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[CPU] support LayerNorm with 3D shape (#15075)

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Co-authored-by: Ma Mingfei <mingfei.ma@intel.com>
This commit is contained in:
Zaili Wang
2026-03-19 13:15:24 +08:00
committed by GitHub
parent dc6aa26ce9
commit 2f4babe32b
4 changed files with 196 additions and 54 deletions
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122
sgl-kernel/csrc/cpu/norm.cpp
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@@ -388,26 +388,32 @@ void fused_rmsnorm_gated_kernel_impl(
template <typename scalar_t>
void fused_add_layernorm_kernel_impl(
scalar_t* __restrict__ input,
scalar_t* __restrict__ output,
const scalar_t* __restrict__ input,
scalar_t* __restrict__ residual,
const scalar_t* __restrict__ weight,
const scalar_t* __restrict__ bias,
float* __restrict__ buffer,
int64_t batch_size,
int64_t seq_len,
int64_t hidden_size,
int64_t input_strideN,
float eps = 1e-5) {
using bVec = at::vec::Vectorized<scalar_t>;
using fVec = at::vec::Vectorized<float>;
constexpr int kVecSize = bVec::size();
at::parallel_for(0, batch_size, 0, [&](int64_t begin, int64_t end) {
int tid = at::get_thread_num();
float* __restrict__ buffer_ptr = buffer + tid * hidden_size;
const bool has_residual{residual != nullptr};
const bool has_bias{bias != nullptr};
const int64_t parallel_size{batch_size * seq_len};
at::parallel_for(0, parallel_size, 0, [&](int64_t begin, int64_t end) {
float* __restrict__ buffer_ptr = buffer + at::get_thread_num() * hidden_size;
for (int64_t i = begin; i < end; ++i) {
scalar_t* __restrict__ input_ptr = input + i * input_strideN;
scalar_t* __restrict__ out_ptr = output + i * hidden_size;
const scalar_t* __restrict__ input_ptr = input + i * input_strideN;
scalar_t* __restrict__ residual_ptr{(scalar_t*)nullptr};
if (residual != nullptr) {
if (has_residual) {
residual_ptr = residual + i * hidden_size;
}
@@ -422,7 +428,7 @@ void fused_add_layernorm_kernel_impl(
fVec x_fvec0, x_fvec1;
std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
if (residual_ptr != nullptr) {
if (has_residual) {
bVec r_bvec = bVec::loadu(residual_ptr + d);
fVec r_fvec0, r_fvec1;
std::tie(r_fvec0, r_fvec1) = at::vec::convert_to_float(r_bvec);
@@ -445,7 +451,7 @@ void fused_add_layernorm_kernel_impl(
#pragma GCC unroll 4
for (; d < hidden_size; ++d) {
float x_val = static_cast<float>(input_ptr[d]);
if (residual_ptr != nullptr) {
if (has_residual) {
float r_val = static_cast<float>(residual_ptr[d]);
x_val += r_val;
residual_ptr[d] = static_cast<scalar_t>(x_val);
@@ -475,7 +481,6 @@ void fused_add_layernorm_kernel_impl(
for (d = 0; d <= hidden_size - kVecSize; d += kVecSize) {
fVec x_fvec0 = fVec::loadu(buffer_ptr + d);
fVec x_fvec1 = fVec::loadu(buffer_ptr + d + fVec::size());
bVec w_bvec = bVec::loadu(weight + d);
fVec w_fvec0, w_fvec1;
std::tie(w_fvec0, w_fvec1) = at::vec::convert_to_float(w_bvec);
@@ -483,14 +488,25 @@ void fused_add_layernorm_kernel_impl(
x_fvec0 = (x_fvec0 - mean_fvec) * scale_fvec * w_fvec0;
x_fvec1 = (x_fvec1 - mean_fvec) * scale_fvec * w_fvec1;
bVec x_bvec = convert_from_float_ext<scalar_t>(x_fvec0, x_fvec1);
x_bvec.store(input_ptr + d);
if (has_bias) {
bVec b_bvec = bVec::loadu(bias + d);
fVec b_fvec0, b_fvec1;
std::tie(b_fvec0, b_fvec1) = at::vec::convert_to_float(b_bvec);
x_fvec0 += b_fvec0;
x_fvec1 += b_fvec1;
}
bVec o_bvec = convert_from_float_ext<scalar_t>(x_fvec0, x_fvec1);
o_bvec.store(out_ptr + d);
}
#pragma GCC unroll 4
for (; d < hidden_size; ++d) {
float normalized = (buffer_ptr[d] - mean) * rsqrt_var;
float x_val = normalized * static_cast<float>(weight[d]);
input_ptr[d] = static_cast<scalar_t>(x_val);
if (has_bias) {
x_val += static_cast<float>(bias[d]);
}
out_ptr[d] = static_cast<scalar_t>(x_val);
}
}
});
@@ -543,33 +559,52 @@ at::Tensor rmsnorm_cpu(at::Tensor& input, at::Tensor& weight, double eps) {
return output;
}
// input : {batch_size, hidden_size}
// input : {batch_size, hidden_size} or {batch_size, seq_len, hidden_size}
// weight: {hidden_size}
void layernorm_cpu(at::Tensor& input, at::Tensor& weight, double eps) {
// bias : {hidden_size}
at::Tensor
layernorm_cpu(const at::Tensor& input, const at::Tensor& weight, const std::optional<at::Tensor>& bias, double eps) {
RECORD_FUNCTION("sgl-kernel::layernorm_cpu", std::vector<c10::IValue>({input, weight}));
CHECK_LAST_DIM_CONTIGUOUS_INPUT(input);
CHECK_INPUT(weight);
CHECK_DIM(2, input);
int64_t inp_dim{input.dim()};
TORCH_CHECK(inp_dim == 2 || inp_dim == 3, "Expected input dim to be 2 or 3, but got ", inp_dim);
CHECK_DIM(1, weight);
CHECK_EQ(input.size(1), weight.size(0));
int64_t batch_size = input.size(0);
int64_t hidden_size = input.size(1);
int64_t input_strideN = input.stride(0);
if (bias.has_value()) {
CHECK_DIM(1, bias.value());
CHECK_EQ(bias.value().size(0), weight.size(0));
}
int64_t batch_size{input.size(0)}, seq_len{1}, hidden_size{input.size(1)}, input_strideN{input.stride(0)};
if (inp_dim == 3) {
CHECK_EQ(input.size(2), weight.size(0));
seq_len = input.size(1);
hidden_size = input.size(2);
input_strideN = input.stride(1);
} else {
CHECK_EQ(input.size(1), weight.size(0));
}
at::Tensor output = at::empty_like(input);
int64_t num_threads = at::get_num_threads();
at::Tensor buffer = at::empty({num_threads, hidden_size}, input.options().dtype(at::kFloat));
AT_DISPATCH_REDUCED_FLOATING_TYPES(input.scalar_type(), "layernorm_kernel", [&] {
fused_add_layernorm_kernel_impl<scalar_t>(
output.data_ptr<scalar_t>(),
input.data_ptr<scalar_t>(),
nullptr,
weight.data_ptr<scalar_t>(),
conditional_data_ptr<scalar_t>(bias),
buffer.data_ptr<float>(),
batch_size,
seq_len,
hidden_size,
input_strideN,
eps);
});
return output;
}
at::Tensor gemma_rmsnorm_cpu(at::Tensor& input, at::Tensor& weight, double eps) {
@@ -776,36 +811,65 @@ void gemma_fused_add_rmsnorm_cpu(at::Tensor& input, at::Tensor& residual, at::Te
});
}
// input : {batch_size, hidden_size}
// residual: {batch_size, hidden_size}
// input : {batch_size, hidden_size} or {batch_size, seq_len, hidden_size}
// residual: {batch_size, hidden_size} or {batch_size, seq_len, hidden_size}
// weight : {hidden_size}
void fused_add_layernorm_cpu(at::Tensor& input, at::Tensor& residual, at::Tensor& weight, double eps) {
// bias : {hidden_size}
at::Tensor fused_add_layernorm_cpu(
const at::Tensor& input,
at::Tensor& residual,
const at::Tensor& weight,
const std::optional<at::Tensor>& bias,
double eps) {
RECORD_FUNCTION("sgl-kernel::fused_add_layernorm_cpu", std::vector<c10::IValue>({input, residual, weight}));
CHECK_LAST_DIM_CONTIGUOUS_INPUT(input);
CHECK_INPUT(residual);
CHECK_INPUT(weight);
CHECK_DIM(2, input);
CHECK_DIM(2, residual);
int64_t inp_dim{input.dim()}, res_dim{residual.dim()};
CHECK_EQ(inp_dim, res_dim);
TORCH_CHECK(inp_dim == 2 || inp_dim == 3, "Expected input dim to be 2 or 3, but got ", inp_dim);
TORCH_CHECK(res_dim == 2 || res_dim == 3, "Expected residual dim to be 2 or 3, but got ", res_dim);
CHECK_DIM(1, weight);
if (bias.has_value()) {
CHECK_DIM(1, bias.value());
CHECK_EQ(bias.value().size(0), weight.size(0));
}
CHECK_EQ(input.size(0), residual.size(0));
CHECK_EQ(input.size(1), residual.size(1));
CHECK_EQ(input.size(1), weight.size(0));
int64_t batch_size = input.size(0);
int64_t hidden_size = input.size(1);
int64_t input_strideN = input.stride(0);
if (inp_dim == 3) {
CHECK_EQ(input.size(2), residual.size(2));
CHECK_EQ(input.size(2), weight.size(0));
} else {
CHECK_EQ(input.size(1), weight.size(0));
}
int64_t batch_size{input.size(0)}, seq_len{1}, hidden_size{input.size(1)}, input_strideN{input.stride(0)};
if (inp_dim == 3) {
seq_len = input.size(1);
hidden_size = input.size(2);
input_strideN = input.stride(1);
}
at::Tensor output = at::empty_like(input);
// Allocate temp buffer to store x in float32 per thread
// It is necessary to store FP32 precision of residual-add results to pass UT acc test
int64_t num_threads = at::get_num_threads();
at::Tensor buffer = at::empty({num_threads, hidden_size}, input.options().dtype(at::kFloat));
AT_DISPATCH_REDUCED_FLOATING_TYPES(input.scalar_type(), "fused_add_layernorm_kernel", [&] {
fused_add_layernorm_kernel_impl<scalar_t>(
output.data_ptr<scalar_t>(),
input.data_ptr<scalar_t>(),
residual.data_ptr<scalar_t>(),
weight.data_ptr<scalar_t>(),
conditional_data_ptr<scalar_t>(bias),
buffer.data_ptr<float>(),
batch_size,
seq_len,
hidden_size,
input_strideN,
eps);
});
return output;
}