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[CPU] add fused input proj for qwen3.5 (#31171)

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This commit is contained in:
Ma Mingfei
2026-07-15 15:06:24 +08:00
committed by GitHub
parent a649b5a9db
commit 41e0b4b369
5 changed files with 238 additions and 94 deletions
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python/sglang/srt/model_executor/cpu_graph_runner.py
+9
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@@ -490,6 +490,15 @@ def register_fake_ops(tp_size: int):
a = mixed_ba.new_empty(batch, num_heads_v)
return mixed_qkv, z, b, a
@register_cpu_compile_fake("fused_input_proj_cpu")
def _(hidden_states, qkvz_weight, ba_weight, is_vnni):
batch = hidden_states.shape[0]
qkvz_dim = qkvz_weight.shape[0]
ba_dim = ba_weight.shape[0]
return hidden_states.new_empty(batch, qkvz_dim), hidden_states.new_empty(
batch, ba_dim
)
@register_cpu_compile_fake("fused_sigmoid_gating_delta_rule_update_cpu")
def _(
A_log,
python/sglang/srt/models/qwen3_5.py
+33 -18
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@@ -112,6 +112,7 @@ from sglang.srt.utils import (
is_xpu,
make_layers,
set_weight_attrs,
use_intel_amx_backend,
)
from sglang.srt.utils.hf_transformers_utils import get_processor, get_rope_config
@@ -152,6 +153,9 @@ if _is_cpu:
fused_qk_gemma_rmsnorm_with_gate = (
torch.ops.sgl_kernel.fused_qk_gemma_rmsnorm_with_gate_cpu
)
fused_qkvzba_split_reshape_cat_contiguous = (
torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_contiguous_cpu
)
if _is_npu:
from sgl_kernel_npu.norm.split_qkv_rmsnorm_rope import (
@@ -233,6 +237,17 @@ class Qwen3_5GatedDeltaNet(nn.Module):
# `weight_scale_inv` / `weight_scale` / `input_scale` if present.
self._bind_packed_weight_loaders(self.in_proj_qkvz)
self._bind_packed_weight_loaders(self.in_proj_ba)
self._fused_input_proj_cpu_enabled = LazyValue(
lambda: (
_is_cpu
and self.in_proj_qkvz.weight.dtype == torch.bfloat16
and self.in_proj_ba.weight.dtype == torch.bfloat16
and self.in_proj_qkvz.bias is None
and self.in_proj_ba.bias is None
and use_intel_amx_backend(self.in_proj_qkvz)
and use_intel_amx_backend(self.in_proj_ba)
)
)
# Conv1d weight loader setup
query_key_settings = (self.key_dim, 0, False)
@@ -497,6 +512,15 @@ class Qwen3_5GatedDeltaNet(nn.Module):
with torch.cuda.stream(self.alt_stream):
projected_states_ba, _ = self.in_proj_ba(hidden_states)
current_stream.wait_stream(self.alt_stream)
elif self._fused_input_proj_cpu_enabled.value:
projected_states_qkvz, projected_states_ba = (
torch.ops.sgl_kernel.fused_input_proj_cpu(
hidden_states,
self.in_proj_qkvz.weight,
self.in_proj_ba.weight,
True,
)
)
else:
projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
projected_states_ba, _ = self.in_proj_ba(hidden_states)
@@ -517,30 +541,21 @@ class Qwen3_5GatedDeltaNet(nn.Module):
hidden_states
)
if (
self.num_v_heads // self.num_k_heads in [1, 2, 4]
and not _is_cpu
and not _is_npu
):
if self.num_v_heads // self.num_k_heads in [1, 2, 4] and not _is_npu:
if _is_cpu:
num_k_heads_tp = self.num_k_heads // self.attn_tp_size
num_v_heads_tp = self.num_v_heads // self.attn_tp_size
else:
num_k_heads_tp = triton.cdiv(self.num_k_heads, self.attn_tp_size)
num_v_heads_tp = triton.cdiv(self.num_v_heads, self.attn_tp_size)
mixed_qkv, z, b, a = fused_qkvzba_split_reshape_cat_contiguous(
projected_states_qkvz,
projected_states_ba,
triton.cdiv(self.num_k_heads, self.attn_tp_size),
triton.cdiv(self.num_v_heads, self.attn_tp_size),
num_k_heads_tp,
num_v_heads_tp,
self.head_k_dim,
self.head_v_dim,
)
elif _is_cpu and _is_amx_available:
mixed_qkv, z, b, a = (
torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_contiguous_cpu(
projected_states_qkvz,
projected_states_ba,
self.num_k_heads // self.attn_tp_size,
self.num_v_heads // self.attn_tp_size,
self.head_k_dim,
self.head_v_dim,
)
)
else:
query, key, value, z, b, a = self.fix_query_key_value_ordering(
projected_states_qkvz, projected_states_ba
sgl-kernel/csrc/cpu/model/qwen3.cpp
+105 -18
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@@ -1,4 +1,5 @@
#include "common.h"
#include "gemm.h"
#include "vec.h"
namespace {
@@ -71,7 +72,6 @@ void fused_qkvzba_split_reshape_cat_contiguous_impl(
scalar_t* __restrict__ b,
scalar_t* __restrict__ a,
int64_t batch,
int64_t k_tp,
int64_t v_tp,
int64_t num_heads_v,
int64_t qkv_dim,
@@ -96,6 +96,60 @@ void fused_qkvzba_split_reshape_cat_contiguous_impl(
});
}
template <typename scalar_t>
void fused_input_proj_kernel_impl(
scalar_t* __restrict__ out,
scalar_t* __restrict__ out2,
const scalar_t* __restrict__ input,
const scalar_t* __restrict__ weight,
const scalar_t* __restrict__ weight2,
int64_t M,
int64_t N,
int64_t N2,
int64_t K) {
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 + N2, BLOCK_N);
const bool use_brgemm = can_use_brgemm<scalar_t>(M);
// parallel on [MB, NB]
parallel_2d(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];
loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
int64_t mb_start = mb * BLOCK_M;
int64_t mb_size = std::min(M - mb_start, BLOCK_M);
int64_t nb_start = nb * BLOCK_N;
const bool is_first = nb_start < N;
int64_t local_nb_start = is_first ? nb_start : nb_start - N;
int64_t nb_size = std::min((is_first ? N : N2) - local_nb_start, BLOCK_N);
scalar_t* __restrict__ curr_out = is_first ? out : out2;
const scalar_t* __restrict__ curr_weight = is_first ? weight : weight2;
int64_t local_out_strideM = is_first ? N : N2;
tinygemm_kernel<scalar_t>(
/* A */ input + mb_start * K,
/* B */ curr_weight + local_nb_start * K,
/* C */ curr_out + mb_start * local_out_strideM + local_nb_start,
/* Ctmp*/ Ctmp,
/* M */ mb_size,
/* N */ nb_size,
/* K */ K,
/* lda */ K,
/* ldb */ nb_size,
/* ldc */ local_out_strideM,
/* brg */ use_brgemm);
});
if (use_brgemm) {
at::native::cpublas::brgemm_release();
}
});
}
} // anonymous namespace
// mixed_qkvz: [batch, num_heads_qk * head_qk * 2 + num_heads_v * head_v * 2]
@@ -107,18 +161,12 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> fused_qkvzba_split_re
int64_t num_heads_v,
int64_t head_qk,
int64_t head_v) {
CHECK_DIM(2, mixed_qkvz);
CHECK_DIM(2, mixed_ba);
CHECK_INPUT(mixed_qkvz);
CHECK_INPUT(mixed_ba);
int64_t batch = mixed_qkvz.size(0);
int64_t qkv_dim = num_heads_qk * head_qk * 2 + num_heads_v * head_v;
int64_t ba_dim = num_heads_v * 2;
int64_t expected_dim = qkv_dim + num_heads_v * head_v;
CHECK_EQ(mixed_qkvz.size(1), expected_dim);
CHECK_EQ(mixed_ba.size(0), batch);
CHECK_EQ(mixed_ba.size(1), ba_dim);
TORCH_CHECK(mixed_ba.scalar_type() == mixed_qkvz.scalar_type(), "mixed_ba and mixed_qkvz must share same dtype");
CHECK_INPUT_SHAPE_DTYPE<false>(mixed_qkvz, {batch, expected_dim}, mixed_qkvz.scalar_type());
CHECK_INPUT_SHAPE_DTYPE<false>(mixed_ba, {batch, ba_dim}, mixed_qkvz.scalar_type());
CHECK_EQ(num_heads_v % num_heads_qk, 0);
at::Tensor mixed_qkv = at::empty({batch, qkv_dim}, mixed_qkvz.options());
at::Tensor z = at::empty({batch, num_heads_v, head_v}, mixed_qkvz.options());
@@ -158,20 +206,14 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> fused_qkvzba_split_re
int64_t num_heads_v,
int64_t head_qk,
int64_t head_v) {
CHECK_DIM(2, mixed_qkvz);
CHECK_DIM(2, mixed_ba);
CHECK_INPUT(mixed_qkvz);
CHECK_INPUT(mixed_ba);
int64_t batch = mixed_qkvz.size(0);
int64_t k_tp = num_heads_qk * head_qk;
int64_t v_tp = num_heads_v * head_v;
int64_t qkv_dim = k_tp * 2 + v_tp;
int64_t ba_dim = num_heads_v * 2;
int64_t expected_dim = qkv_dim + v_tp;
CHECK_EQ(mixed_qkvz.size(1), expected_dim);
CHECK_EQ(mixed_ba.size(0), batch);
CHECK_EQ(mixed_ba.size(1), ba_dim);
TORCH_CHECK(mixed_ba.scalar_type() == mixed_qkvz.scalar_type(), "mixed_ba and mixed_qkvz must share same dtype");
CHECK_INPUT_SHAPE_DTYPE<false>(mixed_qkvz, {batch, expected_dim}, mixed_qkvz.scalar_type());
CHECK_INPUT_SHAPE_DTYPE<false>(mixed_ba, {batch, ba_dim}, mixed_qkvz.scalar_type());
at::Tensor mixed_qkv = at::empty({batch, qkv_dim}, mixed_qkvz.options());
at::Tensor z = at::empty({batch, num_heads_v, head_v}, mixed_qkvz.options());
at::Tensor b = at::empty({batch, num_heads_v}, mixed_ba.options());
@@ -188,7 +230,6 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> fused_qkvzba_split_re
b.data_ptr<scalar_t>(),
a.data_ptr<scalar_t>(),
batch,
k_tp,
v_tp,
num_heads_v,
qkv_dim,
@@ -198,3 +239,49 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> fused_qkvzba_split_re
});
return std::make_tuple(mixed_qkv, z, b, a);
}
// [projected_states_qkvz |projected_states_ba]
// = hidden_states @ [qkvz_weight.T | ba_weight.T]
//
// hidden_states : [batch, hidden_size]
// qkvz_weight : [qkvz_dim, hidden_size]
// ba_weight : [ba_dim, hidden_size]
// projected_states_qkvz : [batch, qkvz_dim]
// projected_states_ba : [batch, ba_dim]
//
std::tuple<at::Tensor, at::Tensor>
fused_input_proj_cpu(at::Tensor& hidden_states, at::Tensor& qkvz_weight, at::Tensor& ba_weight, bool is_vnni) {
const auto st = hidden_states.scalar_type();
TORCH_CHECK(st == at::ScalarType::BFloat16, "fused_input_proj_cpu only supports BFloat16");
int64_t batch = hidden_states.size(0);
int64_t hidden_size = hidden_states.size(1);
int64_t qkvz_dim = qkvz_weight.size(0);
int64_t ba_dim = ba_weight.size(0);
CHECK_INPUT(hidden_states);
CHECK_INPUT_SHAPE_DTYPE<false>(qkvz_weight, {qkvz_dim, hidden_size}, st);
CHECK_INPUT_SHAPE_DTYPE<false>(ba_weight, {ba_dim, hidden_size}, st);
TORCH_CHECK(qkvz_dim % block_size_n() == 0, "qkvz_weight out features must be divisible by ", block_size_n());
TORCH_CHECK(ba_dim % block_size_n() == 0, "ba_weight out features must be divisible by ", block_size_n());
TORCH_CHECK(hidden_size % TILE_K == 0, "hidden_size must be divisible by ", TILE_K);
// weight prepacking if necessary
at::Tensor packed_w = is_vnni ? qkvz_weight : convert_weight_packed(qkvz_weight);
at::Tensor packed_w2 = is_vnni ? ba_weight : convert_weight_packed(ba_weight);
at::Tensor projected_states_qkvz = at::empty({batch, qkvz_dim}, hidden_states.options());
at::Tensor projected_states_ba = at::empty({batch, ba_dim}, hidden_states.options());
AT_DISPATCH_REDUCED_FLOATING_TYPES(st, "fused_input_proj_cpu", [&] {
fused_input_proj_kernel_impl<scalar_t>(
projected_states_qkvz.data_ptr<scalar_t>(),
projected_states_ba.data_ptr<scalar_t>(),
hidden_states.data_ptr<scalar_t>(),
packed_w.data_ptr<scalar_t>(),
packed_w2.data_ptr<scalar_t>(),
batch,
qkvz_dim,
ba_dim,
hidden_size);
});
return std::make_tuple(projected_states_qkvz, projected_states_ba);
}
sgl-kernel/csrc/cpu/torch_extension_cpu.cpp
+9
View File
@@ -502,6 +502,10 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> fused_qkvzba_split_re
int64_t head_qk,
int64_t head_v);
// fused_input_proj_cpu
std::tuple<at::Tensor, at::Tensor>
fused_input_proj_cpu(at::Tensor& hidden_states, at::Tensor& qkvz_weight, at::Tensor& ba_weight, bool is_vnni);
// image preprocessor
std::tuple<at::Tensor, at::Tensor> image_preprocess_cpu(
at::TensorList images,
@@ -844,6 +848,11 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
"num_heads_v, int "
"head_qk, int head_v) -> (Tensor, Tensor, Tensor, Tensor)");
m.impl("fused_qkvzba_split_reshape_cat_contiguous_cpu", torch::kCPU, &fused_qkvzba_split_reshape_cat_contiguous_cpu);
// fused_input_proj_cpu
m.def(
"fused_input_proj_cpu(Tensor hidden_states, Tensor qkvz_weight, Tensor ba_weight, bool is_vnni) -> (Tensor, "
"Tensor)");
m.impl("fused_input_proj_cpu", torch::kCPU, &fused_input_proj_cpu);
// image preprocessor
m.def(
test/registered/cpu/test_qwen3.py
+82 -58
View File
@@ -1,10 +1,11 @@
import unittest
import sys
import pytest
import torch
from utils import precision
from sglang.srt.utils import is_host_cpu_arm64
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase
register_cpu_ci(est_time=10, suite="base-b-test-cpu")
register_cpu_ci(est_time=10, suite="base-b-test-cpu-arm64")
@@ -85,69 +86,92 @@ def fix_query_key_value_ordering_reshape_cat_contiguous(
return mixed_qkv, z, b, a
class TestQwen3(CustomTestCase):
def test_fused_qkvzba_split_reshape_cat(self):
mixed_qkvz = torch.rand(1024, 12288, dtype=torch.bfloat16)
mixed_ba = torch.rand(1024, 64, dtype=torch.bfloat16)
head_k_dim = 128
head_v_dim = 128
num_v_heads = 32
num_k_heads = 16
attn_tp_size = 1
mixed_qkv_ref, z_ref, b_ref, a_ref = fix_query_key_value_ordering_reshape_cat(
@pytest.mark.skipif(
is_host_cpu_arm64(), reason="fused_input_proj_cpu is an x86 AMX kernel"
)
def test_fused_input_proj():
batch = 7
hidden_size = 256
qkvz_dim = 128
ba_dim = 64
hidden_states = torch.randn(batch, hidden_size, dtype=torch.bfloat16)
qkvz_weight = torch.randn(qkvz_dim, hidden_size, dtype=torch.bfloat16)
ba_weight = torch.randn(ba_dim, hidden_size, dtype=torch.bfloat16)
qkvz_ref = torch.nn.functional.linear(hidden_states, qkvz_weight)
ba_ref = torch.nn.functional.linear(hidden_states, ba_weight)
qkvz, ba = torch.ops.sgl_kernel.fused_input_proj_cpu(
hidden_states, qkvz_weight, ba_weight, False
)
atol = rtol = precision[qkvz.dtype]
torch.testing.assert_close(qkvz, qkvz_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(ba, ba_ref, atol=atol, rtol=rtol)
def test_fused_qkvzba_split_reshape_cat():
mixed_qkvz = torch.rand(1024, 12288, dtype=torch.bfloat16)
mixed_ba = torch.rand(1024, 64, dtype=torch.bfloat16)
head_k_dim = 128
head_v_dim = 128
num_v_heads = 32
num_k_heads = 16
attn_tp_size = 1
mixed_qkv_ref, z_ref, b_ref, a_ref = fix_query_key_value_ordering_reshape_cat(
mixed_qkvz,
mixed_ba,
num_k_heads,
num_v_heads,
attn_tp_size,
head_k_dim,
head_v_dim,
)
num_heads_qk = num_k_heads // attn_tp_size
num_heads_v = num_v_heads // attn_tp_size
mixed_qkv, z, b, a = torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_cpu(
mixed_qkvz, mixed_ba, num_heads_qk, num_heads_v, head_k_dim, head_v_dim
)
atol = rtol = precision[mixed_qkv.dtype]
torch.testing.assert_close(mixed_qkv, mixed_qkv_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(z, z_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(b, b_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(a, a_ref, atol=atol, rtol=rtol)
def test_fused_qkvzba_split_reshape_cat_contiguous():
mixed_qkvz = torch.rand(1, 12288, dtype=torch.bfloat16)
mixed_ba = torch.rand(1, 64, dtype=torch.bfloat16)
head_k_dim = 128
head_v_dim = 128
num_v_heads = 32
num_k_heads = 16
attn_tp_size = 1
key_dim = head_k_dim * num_k_heads
value_dim = head_v_dim * num_v_heads
mixed_qkv_ref, z_ref, b_ref, a_ref = (
fix_query_key_value_ordering_reshape_cat_contiguous(
mixed_qkvz,
mixed_ba,
num_k_heads,
key_dim,
value_dim,
num_v_heads,
attn_tp_size,
head_k_dim,
head_v_dim,
attn_tp_size,
)
num_heads_qk = num_k_heads // attn_tp_size
num_heads_v = num_v_heads // attn_tp_size
mixed_qkv, z, b, a = torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_cpu(
)
num_heads_qk = num_k_heads // attn_tp_size
num_heads_v = num_v_heads // attn_tp_size
mixed_qkv, z, b, a = (
torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_contiguous_cpu(
mixed_qkvz, mixed_ba, num_heads_qk, num_heads_v, head_k_dim, head_v_dim
)
atol = rtol = precision[mixed_qkv.dtype]
torch.testing.assert_close(mixed_qkv, mixed_qkv_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(z, z_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(b, b_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(a, a_ref, atol=atol, rtol=rtol)
def test_fused_qkvzba_split_reshape_cat_contiguous(self):
mixed_qkvz = torch.rand(1, 12288, dtype=torch.bfloat16)
mixed_ba = torch.rand(1, 64, dtype=torch.bfloat16)
head_k_dim = 128
head_v_dim = 128
num_v_heads = 32
num_k_heads = 16
attn_tp_size = 1
key_dim = head_k_dim * num_k_heads
value_dim = head_v_dim * num_v_heads
mixed_qkv_ref, z_ref, b_ref, a_ref = (
fix_query_key_value_ordering_reshape_cat_contiguous(
mixed_qkvz,
mixed_ba,
key_dim,
value_dim,
num_v_heads,
head_v_dim,
attn_tp_size,
)
)
num_heads_qk = num_k_heads // attn_tp_size
num_heads_v = num_v_heads // attn_tp_size
mixed_qkv, z, b, a = (
torch.ops.sgl_kernel.fused_qkvzba_split_reshape_cat_contiguous_cpu(
mixed_qkvz, mixed_ba, num_heads_qk, num_heads_v, head_k_dim, head_v_dim
)
)
atol = rtol = precision[mixed_qkv.dtype]
torch.testing.assert_close(mixed_qkv, mixed_qkv_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(z, z_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(b, b_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(a, a_ref, atol=atol, rtol=rtol)
)
atol = rtol = precision[mixed_qkv.dtype]
torch.testing.assert_close(mixed_qkv, mixed_qkv_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(z, z_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(b, b_ref, atol=atol, rtol=rtol)
torch.testing.assert_close(a, a_ref, atol=atol, rtol=rtol)
if __name__ == "__main__":
unittest.main()
sys.exit(pytest.main([__file__]))