derek.holloway
7502bf64a4
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362 lines
17 KiB
C++
362 lines
17 KiB
C++
/*******************************************************************************
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*
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* MIT License
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*
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* Copyright (c) 2024 Advanced Micro Devices, Inc.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*
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*******************************************************************************/
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#include "../driver/tensor_driver.hpp"
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#include "get_handle.hpp"
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#include "random.hpp"
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#include "tensor_holder.hpp"
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#include "verify.hpp"
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#include <gtest/gtest.h>
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#include <miopen/addlayernorm.hpp>
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#include <miopen/miopen.h>
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template <class T>
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void cpu_addlayernorm_forward(tensor<T> input,
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tensor<T> input2,
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tensor<T> weight,
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tensor<T> bias,
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tensor<T>& ref_output,
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tensor<T>& ref_mean,
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tensor<T>& ref_rstd,
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float eps,
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int32_t dim,
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miopenNormMode_t mode)
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{
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auto dims = input.desc.GetLengths();
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size_t outer_size = 1;
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size_t inner_size = 1;
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size_t i = 0;
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for(; i < dim; i++)
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{
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outer_size *= dims[i];
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}
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for(; i < dims.size(); i++)
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{
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inner_size *= dims[i];
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}
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miopen::par_ford(outer_size)([&](int32_t o) {
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float mean_v = 0;
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float var_v = 0;
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miopen::ford(inner_size)([&](int32_t j) {
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float tmp = static_cast<float>(input[o * inner_size + j]) +
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static_cast<float>(input2[o * inner_size + j]);
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mean_v += tmp;
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var_v += tmp * tmp;
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});
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mean_v = mean_v / inner_size;
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var_v = var_v / inner_size - mean_v * mean_v;
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float rstd_v = 1 / sqrt(var_v + eps);
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ref_mean[o] = static_cast<T>(mean_v);
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ref_rstd[o] = static_cast<T>(rstd_v);
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miopen::ford(inner_size)([&](int32_t j) {
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float weight_v =
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(mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD) ? 1 : static_cast<float>(weight[j]);
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float bias_v =
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(mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD) ? 0 : static_cast<float>(bias[j]);
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ref_output[o * inner_size + j] =
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static_cast<T>((static_cast<float>(input[o * inner_size + j]) +
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static_cast<float>(input2[o * inner_size + j]) - mean_v) *
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rstd_v * weight_v +
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bias_v);
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});
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});
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}
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struct AddLayerNormTestCase
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{
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size_t N;
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size_t C;
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size_t D;
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size_t H;
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size_t W;
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size_t nomalized_dim;
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float eps;
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miopenNormMode_t ln_mode;
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friend std::ostream& operator<<(std::ostream& os, const AddLayerNormTestCase& tc)
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{
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return os << " N:" << tc.N << " C:" << tc.C << " D:" << tc.D << " H:" << tc.H
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<< " W:" << tc.W << " dim:" << tc.nomalized_dim << " eps:" << tc.eps
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<< " LayerNorm_mode:" << tc.ln_mode;
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}
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std::vector<size_t> GetInput()
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{
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if((N != 0) && (C != 0) && (D != 0) && (H != 0) && (W != 0))
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{
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return std::vector<size_t>({N, C, D, H, W});
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}
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else if((N != 0) && (C != 0) && (H != 0) && (W != 0))
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{
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return std::vector<size_t>({N, C, H, W});
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}
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else if((N != 0) && (C != 0) && (W != 0))
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{
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return std::vector<size_t>({N, C, W});
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}
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else if((N != 0) && (W != 0))
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{
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return std::vector<size_t>({N, W});
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}
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else
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{
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std::cout << "Error Input Tensor Lengths\n" << std::endl;
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return std::vector<size_t>({0});
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}
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}
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};
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std::vector<AddLayerNormTestCase> AddLayerNormTestConfigs()
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{ // n c d h w nomalized_dim eps ln_mode
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// clang-format off
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return {
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{ 32, 1, 32, 32, 32 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 32x32x32 based on VoxNet arch
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{ 32, 1, 14, 14, 14 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 14, 14, 14 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 12, 12, 12 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 6, 6, 6 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 256, 1, 32, 32, 32 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 32x32x32 based on VoxNet arch
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{ 256, 32, 14, 14, 14 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 256, 32, 12, 12, 12 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 256, 32, 6, 6, 6 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 512, 1, 32, 32, 32 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 32x32x32 based on VoxNet arch
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{ 512, 32, 14, 14, 14 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 512, 32, 12, 12, 12 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 512, 32, 6, 6, 6 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 2, 32, 57, 125 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // Hand-gesture recognition CVPR 2015 paper High Res Net Path
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{ 32, 32, 14, 25, 59 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 6, 10, 27 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 4, 6, 11 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 2, 2, 3 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 32, 28, 62 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // Hand-gesture recognition CVPR 2015 paper Low Res Net Path
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{ 32, 32, 14, 12, 29 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 6, 4, 12 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 32, 32, 4, 2, 2 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{ 16, 32, 6, 50, 50 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // Multi-view 3D convnet
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{ 1, 3, 8, 240, 320 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 16, 240, 320 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 8, 128, 171 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 16, 128, 171 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 8, 112, 112 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 16, 112, 112 , 4, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD}, // 3D convet on video
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{ 32, 1, 32, 32, 32 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 32x32x32 based on VoxNet arch
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{ 32, 1, 14, 14, 14 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 14, 14, 14 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 12, 12, 12 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 6, 6, 6 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 256, 1, 32, 32, 32 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 32x32x32 based on VoxNet arch
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{ 256, 32, 14, 14, 14 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 256, 32, 12, 12, 12 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 256, 32, 6, 6, 6 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 512, 1, 32, 32, 32 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 32x32x32 based on VoxNet arch
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{ 512, 32, 14, 14, 14 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 512, 32, 12, 12, 12 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 512, 32, 6, 6, 6 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 2, 32, 57, 125 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // Hand-gesture recognition CVPR 2015 paper High Res Net Path
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{ 32, 32, 14, 25, 59 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 6, 10, 27 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 4, 6, 11 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 2, 2, 3 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 32, 28, 62 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // Hand-gesture recognition CVPR 2015 paper Low Res Net Path
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{ 32, 32, 14, 12, 29 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 6, 4, 12 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 32, 32, 4, 2, 2 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{ 16, 32, 6, 50, 50 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // Multi-view 3D convnet
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{ 1, 3, 8, 240, 320 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 16, 240, 320 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 8, 128, 171 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 16, 128, 171 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 8, 112, 112 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 3D convet on video
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{ 1, 3, 16, 112, 112 , 4, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}, // 3D convet on video
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{32, 4, 0, 4, 256 , 1, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{64, 4, 0, 4, 256 , 1, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{32, 4, 0, 4, 256 , 1, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{64, 4, 0, 4, 256 , 1, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{32, 0, 0, 0, 256 , 1, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{64, 0, 0, 0, 256 , 1, 1e-5, MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD},
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{32, 0, 0, 0, 256 , 1, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD},
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{64, 0, 0, 0, 256 , 1, 1e-5, MIOPEN_WEIGHT_BIAS_FUSED_ADD}
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};
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// clang-format on
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}
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template <typename T = float>
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struct AddLayerNormTest : public ::testing::TestWithParam<AddLayerNormTestCase>
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{
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protected:
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void SetUp() override
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{
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auto&& handle = get_handle();
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addlayernorm_config = GetParam();
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auto gen_value = [](auto...) { return prng::gen_descreet_uniform_sign<T>(1e-2, 100); };
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nomalized_dim = addlayernorm_config.nomalized_dim;
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eps = addlayernorm_config.eps;
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ln_mode = addlayernorm_config.ln_mode;
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auto in_dim = addlayernorm_config.GetInput();
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x = tensor<T>{in_dim}.generate(gen_value);
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x2 = tensor<T>{in_dim}.generate(gen_value);
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std::vector<size_t> inner_dim;
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if(nomalized_dim == in_dim.size())
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inner_dim = {1};
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else
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inner_dim = {in_dim.begin() + nomalized_dim, in_dim.end()};
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if(ln_mode == MIOPEN_ELEMENTWISE_AFFINE_FUSED_ADD)
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{
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auto gen_one = [&](auto...) { return 1; };
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auto gen_zero = [&](auto...) { return 0; };
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weight = tensor<T>{inner_dim}.generate(gen_one);
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bias = tensor<T>{inner_dim}.generate(gen_zero);
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}
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else
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{
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weight = tensor<T>{inner_dim}.generate(gen_value);
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bias = tensor<T>{inner_dim}.generate(gen_value);
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}
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std::vector<size_t> outer_dim;
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if(nomalized_dim == 0)
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outer_dim = {1};
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else
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outer_dim = {in_dim.begin(), in_dim.end() - (in_dim.size() - nomalized_dim)};
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y = tensor<T>{in_dim};
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mean = tensor<T>{outer_dim};
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rstd = tensor<T>{outer_dim};
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std::fill(y.begin(), y.end(), std::numeric_limits<T>::quiet_NaN());
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std::fill(mean.begin(), mean.end(), std::numeric_limits<T>::quiet_NaN());
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std::fill(rstd.begin(), rstd.end(), std::numeric_limits<T>::quiet_NaN());
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ref_y = tensor<T>{in_dim};
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ref_mean = tensor<T>{outer_dim};
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ref_rstd = tensor<T>{outer_dim};
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std::fill(ref_y.begin(), ref_y.end(), std::numeric_limits<T>::quiet_NaN());
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std::fill(ref_mean.begin(), ref_mean.end(), std::numeric_limits<T>::quiet_NaN());
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std::fill(ref_rstd.begin(), ref_rstd.end(), std::numeric_limits<T>::quiet_NaN());
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x_dev = handle.Write(x.data);
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x2_dev = handle.Write(x2.data);
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weight_dev = handle.Write(weight.data);
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bias_dev = handle.Write(bias.data);
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y_dev = handle.Write(y.data);
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mean_dev = handle.Write(mean.data);
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rstd_dev = handle.Write(rstd.data);
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}
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void RunTest()
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{
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auto&& handle = get_handle();
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cpu_addlayernorm_forward<T>(
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x, x2, weight, bias, ref_y, ref_mean, ref_rstd, eps, nomalized_dim, ln_mode);
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miopenStatus_t status;
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status = miopen::AddLayerNormForward(handle,
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x.desc,
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x_dev.get(),
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x2.desc,
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x2_dev.get(),
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weight.desc,
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weight_dev.get(),
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bias.desc,
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bias_dev.get(),
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y.desc,
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y_dev.get(),
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mean.desc,
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mean_dev.get(),
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rstd.desc,
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rstd_dev.get(),
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ln_mode,
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eps,
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nomalized_dim);
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EXPECT_EQ(status, miopenStatusSuccess);
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y.data = handle.Read<T>(y_dev, y.data.size());
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mean.data = handle.Read<T>(mean_dev, mean.data.size());
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rstd.data = handle.Read<T>(rstd_dev, rstd.data.size());
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}
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void Verify()
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{
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// Computation error of fp16 is ~2^13 (=8192) bigger than
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// the one of fp32 because mantissa is shorter by 13 bits.
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auto threshold = std::is_same<T, float>::value ? 1.5e-5 : 8.2e-2;
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// bf16 mantissa has 7 bits, by 3 bits shorter than fp16.
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if(std::is_same<T, bfloat16>::value)
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threshold *= 8.0;
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auto error = miopen::rms_range(ref_y, y);
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EXPECT_TRUE(miopen::range_distance(ref_y) == miopen::range_distance(y));
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EXPECT_TRUE(error < threshold * 4)
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<< "Error y beyond tolerance Error:" << error << ", Threshold x 4: " << threshold * 4;
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|
|
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error = miopen::rms_range(ref_mean, mean);
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EXPECT_TRUE(miopen::range_distance(ref_mean) == miopen::range_distance(mean));
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EXPECT_TRUE(error < threshold)
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|
<< "Error mean beyond tolerance Error:" << error << ", Threshold: " << threshold;
|
|
|
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error = miopen::rms_range(ref_rstd, rstd);
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|
EXPECT_TRUE(miopen::range_distance(ref_rstd) == miopen::range_distance(rstd));
|
|
EXPECT_TRUE(error < threshold * 16) << "Error rstd beyond tolerance Error:" << error
|
|
<< ", Threshold x 16: " << threshold * 16;
|
|
}
|
|
AddLayerNormTestCase addlayernorm_config;
|
|
|
|
tensor<T> x;
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tensor<T> x2;
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|
tensor<T> weight;
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tensor<T> bias;
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|
tensor<T> y;
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|
tensor<T> mean;
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|
tensor<T> rstd;
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|
|
|
tensor<T> ref_y;
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|
tensor<T> ref_mean;
|
|
tensor<T> ref_rstd;
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|
|
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miopen::Allocator::ManageDataPtr x_dev;
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|
miopen::Allocator::ManageDataPtr x2_dev;
|
|
miopen::Allocator::ManageDataPtr weight_dev;
|
|
miopen::Allocator::ManageDataPtr bias_dev;
|
|
miopen::Allocator::ManageDataPtr y_dev;
|
|
miopen::Allocator::ManageDataPtr mean_dev;
|
|
miopen::Allocator::ManageDataPtr rstd_dev;
|
|
|
|
size_t nomalized_dim;
|
|
float eps;
|
|
miopenNormMode_t ln_mode;
|
|
};
|