Add RMS norm (#979)
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masahi
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tools/util/include/cutlass/util/device_rmsnorm.h
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185
tools/util/include/cutlass/util/device_rmsnorm.h
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/******************************************************************************
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* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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******************************************************************************/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/layout/tensor.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/tensor_coord.h"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/util/device_utils.h"
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#include <float.h>
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namespace cutlass {
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__global__ void rmsnorm_twoPassAlgo_e8(float4 *output, const float4 *input,
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const float4 *weight,
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const int m, const int n) {
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const int m_idx = blockIdx.x;
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const int tid = threadIdx.x;
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const int bdimx = blockDim.x;
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__shared__ float s_mean;
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float local_sums[1] = {0.0f};
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const int n_8 = n / 8;
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int offset = m_idx * n_8;
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input += offset;
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output += offset;
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for (int index = tid; index < n_8; index += bdimx) {
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const float4 local_val = input[index];
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const half2 *h1 = (half2 *)&local_val.x;
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const half2 *h2 = (half2 *)&local_val.y;
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const half2 *h3 = (half2 *)&local_val.z;
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const half2 *h4 = (half2 *)&local_val.w;
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local_sums[0] += static_cast<float>(h1->x) * static_cast<float>(h1->x) +
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static_cast<float>(h1->y) * static_cast<float>(h1->y) +
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static_cast<float>(h2->x) * static_cast<float>(h2->x) +
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static_cast<float>(h2->y) * static_cast<float>(h2->y) +
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static_cast<float>(h3->x) * static_cast<float>(h3->x) +
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static_cast<float>(h3->y) * static_cast<float>(h3->y) +
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static_cast<float>(h4->x) * static_cast<float>(h4->x) +
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static_cast<float>(h4->y) * static_cast<float>(h4->y);
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}
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if (blockDim.x <= 32) {
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warpReduceSum<float, 1>(local_sums);
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} else {
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blockReduceSum<float, 1>(local_sums);
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}
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if (threadIdx.x == 0) {
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s_mean = rsqrtf(local_sums[0] / n + 1e-6);
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}
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__syncthreads();
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for (int index = tid; index < n_8; index += bdimx) {
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const float4 local_val = input[index];
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const float4 weight_val = weight[index];
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const half2 *l1 = (half2 *)&local_val.x;
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const half2 *l2 = (half2 *)&local_val.y;
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const half2 *l3 = (half2 *)&local_val.z;
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const half2 *l4 = (half2 *)&local_val.w;
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const half2 *g1 = (half2 *)&weight_val.x;
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const half2 *g2 = (half2 *)&weight_val.y;
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const half2 *g3 = (half2 *)&weight_val.z;
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const half2 *g4 = (half2 *)&weight_val.w;
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float4 tmp;
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half2 *h1 = (half2 *)&tmp.x;
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half2 *h2 = (half2 *)&tmp.y;
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half2 *h3 = (half2 *)&tmp.z;
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half4 *h4 = (half4 *)&tmp.w;
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h1->x = half(static_cast<float>(l1->x) * s_mean * static_cast<float>(g1->x));
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h1->y = half(static_cast<float>(l1->y) * s_mean * static_cast<float>(g1->y));
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h2->x = half(static_cast<float>(l2->x) * s_mean * static_cast<float>(g2->x));
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h2->y = half(static_cast<float>(l2->y) * s_mean * static_cast<float>(g2->y));
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h3->x = half(static_cast<float>(l3->x) * s_mean * static_cast<float>(g3->x));
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h3->y = half(static_cast<float>(l3->y) * s_mean * static_cast<float>(g3->y));
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h4->x = half(static_cast<float>(l4->x) * s_mean * static_cast<float>(g4->x));
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h4->y = half(static_cast<float>(l4->y) * s_mean * static_cast<float>(g4->y));
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output[index] = tmp;
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}
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}
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template<typename T>
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__global__ void rmsnorm_twoPassAlgo_e1(T* output,
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const T* input,
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const T* weight,
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const int m, const int n)
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{
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const int m_idx = blockIdx.x;
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const int tid = threadIdx.x;
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const int bdimx = blockDim.x;
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__shared__ float s_mean;
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float local_sums[1] = {0.0f};
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int offset = m_idx * n;
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input += offset;
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output += offset;
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for (int index = tid ; index < n ; index += bdimx){
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float local_val = static_cast<float>(input[index]);
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local_sums[0] += local_val * local_val;
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}
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if (blockDim.x <= 32) {
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warpReduceSum<float, 1>(local_sums);
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}
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else {
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blockReduceSum<float, 1>(local_sums);
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}
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if (threadIdx.x == 0) {
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s_mean = rsqrtf(local_sums[0] / n + 1e-6);
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}
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__syncthreads();
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for (int index = tid ; index < n ; index += bdimx){
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const T weight_val = weight[index];
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const T local_val = input[index];
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output[index] = T(static_cast<float>(local_val) * s_mean * static_cast<float>(weight_val));
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}
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}
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template <typename T>
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void rmsnorm(cutlass::MatrixCoord tensor_size,
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TensorRef<T, layout::RowMajor> ref_output,
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TensorRef<T, layout::RowMajor> ref_input,
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TensorRef<T, layout::RowMajor> ref_weight,
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cudaStream_t stream){
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const int m = tensor_size.row();
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const int n = tensor_size.column();
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T* output = ref_output.data();
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const T* input = ref_input.data();
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const T* weight = ref_weight.data();
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dim3 grid(m);
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if (n % 8 == 0 && std::is_same<T, cutlass::half_t>::value) {
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dim3 block(min(1024, (n / 8 + 31) / 32 * 32));
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rmsnorm_twoPassAlgo_e8<<<grid, block, 0, stream>>>(
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(float4 *)output, (const float4 *)input, (const float4 *)weight, m, n);
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} else {
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dim3 block(min(1024, ((n + 31)/32 + 31)/32*32));
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rmsnorm_twoPassAlgo_e1<<<grid, block, 0, stream>>>(
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output, input, weight, m, n);
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}
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auto result = cudaGetLastError();
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if (result != cudaSuccess) {
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std::cerr << "CUDA error: " << cudaGetErrorString(result) << std::endl;
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abort();
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}
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}
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} // namespace cutlass
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