexpr int64_t BLOCK_M = block_size_m();
constexpr int64_t BLOCK_N = block_size_n();
// stage 1: intermediate_cache0 = hidden_states @ w1
const int64_t MB = div_up(num_tokens_post_pad, BLOCK_M);
const int64_t NB = div_up(2 * N, BLOCK_N);
int64_t scale_size_N = div_up(2 * N, block_size_N);
int64_t scale_size_K = div_up(K, block_size_K);
int64_t blocks_n_per_group = block_size_N / BLOCK_N;
const int64_t stride_e = 2 * N * K;
const int64_t stride_n = K;
// here we only parallel on half of 2N to fuse silu_and_mul with gemm
at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
// get local pointers
int tid = at::get_thread_num();
scalar_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
alignas(64) scalar_t Btmp[BLOCK_N * BLOCK_K];
alignas(64) float Ctmp[BLOCK_M * BLOCK_N];
bool is_brgemm_used = false;
for (int64_t i = begin; i < end; ++i) {
int64_t mb = i / NB;
int64_t nb = i % NB;
int64_t n_size = std::min(2 * N - nb * BLOCK_N, BLOCK_N);
// B shape [K, n_size] in vnni format
int32_t expert_id = expert_ids[mb];
const at::Float8_e4m3fn* __restrict__ B = packed_w1 + expert_id * stride_e + nb * BLOCK_N * stride_n;
const float* __restrict__ Bs =
w1s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
// 1.a load A
const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
int64_t m_size = offsets[mb + 1] - offsets[mb];
const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(m_size);
is_brgemm_used = is_brgemm_used || use_brgemm;
for (int64_t m = 0; m < m_size; ++m) {
int32_t index = A_ids[m] / topk;
copy_stub(A + m * K, input + index * K, K);
}
const int64_t offset = offsets[mb];
tinygemm_kernel<scalar_t>(
/* A */ A,
/* B */ B,
/* C */ ic0 + offset * 2 * N + nb * BLOCK_N,
/* Btmp */ Btmp,
/* Ctmp */ Ctmp,
/* scale */ Bs,
/* M */ m_size,
/* N */ n_size,
/* K */ K,
/* lda */ K,
/* ldb */ n_size,
/* ldc */ 2 * N,
/* brg */ use_brgemm,
/* block_size_K */ block_size_K);
}
if (is_brgemm_used) {
at::native::cpublas::brgemm_release();
}
});
// stage 1.5: intermediate_cache1 = silu(intermediate_cache0)
at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
for (int64_t m = begin; m < end; ++m) {
silu_and_mul_stub(ic1 + m * N, ic0 + m * 2 * N, ic0 + m * 2 * N + N, N);
}
});
// stage 2: intermediate_cache2 = intermediate_cache1 @ w2
// w2 : [E, K, N] as [E, OC, IC]
const int64_t OC = K; // rename K as OC
const int64_t IC = N; // rename N as IC
const int64_t MB2 = MB;
const int64_t NB2 = div_up(OC, BLOCK_N);
scale_size_N = div_up(K, block_size_N);
scale_size_K = div_up(N, block_size_K);
const int64_t stride_e2 = OC * IC;
const int64_t stride_oc = IC;
// parallel on [MB2, NB2]
at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
alignas(64) scalar_t Btmp[BLOCK_K * BLOCK_N];
alignas(64) scalar_t C[BLOCK_M * BLOCK_K];
alignas(64) float Ctmp[BLOCK_M * BLOCK_K];
bool is_brgemm_used = false;
for (int64_t i = begin; i < end; ++i) {
int64_t mb = i / NB2;
int64_t nb = i % NB2;
int64_t m_size = offsets[mb + 1] - offsets[mb];
int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(m_size);
is_brgemm_used = is_brgemm_used || use_brgemm;
// A ptr from ic1 of [M * topk, N] in sorted order
// so as to avoid copy A to tmp buffer again
const scalar_t* __restrict__ A = ic1 + offsets[mb] * N;
const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
// B shape [IC, n_size] in vnni format
int32_t expert_id = expert_ids[mb];
const at::Float8_e4m3fn* __restrict__ B = packed_w2 + expert_id * stride_e2 + nb * BLOCK_N * stride_oc;
const float* __restrict__ Bs =
w2s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
tinygemm_kernel<scalar_t>(
/* A */ A,
/* B */ B,
/* C */ C,
/* Btmp */ Btmp,
/* Ctmp */ Ctmp,
/* scale */ Bs,
/* M */ m_size,
/* N */ n_size,
/* K */ IC,
/* lda */ IC,
/* ldb */ n_size,
/* ldc */ BLOCK_N,
/* brg */ use_brgemm,
/* block_size_K */ block_size_K);
// 2.b copy from C to ic2 in original order
// and also mul topk_weights in float32
for (int64_t m = 0; m < m_size; ++m) {
int32_t index = A_ids[m];
float weight = topk_weights[index];
copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C + m * BLOCK_N, weight, n_size);
}
}
if (is_brgemm_used) {
at::native::cpublas::brgemm_release();
}
});
// stage 3: out = intermediate_cache2.sum(dim=1)
// from [M, topk, K] to [M, K]
at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
for (int64_t m = begin; m < end; ++m) {
sum_stub(output + m * K, ic2 + m * topk * K, topk, K);
}
});
}
#define INSTANTIATE_MOE_FP8_TEMPLATE(TYPE) \
template void fused_experts_fp8_kernel_impl<TYPE>( \
TYPE* __restrict__ output, \
TYPE* __restrict__ ic0, \
TYPE* __restrict__ ic1, \
TYPE* __restrict__ ic2, \
TYPE* __restrict__ A_tmp, \
const TYPE* __restrict__ input, \
const at::Float8_e4m3fn* __restrict__ packed_w1, \
const at::Float8_e4m3fn* __restrict__ packed_w2, \
const float* __restrict__ w1s, \
const float* __restrict__ w2s, \
int64_t block_size_N, \
int64_t block_size_K, \
const float* __restrict__ topk_weights, \
const int32_t* __restrict__ sorted_ids, \
const int32_t* __restrict__ expert_ids, \
const int32_t* __restrict__ offsets, \
int64_t M, \
int64_t N, \
int64_t K, \
int64_t E, \
int64_t topk, \
int64_t num_tokens_post_pad)
INSTANTIATE_MOE_FP8_TEMPLATE(at::BFloat16);
INSTANTIATE_MOE_FP8_TEMPLATE(at::Half);
template <typename scalar_t>
void shared_expert_fp8_kernel_impl(
scalar_t* __restrict__ output,
......@@ -100,8 +379,8 @@ void shared_expert_fp8_kernel_impl(
for (int64_t i = begin; i < end; ++i) {
int64_t mb = i / NB;
int64_t nb = i % NB;
int64_t mb_size = std::min(M - mb * BLOCK_M, BLOCK_M);
int64_t nb_size = std::min(2 * N - nb * BLOCK_N, BLOCK_N);
int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
int64_t n_size = std::min(2 * N - nb * BLOCK_N, BLOCK_N);
tinygemm_kernel<scalar_t>(
/* A */ input + mb * BLOCK_M * K,
......@@ -110,11 +389,11 @@ void shared_expert_fp8_kernel_impl(
/* Btmp */ Btmp,
/* Ctmp */ Ctmp,
/* scale */ w1s + (nb / blocks_n_per_group) * scale_size_K,
/* M */ mb_size,
/* N */ nb_size,
/* M */ m_size,
/* N */ n_size,
/* K */ K,
/* lda */ K,
/* ldb */ nb_size,
/* ldb */ n_size,
/* ldc */ 2 * N,
/* brg */ use_brgemm,
/* block_size_K */ block_size_K);
......@@ -149,8 +428,8 @@ void shared_expert_fp8_kernel_impl(
for (int64_t i = begin; i < end; ++i) {
int64_t mb = i / NB2;
int64_t nb = i % NB2;
int64_t mb_size = std::min(M - mb * BLOCK_M, BLOCK_M);
int64_t nb_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
// 2.a gemm: C = A @ B
tinygemm_kernel<scalar_t>(
......@@ -160,11 +439,11 @@ void shared_expert_fp8_kernel_impl(
/* Btmp */ Btmp,
/* Ctmp */ Ctmp,
/* scale */ w2s + (nb / blocks_n_per_group) * scale_size_K,
/* M */ mb_size,
/* N */ nb_size,
/* M */ m_size,
/* N */ n_size,
/* K */ IC,
/* lda */ IC,
/* ldb */ nb_size,
/* ldb */ n_size,
/* ldc */ BLOCK_N,
/* brg */ use_brgemm,
/* block_size_K */ block_size_K);
......@@ -172,8 +451,8 @@ void shared_expert_fp8_kernel_impl(
// 2.b copy from C to output and add fused_experts_out
scalar_t* __restrict__ out = output + mb * BLOCK_M * K + nb * BLOCK_N;
const scalar_t* __restrict__ fused_out = fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N;
for (int64_t m = 0; m < mb_size; ++m) {
add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out + m * K, routed_scaling_factor, nb_size);
for (int64_t m = 0; m < m_size; ++m) {
add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out + m * K, routed_scaling_factor, n_size);
}
}
});
......
sgl-kernel/csrc/cpu/torch_extension_cpu.cpp
View file @ 3ded6235
......@@ -130,8 +130,10 @@ at::Tensor fused_experts_cpu(
at::Tensor& topk_ids,
bool inplace,
bool use_int8_w8a8,
bool use_fp8_w8a16,
const std::optional<at::Tensor>& w1_scale,
const std::optional<at::Tensor>& w2_scale,
const std::optional<std::vector<int64_t>> block_size,
const std::optional<at::Tensor>& a1_scale,
const std::optional<at::Tensor>& a2_scale,
bool is_vnni);
......@@ -260,7 +262,8 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
// moe
m.def(
"fused_experts_cpu(Tensor hidden_states, Tensor w1, Tensor w2, Tensor topk_weights, Tensor topk_ids, bool "
"inplace, bool use_int8_w8a8, Tensor? w1_scale, Tensor? w2_scale, Tensor? a1_scale, Tensor? a2_scale, bool "
"inplace, bool use_int8_w8a8, bool use_fp8_w8a16, Tensor? w1_scale, Tensor? w2_scale, int[]? block_size, Tensor? "
"a1_scale, Tensor? a2_scale, bool "
"is_vnni) -> Tensor");
m.impl("fused_experts_cpu", torch::kCPU, &fused_experts_cpu);
......
sgl-kernel/setup_cpu.py deleted 100644 → 0
View file @ 4ba1eea8
# Copyright 2025 SGLang Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import platform
import shutil
import sys
from pathlib import Path
import torch
from setuptools import find_packages, setup
from setuptools.command.build_py import build_py
from torch.utils.cpp_extension import BuildExtension, CppExtension
root = Path(__file__).parent.resolve()
arch = platform.machine().lower()
if arch in ("x86_64", "amd64"):
plat_name = "manylinux2014_x86_64"
elif arch in ("aarch64", "arm64"):
plat_name = "manylinux2014_aarch64"
elif arch.startswith("ppc"):
plat_name = "manylinux2014_ppc64le"
else:
plat_name = f"manylinux2014_{arch}"
if "bdist_wheel" in sys.argv and "--plat-name" not in sys.argv:
sys.argv.extend(["--plat-name", plat_name])
def _get_version():
with open(root / "pyproject.toml") as f:
for line in f:
if line.startswith("version"):
return line.split("=")[1].strip().strip('"')
cpu_fp8_ftz = os.getenv("SGLANG_CPU_FP8_CVT_FTZ", "1") == "1"
operator_namespace = "sgl_kernel"
include_dirs = [
"../../include",
]
sources = [
"csrc/cpu/activation.cpp",
"csrc/cpu/bmm.cpp",
"csrc/cpu/decode.cpp",
"csrc/cpu/extend.cpp",
"csrc/cpu/gemm.cpp",
"csrc/cpu/gemm_fp8.cpp",
"csrc/cpu/gemm_int8.cpp",
"csrc/cpu/moe.cpp",
"csrc/cpu/moe_fp8.cpp",
"csrc/cpu/moe_int8.cpp",
"csrc/cpu/norm.cpp",
"csrc/cpu/qkv_proj.cpp",
"csrc/cpu/topk.cpp",
"csrc/cpu/interface.cpp",
"csrc/cpu/shm.cpp",
"csrc/cpu/rope.cpp",
"csrc/cpu/torch_extension_cpu.cpp",
]
extra_compile_args = {
"cxx": [
"-O3",
"-Wno-unknown-pragmas",
"-march=native",
"-fopenmp",
]
}
if cpu_fp8_ftz:
extra_compile_args["cxx"].append("-DSGLANG_CPU_FP8_CVT_FTZ")
libraries = ["c10", "torch", "torch_python"]
cmdclass = {
"build_ext": BuildExtension.with_options(use_ninja=True),
}
Extension = CppExtension
extra_link_args = ["-Wl,-rpath,$ORIGIN/../../torch/lib", f"-L/usr/lib/{arch}-linux-gnu"]
ext_modules = [
Extension(
name="sgl_kernel.common_ops",
sources=sources,
include_dirs=include_dirs,
extra_compile_args=extra_compile_args,
libraries=libraries,
extra_link_args=extra_link_args,
py_limited_api=False,
),
]
setup(
name="sgl-kernel",
version=_get_version(),
packages=find_packages(where="python"),
package_dir={"": "python"},
ext_modules=ext_modules,
cmdclass=cmdclass,
options={"bdist_wheel": {"py_limited_api": "cp39"}},
)
test/srt/cpu/test_moe.py 0 → 100644
View file @ 3ded6235
import itertools
import math
import unittest
# TODO: use interface in cpu.py
import sgl_kernel
import torch
kernel = torch.ops.sgl_kernel
from utils import (
BLOCK_K,
BLOCK_N,
factor_for_scale,
fp8_max,
fp8_min,
native_fp8_fused_moe,
precision,
scaled_weight,
torch_naive_fused_moe,
torch_w8a8_per_column_fused_moe,
)
from sglang.test.test_utils import CustomTestCase
def fused_moe(a, w1, w2, score, topk, renormalize, prepack):
G = 1
topk_group = 1
B, D = a.shape
topk_weights = torch.empty(B, topk, dtype=torch.float32)
topk_ids = torch.empty(B, topk, dtype=torch.int32)
topk_weights, topk_ids = kernel.grouped_topk_cpu(
a, score, topk, renormalize, G, topk_group
)
packed_w1 = kernel.convert_weight_packed(w1) if prepack else w1
packed_w2 = kernel.convert_weight_packed(w2) if prepack else w2
inplace = True
return kernel.fused_experts_cpu(
a,
packed_w1,
packed_w2,
topk_weights,
topk_ids,
inplace,
False,
False,
None,
None,
None,
None,
None,
prepack,
)
class TestFusedExperts(CustomTestCase):
M = [2, 114]
N = [32]
K = [32]
E = [4]
topk = [2]
renormalize = [False, True]
M_int8 = [1, 39]
N_int8 = [128]
K_int8 = [256]
E_int8 = [8]
topk_int8 = [3]
M_fp8 = [2, 121]
N_fp8 = [512]
K_fp8 = [256]
E_fp8 = [8]
topk_fp8 = [4]
def _bf16_moe(self, m, n, k, e, topk, renormalize):
dtype = torch.bfloat16
prepack = True
a = torch.randn((m, k), device="cpu", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cpu", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cpu", dtype=dtype) / 10
score = torch.randn((m, e), device="cpu", dtype=dtype)
torch_output = torch_naive_fused_moe(a, w1, w2, score, topk, renormalize)
fused_output = fused_moe(a, w1, w2, score, topk, renormalize, prepack)
atol = rtol = precision[torch_output.dtype]
self.assertTrue(
torch.allclose(torch_output, fused_output, atol=atol, rtol=rtol)
)
def test_bf16_moe(self):
for params in itertools.product(
self.M,
self.N,
self.K,
self.E,
self.topk,
self.renormalize,
):
with self.subTest(
m=params[0],
n=params[1],
k=params[2],
e=params[3],
topk=params[4],
renormalize=params[5],
):
self._bf16_moe(*params)
def _int8_moe(self, M, N, K, E, topk):
dtype = torch.bfloat16
prepack = True
# Initialize int8 quantization parameters
int8_factor_for_scale = 1e-2
int8_max = 127
int8_min = -128
# Input tensor
# M * K
a = torch.randn((M, K), dtype=dtype) / math.sqrt(K)
# Generate int8 weights
w1_fp32 = (torch.rand((E, 2 * N, K), dtype=torch.float32) - 0.5) * 2
w1 = (w1_fp32 * int8_max).clamp(min=int8_min, max=int8_max).to(torch.int8)
w2_fp32 = (torch.rand((E, K, N), dtype=torch.float32) - 0.5) * 2
w2 = (w2_fp32 * int8_max).clamp(min=int8_min, max=int8_max).to(torch.int8)
# Generate scale for each column (per-column quantization)
w1_s = torch.rand(E, 2 * N, device=w1_fp32.device) * int8_factor_for_scale
w2_s = torch.rand(E, K, device=w2_fp32.device) * int8_factor_for_scale
# Calculate routing
score = torch.randn((M, E), dtype=dtype)
score = torch.softmax(score, dim=-1, dtype=torch.float32)
topk_weight, topk_ids = torch.topk(score, topk)
ref_out = torch_w8a8_per_column_fused_moe(
a, w1, w2, w1_s, w2_s, topk_weight, topk_ids, topk
)
inplace = True
packed_w1 = kernel.convert_weight_packed(w1) if prepack else w1
packed_w2 = kernel.convert_weight_packed(w2) if prepack else w2
out = kernel.fused_experts_cpu(
a,
packed_w1,
packed_w2,
topk_weight,
topk_ids.to(torch.int32),
inplace,
True,
False,
w1_s,
w2_s,
None,
None,
None,
prepack,
)
atol = rtol = precision[ref_out.dtype]
# Increase the tolerance for large input shapes
if M > 35:
atol = rtol = 0.02
self.assertTrue(torch.allclose(ref_out, out, atol=atol, rtol=rtol))
def test_int8_moe(self):
for params in itertools.product(
self.M_int8,
self.N_int8,
self.K_int8,
self.E_int8,
self.topk_int8,
):
with self.subTest(
M=params[0],
N=params[1],
K=params[2],
E=params[3],
topk=params[4],
):
self._int8_moe(*params)
def _fp8_moe(self, M, N, K, E, topk):
dtype = torch.bfloat16
a = torch.randn(M, K, dtype=dtype) / math.sqrt(K)
w1_fp32 = torch.randn(E, 2 * N, K)
w1 = (w1_fp32 * fp8_max).clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
w2_fp32 = torch.randn(E, K, N)
w2 = (w2_fp32 * fp8_max).clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
w1s = torch.randn(E, 2 * N // BLOCK_N, K // BLOCK_K) * factor_for_scale
w2s = torch.randn(E, K // BLOCK_N, N // BLOCK_K) * factor_for_scale
w1_scaled = scaled_weight(w1, w1s)
w2_scaled = scaled_weight(w2, w2s)
score = torch.randn((M, E), dtype=dtype)
score = torch.softmax(score, dim=-1, dtype=torch.float32)
topk_weight, topk_ids = torch.topk(score, topk)
w1 = kernel.convert_weight_packed(w1)
w2 = kernel.convert_weight_packed(w2)
ref_out = native_fp8_fused_moe(
a, w1_scaled, w2_scaled, topk_weight, topk_ids, topk
)
out = kernel.fused_experts_cpu(
a,
w1,
w2,
topk_weight,
topk_ids.to(torch.int32),
False,
False,
True,
w1s,
w2s,
[BLOCK_N, BLOCK_K],
None,
None,
True,
)
atol = rtol = precision[dtype]
self.assertTrue(torch.allclose(ref_out.bfloat16(), out, atol=atol, rtol=rtol))
def test_fp8_moe(self):
for params in itertools.product(
self.M_fp8,
self.N_fp8,
self.K_fp8,
self.E_fp8,
self.topk_fp8,
):
with self.subTest(
M=params[0],
N=params[1],
K=params[2],
E=params[3],
topk=params[4],
):
self._fp8_moe(*params)
if __name__ == "__main__":
unittest.main()
test/srt/cpu/utils.py
View file @ 3ded6235
......@@ -148,3 +148,99 @@ def scaled_weight(weight, scales):
.contiguous()
.view(E, N, K)
)
def torch_naive_fused_moe(a, w1, w2, score, topk, renormalize):
B, D = a.shape
a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
score = torch.softmax(score, dim=-1, dtype=torch.float32)
topk_weight, topk_ids = torch.topk(score, topk)
if renormalize:
topk_weight = topk_weight / topk_weight.sum(dim=-1, keepdim=True)
topk_weight = topk_weight.view(-1)
topk_ids = topk_ids.view(-1)
for i in range(w1.shape[0]):
mask = topk_ids == i
if mask.sum():
out[mask] = SiluAndMul(a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(
0, 1
)
return (
out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
).sum(dim=1)
def torch_w8a8_per_column_fused_moe(a, w1, w2, w1_s, w2_s, topk_weight, topk_ids, topk):
"""This function performs fused moe with per-column int8 quantization using native torch."""
B, D = a.shape
# Perform per-token quantization
a_q, a_s = per_token_quant_int8(a)
# Repeat tokens to match topk
a_q = a_q.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
# Also repeat the scale
a_s = a_s.view(B, -1, 1).repeat(1, topk, 1).reshape(-1, 1) # [B*topk, 1]
out = torch.zeros(B * topk, w2.shape[1], dtype=torch.float32, device=a.device)
# Calculate routing
topk_weight = topk_weight.view(-1)
topk_ids = topk_ids.view(-1)
# Process each expert
for i in range(w1.shape[0]):
mask = topk_ids == i
if mask.sum():
# First MLP layer: note that a_s is now per-token
inter_out = native_w8a8_per_token_matmul(
a_q[mask],
w1[i],
a_s[mask],
w1_s[i],
bias=None,
output_dtype=torch.float32,
)
# Activation function
act_out = SiluAndMul(inter_out)
# Quantize activation output with per-token
act_out_q, act_out_s = per_token_quant_int8(act_out)
# Second MLP layer
out[mask] = native_w8a8_per_token_matmul(
act_out_q,
w2[i],
act_out_s,
w2_s[i],
bias=None,
output_dtype=torch.float32,
)
# Apply routing weights and sum
return (
(out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype))
.sum(dim=1)
.to(a.dtype)
)
def native_fp8_fused_moe(a, w1, w2, topk_weight, topk_ids, topk):
B, D = a.shape
a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D).float()
out = torch.zeros(B * topk, w2.shape[1], dtype=torch.float32, device=a.device)
# Calculate routing
topk_weight = topk_weight.view(-1)
topk_ids = topk_ids.view(-1)
for i in range(w1.shape[0]):
mask = topk_ids == i
if mask.sum():
ic0 = torch.matmul(a[mask], w1[i].transpose(0, 1))
ic1 = SiluAndMul(ic0)
out[mask] = torch.matmul(ic1, w2[i].transpose(0, 1))
return (
(out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype))
.sum(dim=1)
.to(a.dtype)
)
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