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Merged
bigPYJ1151 merged 4 commits into from
Nov 4, 2025
Merged

[CPU]Improve dynamic 4bit moe performance #27240

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00e2d64
[CPU]Improve dynamic 4bit moe performance
xiangze-arm Sep 17, 2025
863aca8
Fix pre-commit
xiangze-arm Oct 22, 2025
c083ca4
Revert silu_and_mul change
xiangze-arm Nov 3, 2025
0119fc3
Merge branch 'main' into int4_moe
bigPYJ1151 Nov 3, 2025
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47 changes: 20 additions & 27 deletions csrc/moe/dynamic_4bit_int_moe_cpu.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,8 @@ inline torch::Tensor mm(const torch::Tensor& a, const torch::Tensor& packed_w,
#endif
}

extern void silu_and_mul(torch::Tensor& out, torch::Tensor& input);

enum ActivationKind : int64_t {
SwiGLU_Gu = 0, // act = SiLU(g) * u
SwiGLUOAI = 1, // act = SiLU(u) * g
Expand Down Expand Up @@ -87,30 +89,23 @@ torch::Tensor dynamic_4bit_int_moe_cpu(
const int64_t g_eff_13 = (group_size != -1) ? group_size : H;
const int64_t g_eff_2 = (group_size != -1) ? group_size : I;

// Per-expert outputs filled in parallel
std::vector<torch::Tensor> y_list(E);
y_list.resize(E);
auto X_all = x_c.index_select(/*dim=*/0, expert_tokens);
if (apply_router_weight_on_input) {
X_all = X_all.mul(expert_gates.unsqueeze(1));
}
auto Y_all = at::empty({offsets[E], H}, x_c.options());

at::parallel_for(0, E, 1, [&](int64_t e_begin, int64_t e_end) {
c10::InferenceMode guard;
for (int64_t e = e_begin; e < e_end; ++e) {
const int64_t te = counts[e];
if (te == 0) {
y_list[e] = at::empty({0, H}, x_c.options());
continue;
}

const int64_t start = offsets[e];

auto sel_tokens =
expert_tokens.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);
auto gates_e =
expert_gates.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);

auto x_e = x_c.index_select(/*dim=*/0, sel_tokens);

if (apply_router_weight_on_input) {
x_e = x_e.mul(gates_e.unsqueeze(1));
}
auto x_e = X_all.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);

auto w13_e = w13_packed.select(/*dim=*/0, e);
auto w2_e = w2_packed.select(/*dim=*/0, e);
Expand All @@ -119,35 +114,33 @@ torch::Tensor dynamic_4bit_int_moe_cpu(
auto y13 =
mm(x_e, w13_e, g_eff_13, /*in_features=*/H, /*out_features=*/I2);

auto g_part = y13.narrow(/*dim=*/1, /*start=*/0, /*length=*/I);
auto u_part = y13.narrow(/*dim=*/1, /*start=*/I, /*length=*/I);

torch::Tensor act;
if (activation_kind == ActivationKind::SwiGLUOAI) { // SwiGLUOAI
constexpr double kAlpha = 1.702; // GPT-OSS default
constexpr double kLimit = 7.0; // GPT-OSS default
auto g_part = y13.narrow(/*dim=*/1, /*start=*/0, /*length=*/I);
auto u_part = y13.narrow(/*dim=*/1, /*start=*/I, /*length=*/I);
constexpr double kAlpha = 1.702; // GPT-OSS default
constexpr double kLimit = 7.0; // GPT-OSS default
auto gate_c = at::clamp_max(g_part, kLimit);
auto up_c = at::clamp(u_part, -kLimit, kLimit);
auto glu = gate_c.mul(at::sigmoid(gate_c.mul(kAlpha)));
act = up_c.add(1.0).mul(glu);
} else { // SiLU , SwiGLU_GU, vLLM maps silu to SiluAndMul()
act = at::silu(g_part).mul(u_part);
act = at::empty({te, I}, y13.options());
silu_and_mul(act, y13);
}

// W2
auto y = mm(act, w2_e, g_eff_2, /*in_features=*/I, /*out_features=*/H);

if (!apply_router_weight_on_input) {
y = y.mul(gates_e.unsqueeze(1));
}

// Store per-expert result
y_list[e] = y;
Y_all.narrow(/*dim=*/0, /*start=*/start, /*length=*/te).copy_(y);
}
});

// Concatenate all expert outputs to match expert_tokens order
auto Y_all = at::cat(y_list, /*dim=*/0);
if (!apply_router_weight_on_input) {
Y_all = Y_all.mul(expert_gates.unsqueeze(1));
}

auto out = at::zeros({T, H}, x.options());
out =
at::index_add(out, /*dim=*/0, /*index=*/expert_tokens, /*source=*/Y_all);
Expand Down