Add RMS norm (#979)
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@ -30,4 +30,5 @@ cutlass_test_unit_add_executable(
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cutlass_test_unit_util
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tensor_reduce.cu
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cutlass_test_levels.cu
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rms_norm.cu
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)
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123
test/unit/util/rms_norm.cu
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123
test/unit/util/rms_norm.cu
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@ -0,0 +1,123 @@
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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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#include "../common/cutlass_unit_test.h"
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#include "cutlass/util/device_rmsnorm.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/constants.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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using ElementType = cutlass::half_t;
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using Layout = cutlass::layout::RowMajor;
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void rmsnorm_host(cutlass::MatrixCoord tensor_size,
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cutlass::TensorRef<ElementType, Layout> output,
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cutlass::TensorRef<ElementType, Layout> input,
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cutlass::TensorRef<ElementType, Layout> weight) {
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const int M = tensor_size.row();
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const int N = tensor_size.column();
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for (int m = 0; m < M; ++m) {
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float square_sum{0};
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for (int n = 0; n < N; ++n) {
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float inp = static_cast<float>(input.at({m, n}));
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square_sum += inp * inp;
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}
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float sq_mean = square_sum / (float)N;
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float sqrt_var = cutlass::fast_sqrt(sq_mean + (float)1e-6);
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for (int n = 0; n < N; ++n) {
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float inp = static_cast<float>(input.at({m, n}));
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float g = static_cast<float>(weight.at({0, n}));
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float res_fp32 = inp / sqrt_var * g;
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output.at({m, n}) = ElementType(res_fp32);
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}
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}
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}
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void run_test(int M, int N) {
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cutlass::HostTensor<ElementType, Layout> input, output_ref, output, weight;
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input.reset({M, N});
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output.reset({M, N});
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output_ref.reset({M, N});
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weight.reset({1, N});
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const unsigned seed = 2022;
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cutlass::reference::host::TensorFillRandomUniform(input.host_view(),
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seed,
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ElementType(5),
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ElementType(-5),
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0);
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cutlass::reference::host::TensorFillRandomUniform(weight.host_view(),
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seed,
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ElementType(5),
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ElementType(-5),
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0);
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input.sync_device();
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weight.sync_device();
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rmsnorm_host({M, N}, output_ref.host_ref(), input.host_ref(), weight.host_ref());
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cutlass::rmsnorm({M, N}, output.device_ref(),
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input.device_ref(), weight.device_ref(), NULL);
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output.sync_host();
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float max_abs_diff = -1;
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float mean_abs_diff = 0;
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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auto diff = abs(static_cast<float>(output_ref.at({m, n}) - output.at({m, n})));
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mean_abs_diff += diff;
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max_abs_diff = max(max_abs_diff, diff);
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}
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}
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mean_abs_diff /= float(M * N);
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EXPECT_TRUE(max_abs_diff < 0.001f && mean_abs_diff < 0.001f)
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<< "Max absolute difference : " << max_abs_diff << "\n"
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<< "Mean absolute difference: " << mean_abs_diff;
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}
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TEST(RMSNorm, 16x1024) {
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run_test(16, 1024);
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}
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TEST(RMSNorm, 1x127) {
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run_test(1, 127);
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}
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185
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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@ -0,0 +1,185 @@
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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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